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"!pip install -q --upgrade torch==2.5.1+cu124 torchvision==0.20.1+cu124 torchaudio==2.5.1+cu124 --index-url https://download.pytorch.org/whl/cu124\n", - "!pip install -q --upgrade requests==2.32.3 bitsandbytes==0.46.0 transformers==4.48.3 accelerate==1.3.0 datasets==3.2.0 peft==0.14.0 trl==0.14.0 matplotlib wandb" - ], - "metadata": { - "id": "MDyR63OTNUJ6", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "175badd5-5f72-42b8-812e-f8f782235e81" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m908.2/908.2 MB\u001b[0m \u001b[31m205.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n", - "\u001b[?25h\u001b[31mERROR: Operation cancelled by user\u001b[0m\u001b[31m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.4/44.4 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - 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"import torch.nn.functional as F\n", - "import transformers\n", - "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, set_seed\n", - "from datasets import load_dataset, Dataset, DatasetDict\n", - "from datetime import datetime\n", - "from peft import PeftModel\n", - "import matplotlib.pyplot as plt" - ], - "metadata": { - "id": "-yikV8pRBer9" - }, - "execution_count": 1, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Constants\n", - "\n", - "BASE_MODEL = \"meta-llama/Meta-Llama-3.1-8B\"\n", - "PROJECT_NAME = \"pricer\"\n", - "# HF_USER = \"dkisselev\" # your HF name here! Or use mine if you just want to reproduce my results.\n", - "HF_USER = \"dkisselev\"\n", - "\n", - "# The run itself\n", - "\n", - "# RUN_NAME = \"2025-10-23_23.41.24\"\n", - "RUN_NAME = \"2024-09-13_13.04.39\"\n", - "PROJECT_RUN_NAME = f\"{PROJECT_NAME}-{RUN_NAME}\"\n", - "# REVISION = None\n", - "REVISION = \"e8d637df551603dc86cd7a1598a8f44af4d7ae36\" # or REVISION = None\n", - "# FINETUNED_MODEL = f\"{HF_USER}/{PROJECT_RUN_NAME}\"\n", - "\n", - "# Uncomment this line if you wish to use my model\n", - "FINETUNED_MODEL = f\"ed-donner/{PROJECT_RUN_NAME}\"\n", - "\n", - "WANDB_ENTITY = \"dkisselev\"\n", - "os.environ[\"WANDB_API_KEY\"]=userdata.get('WANDB_API_KEY')\n", - "MODEL_ARTIFACT_NAME = \"model-2025-10-23_23.41.24\"\n", - "REVISION_TAG=\"v22\"\n", - "WANDB_ARTIFACT_PATH = f\"{WANDB_ENTITY}/{PROJECT_NAME}/{MODEL_ARTIFACT_NAME}:{REVISION_TAG}\"\n", - "\n", - "# Data\n", - "\n", - "# DATASET_NAME = f\"{HF_USER}/pricer-data2\"\n", - "# Or just use the one I've uploaded\n", - "DATASET_NAME = \"ed-donner/pricer-data\"\n", - "\n", - "# Hyperparameters for QLoRA\n", - "\n", - "QUANT_4_BIT = False\n", - "\n", - "%matplotlib inline\n", - "\n", - "# Used for writing to output in color\n", - "\n", - "GREEN = \"\\033[92m\"\n", - "YELLOW = \"\\033[93m\"\n", - "RED = \"\\033[91m\"\n", - "RESET = \"\\033[0m\"\n", - "COLOR_MAP = {\"red\":RED, \"orange\": YELLOW, \"green\": GREEN}" - ], - "metadata": { - "id": "uuTX-xonNeOK" - }, - "execution_count": 2, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Log in to HuggingFace\n", - "\n", - "If you don't already have a HuggingFace account, visit https://huggingface.co to sign up and create a token.\n", - "\n", - "Then select the Secrets for this Notebook by clicking on the key icon in the left, and add a new secret called `HF_TOKEN` with the value as your token.\n" - ], - "metadata": { - "id": "8JArT3QAQAjx" - } - }, - { - "cell_type": "code", - "source": [ - "# Log in to HuggingFace\n", - "\n", - "hf_token = userdata.get('HF_TOKEN')\n", - "login(hf_token)" - ], - "metadata": { - "id": "WyFPZeMcM88v" - }, - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "dataset = load_dataset(DATASET_NAME)\n", - "train = dataset['train']\n", - "test = dataset['test']" - ], - "metadata": { - "id": "cvXVoJH8LS6u" - }, - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "train[0]\n" - ], - "metadata": { - "id": "xb86e__Wc7j_", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "41e0386c-4a3c-4e1e-c01c-e72dca4cb2cd" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "{'text': 'How much does this cost to the nearest dollar?\\n\\nDelphi FG0166 Fuel Pump Module\\nDelphi brings 80 years of OE Heritage into each Delphi pump, ensuring quality and fitment for each Delphi part. Part is validated, tested and matched to the right vehicle application Delphi brings 80 years of OE Heritage into each Delphi assembly, ensuring quality and fitment for each Delphi part Always be sure to check and clean fuel tank to avoid unnecessary returns Rigorous OE-testing ensures the pump can withstand extreme temperatures Brand Delphi, Fit Type Vehicle Specific Fit, Dimensions LxWxH 19.7 x 7.7 x 5.1 inches, Weight 2.2 Pounds, Auto Part Position Unknown, Operation Mode Mechanical, Manufacturer Delphi, Model FUEL PUMP, Dimensions 19.7\\n\\nPrice is $227.00',\n", - " 'price': 226.95}" - ] - }, - "metadata": {}, - "execution_count": 7 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "## Now load the Tokenizer and Model" - ], - "metadata": { - "id": "qJWQ0a3wZ0Bw" - } - }, - { - "cell_type": "code", - "source": [ - "# pick the right quantization (thank you Robert M. for spotting the bug with the 8 bit version!)\n", - "\n", - "if QUANT_4_BIT:\n", - " quant_config = BitsAndBytesConfig(\n", - " load_in_4bit=True,\n", - " bnb_4bit_use_double_quant=True,\n", - " bnb_4bit_compute_dtype=torch.bfloat16,\n", - " bnb_4bit_quant_type=\"nf4\"\n", - " )\n", - "else:\n", - " quant_config = BitsAndBytesConfig(\n", - " load_in_8bit=True,\n", - " bnb_8bit_compute_dtype=torch.bfloat16\n", - " )" - ], - "metadata": { - "id": "lAUAAcEC6ido", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "ada25a72-4775-46ee-8c4e-16c053dd6ce3" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "Unused kwargs: ['bnb_8bit_compute_dtype']. These kwargs are not used in .\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Load model from w&b\n", - "artifact = wandb.Api().artifact(WANDB_ARTIFACT_PATH, type='model')\n", - "artifact_dir = artifact.download() # Downloads to a local cache dir" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "OQy4pCk-dutf", - "outputId": "9e915c61-e77b-4e3d-908e-02bb46410fcc" - }, - "execution_count": 6, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact 'model-2025-10-23_23.41.24:v22', 328.81MB. 11 files...\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: 11 of 11 files downloaded. \n", - "Done. 00:00:00.2 (1319.5MB/s)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Load the Tokenizer and the Model\n", - "\n", - "tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)\n", - "tokenizer.pad_token = tokenizer.eos_token\n", - "tokenizer.padding_side = \"right\"\n", - "\n", - "base_model = AutoModelForCausalLM.from_pretrained(\n", - " BASE_MODEL,\n", - " quantization_config=quant_config,\n", - " device_map=\"auto\",\n", - ")\n", - "base_model.generation_config.pad_token_id = tokenizer.pad_token_id\n", - "\n", - "# Load the fine-tuned model with PEFT\n", - "# if REVISION:\n", - "# fine_tuned_model = PeftModel.from_pretrained(base_model, FINETUNED_MODEL, revision=REVISION)\n", - "# else:\n", - "# fine_tuned_model = PeftModel.from_pretrained(base_model, FINETUNED_MODEL)\n", - "\n", - "# Model at W&B\n", - "fine_tuned_model = PeftModel.from_pretrained(base_model, artifact_dir)\n", - "\n", - "print(f\"Memory footprint: {fine_tuned_model.get_memory_footprint() / 1e6:.1f} MB\")" - ], - "metadata": { - "id": "R_O04fKxMMT-", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 67, - "referenced_widgets": [ - "44e708a87187403fa3a2a5eb5fe6f47d", - "50c4ad16d779428084a1099b09eb2550", - "b24f3199787f40f383895d355ca80f84", - "34757092af9041b1a0cdbdd9d37a96cf", - "eea6e3ba7e974dd2b74324c19b8264ed", - "051a67284e5d45e6811091c12c876c6d", - "d56ab4b309e74e7d9822122710c32d1b", - "237369cf692a416e886d1afc845497fa", - "5c2be9cd17fc46b9b4de1761bf40d10b", - "cf69f4a0cdf74ce4bad7951fe141955b", - "e0def1b6f7e6492cb2b780628670381b" - ] - }, - "outputId": "74c49d02-1df8-46ed-a442-7512508ea19c" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Loading checkpoint shards: 0%| | 0/4 [00:00 0:\n", - " prices.append(price)\n", - " # We append the tensor to our list\n", - " probabilities.append(probability_tensor)\n", - "\n", - " if not prices:\n", - " # If no valid prices were found, return 0.0\n", - " return 0.0\n", - "\n", - " # --- MODIFIED SECTION ---\n", - " # Use the numpy weighted average technique\n", - "\n", - " # 1. Convert the list of prices to a numpy array\n", - " prices_np = np.array(prices)\n", - "\n", - " # 2. Convert the list of torch.Tensors to a numpy array of floats\n", - " probs_np = np.array([p.item() for p in probabilities])\n", - "\n", - " # 3. Calculate the normalized weighted average\n", - " # This is equivalent to: sum(prices_np * probs_np) / sum(probs_np)\n", - " final_price = np.average(prices_np, weights=probs_np)\n", - "\n", - " return float(final_price) # Return as a standard python float" - ], - "metadata": { - "id": "tuwYu1NYljIv" - }, - "execution_count": 51, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# An improved prediction function takes a weighted average of the top 3 choices\n", - "# This code would be more complex if we couldn't take advantage of the fact\n", - "# That Llama generates 1 token for any 3 digit number\n", - "\n", - "# top_K = 3\n", - "\n", - "# def improved_model_predict(prompt, device=\"cuda\"):\n", - "# set_seed(42)\n", - "# inputs = tokenizer.encode(prompt, return_tensors=\"pt\").to(device)\n", - "# attention_mask = torch.ones(inputs.shape, device=device)\n", - "\n", - "# with torch.no_grad():\n", - "# outputs = fine_tuned_model(inputs, attention_mask=attention_mask)\n", - "# next_token_logits = outputs.logits[:, -1, :].to('cpu')\n", - "\n", - "# next_token_probs = F.softmax(next_token_logits, dim=-1)\n", - "# top_prob, top_token_id = next_token_probs.topk(top_K)\n", - "# prices, weights = [], []\n", - "# for i in range(top_K):\n", - "# predicted_token = tokenizer.decode(top_token_id[0][i])\n", - "# print(predicted_token, top_prob[0][i])\n", - "# probability = top_prob[0][i]\n", - "# try:\n", - "# result = float(predicted_token)\n", - "# except ValueError as e:\n", - "# result = 0.0\n", - "# if result > 0:\n", - "# prices.append(result)\n", - "# weights.append(probability)\n", - "# if not prices:\n", - "# return 0.0, 0.0\n", - "# total = sum(weights)\n", - "# weighted_prices = [price * weight / total for price, weight in zip(prices, weights)]\n", - "# return sum(weighted_prices).item()" - ], - "metadata": { - "id": "Je5dR8QEAI1d" - }, - "execution_count": 26, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# # An improved prediction function takes the median of the top 3 choices\n", - "# # This code would be more complex if we couldn't take advantage of the fact\n", - "# # That Llama generates 1 token for any 3 digit number\n", - "\n", - "# top_K = 3\n", - "\n", - "# def improved_model_predict(prompt, device=\"cuda\"):\n", - "# set_seed(42)\n", - "# inputs = tokenizer.encode(prompt, return_tensors=\"pt\").to(device)\n", - "# attention_mask = torch.ones(inputs.shape, device=device)\n", - "\n", - "# with torch.no_grad():\n", - "# outputs = fine_tuned_model(inputs, attention_mask=attention_mask)\n", - "# next_token_logits = outputs.logits[:, -1, :].to('cpu')\n", - "\n", - "# next_token_probs = F.softmax(next_token_logits, dim=-1)\n", - "# top_prob, top_token_id = next_token_probs.topk(top_K)\n", - "# prices = []\n", - "# for i in range(top_K):\n", - "\n", - "# predicted_token = tokenizer.decode(top_token_id[0][i])\n", - "# # print(predicted_token, top_prob[0][i])\n", - "\n", - "# probability = top_prob[0][i]\n", - "# try:\n", - "# result = float(predicted_token)\n", - "# except ValueError as e:\n", - "# result = 0.0\n", - "# if result > 0:\n", - "# prices.append(result)\n", - "\n", - "# if not prices:\n", - "# return 0.0\n", - "\n", - "# # Calculate the median\n", - "# prices.sort()\n", - "# mid = len(prices) // 2\n", - "# median_price = (prices[mid] + prices[~mid]) / 2 if len(prices) % 2 == 0 else prices[mid]\n", - "\n", - "# return median_price" - ], - "metadata": { - "id": "lQk7jNlm1oV9" - }, - "execution_count": 25, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "test[80]" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EVsACuru4zJE", - "outputId": "41dc617a-d15f-42e4-a52a-f97ab838ec8b" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "{'text': \"How much does this cost to the nearest dollar?\\n\\nLongacre Aluminum Turn Plates\\nLongacre is an established brand name in the racing industry and is recognized for dedication to quality, innovation and customer satisfaction. Check out our comprehensive line of race scales, alignment tools, racing gauges and other products. Whether you are into stock, modified, drag, go kart, off-road, sprint or RC car racing, we'll provide you with the quality racing parts you deserve. The free floating in 2 directions eliminates bind It reads to 1/2° - Degrees can be zeroed with the car on The low profile design means that its only 1 tall Can also be used on top of scale pads Has a weight capacity of 1,500 lbs. per scale Manufacturer Longacre, Brand Longacre, Model Longacre Racing Products, Weight 31\\n\\nPrice is $\",\n", - " 'price': 940.33}" - ] - }, - "metadata": {}, - "execution_count": 12 - } - ] - }, - { - "cell_type": "code", - "source": [ - "def make_prompt(text):\n", - " p_array = text.split(\"\\n\")\n", - " p_question = p_array[0].replace(\"How much does this cost to the nearest dollar?\",\"What is the price of this item?\")\n", - " p_title = p_array[2]\n", - " p_descr = re.sub(r'\\d', '', p_array[3])\n", - " p_price = p_array[5]\n", - " prompt = p_title + \"\\n\" + p_descr + \"\\n\" + \"Question: \"+ p_question + \"\\n\\n\" + p_price\n", - " # prompt = p_array[0] + \"\\n\\n\\n\" + p_title + \"\\n\\n\" + p_descr + \"\\n\\n\" + p_price\n", - " # return text\n", - " return prompt" - ], - "metadata": { - "id": "qJgJuVJnMIUF" - }, - "execution_count": 12, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "prompt=make_prompt(test[80]['text'])\n", - "print(prompt)\n", - "\n", - "improved_model_predict(prompt)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3SxpLBJH70E-", - "outputId": "9ea1daae-d542-4766-da8f-5b9cc1651a32" - }, - "execution_count": 14, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Longacre Aluminum Turn Plates\n", - "Longacre is an established brand name in the racing industry and is recognized for dedication to quality, innovation and customer satisfaction. Check out our comprehensive line of race scales, alignment tools, racing gauges and other products. Whether you are into stock, modified, drag, go kart, off-road, sprint or RC car racing, we'll provide you with the quality racing parts you deserve. The free floating in directions eliminates bind It reads to /° - Degrees can be zeroed with the car on The low profile design means that its only tall Can also be used on top of scale pads Has a weight capacity of , lbs. per scale Manufacturer Longacre, Brand Longacre, Model Longacre Racing Products, Weight \n", - "Question: What is the price of this item?\n", - "\n", - "Price is $\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "138.63358778625954" - ] - }, - "metadata": {}, - "execution_count": 14 - } - ] - }, - { - "cell_type": "code", - "source": [ - "FEW_SHOT_EXAMPLES = \"\"\"\n", - "BATHLAVISH Utility Sink Faucet Wall Mount Commercial Kitchen 12 Inch Length Swivel Spout 2 Handle Bar Laundry Polished Chrome Mixer Tap\\nHeavy Duty Brass Constructed wall mount kitchen faucet, 12” swivel spout, Chrome polished, engineered design and built for performance & dependability. Sturdy heavy duty brass construction for extra durability and longevity. Reinforced double O-Ring valves to avoid leakage, with hot and cold mark on stem. Color coded red and blue handles with heavy duty double O-ring swivel spout. Wall Mount Installation, 1/2 NPT female inlets, ideal for use in kitchen, commercial, laundry, restaurant, farm etc. Brand BATHLAVISH, Mounting Type Wall Mount, Finish Type Chrome, Material Brass,\n", - "\n", - "Price is $65.99\n", - "\n", - "Coverking Custom Fit Front 50/50 Bucket Seat Cover for Select Chevrolet Silverado HD Models - Neosupreme (Charcoal with Black Sides)\\nThe exact seat configuration is Front 50/50 Bucket; Without Armrest; Without Built-In Shoulder Belt Made from Neosupreme fabric for insulation, soft touch, and comfort Neosupreme seat covers are water-resistant and are an affordable alternative to Neoprene Tailor-made to the exact specifications of your vehicles seats and protects your seats from spills, stains, and damage Stitching designed to emulate factory seat style and the high quality buckles and zippers enable for a secure fit Designed to install yourself (installation may require some effort for a snug fit) and includes a 1 year limited warranty against defects Manufacturer Coverking, Brand\n", - "\n", - "Price is $202.81\n", - "\"\"\"\n", - "\n", - "class Tester:\n", - "\n", - " def __init__(self, predictor, data, title=None, size=250):\n", - " self.predictor = predictor\n", - " self.data = data\n", - " self.title = title or predictor.__name__.replace(\"_\", \" \").title()\n", - " self.size = size\n", - " self.guesses = []\n", - " self.truths = []\n", - " self.errors = []\n", - " self.sles = []\n", - " self.colors = []\n", - "\n", - " def color_for(self, error, truth):\n", - " if error<40 or error/truth < 0.2:\n", - " return \"green\"\n", - " elif error<80 or error/truth < 0.4:\n", - " return \"orange\"\n", - " else:\n", - " return \"red\"\n", - "\n", - " def run_datapoint(self, i):\n", - " datapoint = self.data[i]\n", - "\n", - " base_prompt = datapoint[\"text\"]\n", - " prompt = make_prompt(base_prompt)\n", - "\n", - " guess = self.predictor(prompt)\n", - "\n", - " # guess = self.predictor(datapoint[\"text\"])\n", - " truth = datapoint[\"price\"]\n", - " error = abs(guess - truth)\n", - " log_error = math.log(truth+1) - math.log(guess+1)\n", - " sle = log_error ** 2\n", - " color = self.color_for(error, truth)\n", - " title = datapoint[\"text\"].split(\"\\n\\n\")[1][:20] + \"...\"\n", - " self.guesses.append(guess)\n", - " self.truths.append(truth)\n", - " self.errors.append(error)\n", - " self.sles.append(sle)\n", - " self.colors.append(color)\n", - " print(f\"{COLOR_MAP[color]}{i+1}: Guess: ${guess:,.2f} Truth: ${truth:,.2f} Error: ${error:,.2f} SLE: {sle:,.2f} Item: {title}{RESET}\")\n", - "\n", - " def chart(self, title):\n", - " max_error = max(self.errors)\n", - " plt.figure(figsize=(12, 8))\n", - " max_val = max(max(self.truths), max(self.guesses))\n", - " plt.plot([0, max_val], [0, max_val], color='deepskyblue', lw=2, alpha=0.6)\n", - " plt.scatter(self.truths, self.guesses, s=3, c=self.colors)\n", - " plt.xlabel('Ground Truth')\n", - " plt.ylabel('Model Estimate')\n", - " plt.xlim(0, max_val)\n", - " plt.ylim(0, max_val)\n", - " plt.title(title)\n", - " plt.show()\n", - "\n", - " def report(self):\n", - " average_error = sum(self.errors) / self.size\n", - " rmsle = math.sqrt(sum(self.sles) / self.size)\n", - " hits = sum(1 for color in self.colors if color==\"green\")\n", - " title = f\"{self.title} Error=${average_error:,.2f} RMSLE={rmsle:,.2f} Hits={hits/self.size*100:.1f}%\"\n", - " self.chart(title)\n", - "\n", - " def run(self):\n", - " self.error = 0\n", - " for i in range(self.size):\n", - " self.run_datapoint(i)\n", - " self.report()\n", - "\n", - " @classmethod\n", - " def test(cls, function, data):\n", - " cls(function, data).run()" - ], - "metadata": { - "id": "30lzJXBH7BcK" - }, - "execution_count": 13, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "tester = Tester(improved_model_predict, test, title=f\"{MODEL_ARTIFACT_NAME}:{REVISION_TAG}\")\n", - "tester.run()" - ], - "metadata": { - "id": "W_KcLvyt6kbb", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "4c52fa1e-5ea4-4cfc-9631-750fc7c6e992" - }, - "execution_count": 52, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", - "240 tensor(0.0204, dtype=torch.float16)\n", - "250 tensor(0.0186, dtype=torch.float16)\n", - "300 tensor(0.0175, dtype=torch.float16)\n", - "270 tensor(0.0150, dtype=torch.float16)\n", - "280 tensor(0.0128, dtype=torch.float16)\n", - "260 tensor(0.0124, dtype=torch.float16)\n", - "210 tensor(0.0106, dtype=torch.float16)\n", - "299 tensor(0.0103, dtype=torch.float16)\n", - "290 tensor(0.0103, dtype=torch.float16)\n", - "220 tensor(0.0100, dtype=torch.float16)\n", - "350 tensor(0.0097, dtype=torch.float16)\n", - "230 tensor(0.0094, dtype=torch.float16)\n", - "400 tensor(0.0094, dtype=torch.float16)\n", - "249 tensor(0.0091, dtype=torch.float16)\n", - "190 tensor(0.0088, dtype=torch.float16)\n", - "199 tensor(0.0085, dtype=torch.float16)\n", - "330 tensor(0.0083, dtype=torch.float16)\n", - "180 tensor(0.0083, dtype=torch.float16)\n", - "320 tensor(0.0080, dtype=torch.float16)\n", - "200 tensor(0.0078, dtype=torch.float16)\n", - "\u001b[93m13: Guess: $263.94 Truth: $205.50 Error: $58.44 SLE: 0.06 Item: Solar HAMMERED BRONZ...\u001b[0m\n", - "300 tensor(0.0634, dtype=torch.float16)\n", - "250 tensor(0.0509, dtype=torch.float16)\n", - "260 tensor(0.0449, dtype=torch.float16)\n", - "240 tensor(0.0449, dtype=torch.float16)\n", - "280 tensor(0.0422, dtype=torch.float16)\n", - "270 tensor(0.0396, dtype=torch.float16)\n", - "290 tensor(0.0264, dtype=torch.float16)\n", - "230 tensor(0.0256, dtype=torch.float16)\n", - "330 tensor(0.0256, dtype=torch.float16)\n", - "220 tensor(0.0248, dtype=torch.float16)\n", - "350 tensor(0.0212, dtype=torch.float16)\n", - "400 tensor(0.0212, dtype=torch.float16)\n", - "320 tensor(0.0206, dtype=torch.float16)\n", - "200 tensor(0.0182, dtype=torch.float16)\n", - "190 tensor(0.0171, dtype=torch.float16)\n", - "210 tensor(0.0171, dtype=torch.float16)\n", - "180 tensor(0.0155, dtype=torch.float16)\n", - "170 tensor(0.0137, dtype=torch.float16)\n", - "310 tensor(0.0137, dtype=torch.float16)\n", - "340 tensor(0.0129, dtype=torch.float16)\n", - "\u001b[92m14: Guess: $269.67 Truth: $248.23 Error: $21.44 SLE: 0.01 Item: COSTWAY Electric Tum...\u001b[0m\n", - "300 tensor(0.0442, dtype=torch.float16)\n", - "400 tensor(0.0390, dtype=torch.float16)\n", - "350 tensor(0.0276, dtype=torch.float16)\n", - "250 tensor(0.0252, dtype=torch.float16)\n", - "500 tensor(0.0209, dtype=torch.float16)\n", - "299 tensor(0.0184, dtype=torch.float16)\n", - "399 tensor(0.0168, dtype=torch.float16)\n", - "450 tensor(0.0139, dtype=torch.float16)\n", - "280 tensor(0.0135, dtype=torch.float16)\n", - "600 tensor(0.0126, dtype=torch.float16)\n", - "270 tensor(0.0119, dtype=torch.float16)\n", - "330 tensor(0.0119, dtype=torch.float16)\n", - "349 tensor(0.0119, dtype=torch.float16)\n", - "240 tensor(0.0108, dtype=torch.float16)\n", - "499 tensor(0.0108, dtype=torch.float16)\n", - "290 tensor(0.0105, dtype=torch.float16)\n", - "260 tensor(0.0105, dtype=torch.float16)\n", - "249 tensor(0.0098, dtype=torch.float16)\n", - "320 tensor(0.0093, dtype=torch.float16)\n", - "340 tensor(0.0084, dtype=torch.float16)\n", - "\u001b[92m15: Guess: $350.40 Truth: $399.00 Error: $48.60 SLE: 0.02 Item: FREE SIGNAL TV Trans...\u001b[0m\n", - "338 tensor(0.0162, dtype=torch.float16)\n", - "339 tensor(0.0157, dtype=torch.float16)\n", - "343 tensor(0.0157, dtype=torch.float16)\n", - "349 tensor(0.0153, dtype=torch.float16)\n", - "344 tensor(0.0148, dtype=torch.float16)\n", - "354 tensor(0.0139, dtype=torch.float16)\n", - "346 tensor(0.0130, dtype=torch.float16)\n", - "350 tensor(0.0130, dtype=torch.float16)\n", - "337 tensor(0.0123, dtype=torch.float16)\n", - "376 tensor(0.0119, dtype=torch.float16)\n", - "341 tensor(0.0119, dtype=torch.float16)\n", - "331 tensor(0.0112, dtype=torch.float16)\n", - "322 tensor(0.0108, dtype=torch.float16)\n", - "352 tensor(0.0105, dtype=torch.float16)\n", - "373 tensor(0.0105, dtype=torch.float16)\n", - "336 tensor(0.0105, dtype=torch.float16)\n", - "329 tensor(0.0102, dtype=torch.float16)\n", - "340 tensor(0.0102, dtype=torch.float16)\n", - "372 tensor(0.0098, dtype=torch.float16)\n", - "353 tensor(0.0098, dtype=torch.float16)\n", - "\u001b[92m16: Guess: $345.94 Truth: $373.94 Error: $28.00 SLE: 0.01 Item: Bilstein 5100 Monotu...\u001b[0m\n", - "98 tensor(0.0124, dtype=torch.float16)\n", - "95 tensor(0.0124, dtype=torch.float16)\n", - "87 tensor(0.0121, dtype=torch.float16)\n", - "94 tensor(0.0121, dtype=torch.float16)\n", - "84 tensor(0.0117, dtype=torch.float16)\n", - "92 tensor(0.0117, dtype=torch.float16)\n", - "85 tensor(0.0117, dtype=torch.float16)\n", - "93 tensor(0.0113, dtype=torch.float16)\n", - "72 tensor(0.0113, dtype=torch.float16)\n", - "105 tensor(0.0113, dtype=torch.float16)\n", - "97 tensor(0.0113, dtype=torch.float16)\n", - "73 tensor(0.0110, dtype=torch.float16)\n", - "81 tensor(0.0110, dtype=torch.float16)\n", - "75 tensor(0.0110, dtype=torch.float16)\n", - "91 tensor(0.0110, dtype=torch.float16)\n", - 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"118 tensor(0.0091, dtype=torch.float16)\n", - "107 tensor(0.0091, dtype=torch.float16)\n", - "105 tensor(0.0089, dtype=torch.float16)\n", - "128 tensor(0.0089, dtype=torch.float16)\n", - "123 tensor(0.0089, dtype=torch.float16)\n", - "132 tensor(0.0089, dtype=torch.float16)\n", - "\u001b[93m18: Guess: $119.20 Truth: $51.99 Error: $67.21 SLE: 0.67 Item: Charles Leonard Magn...\u001b[0m\n", - "199 tensor(0.0550, dtype=torch.float16)\n", - "299 tensor(0.0516, dtype=torch.float16)\n", - "249 tensor(0.0355, dtype=torch.float16)\n", - "149 tensor(0.0244, dtype=torch.float16)\n", - "399 tensor(0.0236, dtype=torch.float16)\n", - "179 tensor(0.0215, dtype=torch.float16)\n", - "189 tensor(0.0215, dtype=torch.float16)\n", - "250 tensor(0.0196, dtype=torch.float16)\n", - "300 tensor(0.0173, dtype=torch.float16)\n", - "169 tensor(0.0157, dtype=torch.float16)\n", - "229 tensor(0.0157, dtype=torch.float16)\n", - "219 tensor(0.0139, dtype=torch.float16)\n", - "499 tensor(0.0135, dtype=torch.float16)\n", - 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"161 tensor(0.0093, dtype=torch.float16)\n", - "153 tensor(0.0090, dtype=torch.float16)\n", - "147 tensor(0.0087, dtype=torch.float16)\n", - "174 tensor(0.0087, dtype=torch.float16)\n", - "154 tensor(0.0085, dtype=torch.float16)\n", - "192 tensor(0.0085, dtype=torch.float16)\n", - "193 tensor(0.0085, dtype=torch.float16)\n", - "181 tensor(0.0082, dtype=torch.float16)\n", - "164 tensor(0.0079, dtype=torch.float16)\n", - "142 tensor(0.0079, dtype=torch.float16)\n", - "\u001b[93m29: Guess: $161.77 Truth: $231.62 Error: $69.85 SLE: 0.13 Item: ICON 01725 Tandem Ax...\u001b[0m\n", - "100 tensor(0.0126, dtype=torch.float16)\n", - "95 tensor(0.0118, dtype=torch.float16)\n", - "72 tensor(0.0115, dtype=torch.float16)\n", - "75 tensor(0.0115, dtype=torch.float16)\n", - "65 tensor(0.0115, dtype=torch.float16)\n", - "98 tensor(0.0111, dtype=torch.float16)\n", - "85 tensor(0.0111, dtype=torch.float16)\n", - "80 tensor(0.0104, dtype=torch.float16)\n", - "94 tensor(0.0104, dtype=torch.float16)\n", - "64 tensor(0.0104, dtype=torch.float16)\n", - "90 tensor(0.0104, dtype=torch.float16)\n", - "73 tensor(0.0104, dtype=torch.float16)\n", - "87 tensor(0.0104, dtype=torch.float16)\n", - "74 tensor(0.0101, dtype=torch.float16)\n", - "70 tensor(0.0101, dtype=torch.float16)\n", - "68 tensor(0.0101, dtype=torch.float16)\n", - "105 tensor(0.0101, dtype=torch.float16)\n", - "84 tensor(0.0101, dtype=torch.float16)\n", - "78 tensor(0.0101, dtype=torch.float16)\n", - "92 tensor(0.0098, dtype=torch.float16)\n", - "\u001b[93m30: Guess: $82.59 Truth: $135.00 Error: $52.41 SLE: 0.24 Item: SanDisk 128GB Ultra ...\u001b[0m\n", - "250 tensor(0.0056, dtype=torch.float16)\n", - "193 tensor(0.0056, dtype=torch.float16)\n", - "240 tensor(0.0056, dtype=torch.float16)\n", - "216 tensor(0.0052, dtype=torch.float16)\n", - "215 tensor(0.0052, dtype=torch.float16)\n", - "255 tensor(0.0051, dtype=torch.float16)\n", - "204 tensor(0.0051, dtype=torch.float16)\n", - "239 tensor(0.0051, dtype=torch.float16)\n", - "208 tensor(0.0049, dtype=torch.float16)\n", - "196 tensor(0.0049, dtype=torch.float16)\n", - "209 tensor(0.0049, dtype=torch.float16)\n", - "198 tensor(0.0049, dtype=torch.float16)\n", - "236 tensor(0.0049, dtype=torch.float16)\n", - "186 tensor(0.0049, dtype=torch.float16)\n", - "205 tensor(0.0048, dtype=torch.float16)\n", - "217 tensor(0.0048, dtype=torch.float16)\n", - "192 tensor(0.0048, dtype=torch.float16)\n", - "197 tensor(0.0048, dtype=torch.float16)\n", - "238 tensor(0.0048, dtype=torch.float16)\n", - "218 tensor(0.0048, dtype=torch.float16)\n", - "\u001b[93m31: Guess: $215.95 Truth: $356.62 Error: $140.67 SLE: 0.25 Item: Velvac - 715427\n", - "2020...\u001b[0m\n", - "250 tensor(0.0536, dtype=torch.float16)\n", - "300 tensor(0.0504, dtype=torch.float16)\n", - "270 tensor(0.0473, dtype=torch.float16)\n", - "240 tensor(0.0369, dtype=torch.float16)\n", - "260 tensor(0.0369, dtype=torch.float16)\n", - "290 tensor(0.0369, dtype=torch.float16)\n", - "280 tensor(0.0336, dtype=torch.float16)\n", - "330 tensor(0.0287, dtype=torch.float16)\n", - "350 tensor(0.0238, dtype=torch.float16)\n", - "320 tensor(0.0210, dtype=torch.float16)\n", - "400 tensor(0.0210, dtype=torch.float16)\n", - "310 tensor(0.0174, dtype=torch.float16)\n", - "340 tensor(0.0159, dtype=torch.float16)\n", - "360 tensor(0.0136, dtype=torch.float16)\n", - "390 tensor(0.0123, dtype=torch.float16)\n", - "370 tensor(0.0123, dtype=torch.float16)\n", - "380 tensor(0.0120, dtype=torch.float16)\n", - "249 tensor(0.0096, dtype=torch.float16)\n", - "255 tensor(0.0093, dtype=torch.float16)\n", - "265 tensor(0.0082, dtype=torch.float16)\n", - "\u001b[92m32: Guess: $298.20 Truth: $257.99 Error: $40.21 SLE: 0.02 Item: TCMT Passenger Backr...\u001b[0m\n", - "19 tensor(0.0337, dtype=torch.float16)\n", - "16 tensor(0.0337, dtype=torch.float16)\n", - "18 tensor(0.0317, dtype=torch.float16)\n", - "17 tensor(0.0317, dtype=torch.float16)\n", - "12 tensor(0.0317, dtype=torch.float16)\n", - "14 tensor(0.0317, dtype=torch.float16)\n", - "13 tensor(0.0317, dtype=torch.float16)\n", - "11 tensor(0.0307, dtype=torch.float16)\n", - "21 tensor(0.0288, dtype=torch.float16)\n", - "9 tensor(0.0280, dtype=torch.float16)\n", - "15 tensor(0.0263, dtype=torch.float16)\n", - "22 tensor(0.0255, dtype=torch.float16)\n", - "24 tensor(0.0247, dtype=torch.float16)\n", - "23 tensor(0.0247, dtype=torch.float16)\n", - "26 tensor(0.0247, dtype=torch.float16)\n", - "10 tensor(0.0239, dtype=torch.float16)\n", - "8 tensor(0.0232, dtype=torch.float16)\n", - "29 tensor(0.0218, dtype=torch.float16)\n", - "20 tensor(0.0218, dtype=torch.float16)\n", - "28 tensor(0.0211, dtype=torch.float16)\n", - "\u001b[92m33: Guess: $17.35 Truth: $27.99 Error: $10.64 SLE: 0.21 Item: Alnicov 63.5MM Brass...\u001b[0m\n", - "91 tensor(0.0107, dtype=torch.float16)\n", - "121 tensor(0.0100, dtype=torch.float16)\n", - "101 tensor(0.0094, dtype=torch.float16)\n", - "131 tensor(0.0091, dtype=torch.float16)\n", - "127 tensor(0.0091, dtype=torch.float16)\n", - "102 tensor(0.0091, dtype=torch.float16)\n", - "103 tensor(0.0091, dtype=torch.float16)\n", - "122 tensor(0.0091, dtype=torch.float16)\n", - "81 tensor(0.0089, dtype=torch.float16)\n", - "123 tensor(0.0089, dtype=torch.float16)\n", - "94 tensor(0.0086, dtype=torch.float16)\n", - "141 tensor(0.0086, dtype=torch.float16)\n", - "92 tensor(0.0086, dtype=torch.float16)\n", - "132 tensor(0.0083, dtype=torch.float16)\n", - "124 tensor(0.0083, dtype=torch.float16)\n", - "142 tensor(0.0083, dtype=torch.float16)\n", - "114 tensor(0.0081, dtype=torch.float16)\n", - "148 tensor(0.0081, dtype=torch.float16)\n", - "118 tensor(0.0081, dtype=torch.float16)\n", - "111 tensor(0.0081, dtype=torch.float16)\n", - "\u001b[93m34: Guess: $115.39 Truth: $171.20 Error: $55.81 SLE: 0.15 Item: Subaru Forester Outb...\u001b[0m\n", - "249 tensor(0.0620, dtype=torch.float16)\n", - "295 tensor(0.0413, dtype=torch.float16)\n", - "265 tensor(0.0413, dtype=torch.float16)\n", - "229 tensor(0.0388, dtype=torch.float16)\n", - "289 tensor(0.0388, dtype=torch.float16)\n", - "285 tensor(0.0365, dtype=torch.float16)\n", - "195 tensor(0.0365, dtype=torch.float16)\n", - "349 tensor(0.0365, dtype=torch.float16)\n", - 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"545 tensor(0.0041, dtype=torch.float16)\n", - "584 tensor(0.0040, dtype=torch.float16)\n", - "616 tensor(0.0040, dtype=torch.float16)\n", - "577 tensor(0.0040, dtype=torch.float16)\n", - "535 tensor(0.0040, dtype=torch.float16)\n", - "591 tensor(0.0039, dtype=torch.float16)\n", - "536 tensor(0.0039, dtype=torch.float16)\n", - "546 tensor(0.0039, dtype=torch.float16)\n", - "582 tensor(0.0038, dtype=torch.float16)\n", - "604 tensor(0.0038, dtype=torch.float16)\n", - "574 tensor(0.0038, dtype=torch.float16)\n", - "534 tensor(0.0037, dtype=torch.float16)\n", - "563 tensor(0.0037, dtype=torch.float16)\n", - "596 tensor(0.0037, dtype=torch.float16)\n", - "587 tensor(0.0037, dtype=torch.float16)\n", - "\u001b[92m38: Guess: $568.15 Truth: $535.09 Error: $33.06 SLE: 0.00 Item: Gibson Performance E...\u001b[0m\n", - "12 tensor(0.0485, dtype=torch.float16)\n", - "15 tensor(0.0456, dtype=torch.float16)\n", - "14 tensor(0.0456, dtype=torch.float16)\n", - "20 tensor(0.0428, dtype=torch.float16)\n", - "18 tensor(0.0428, dtype=torch.float16)\n", - "16 tensor(0.0415, dtype=torch.float16)\n", - "10 tensor(0.0390, dtype=torch.float16)\n", - "19 tensor(0.0378, dtype=torch.float16)\n", - "13 tensor(0.0355, dtype=torch.float16)\n", - "17 tensor(0.0344, dtype=torch.float16)\n", - "22 tensor(0.0334, dtype=torch.float16)\n", - "11 tensor(0.0323, dtype=torch.float16)\n", - "25 tensor(0.0323, dtype=torch.float16)\n", - "9 tensor(0.0323, dtype=torch.float16)\n", - "24 tensor(0.0313, dtype=torch.float16)\n", - "8 tensor(0.0304, dtype=torch.float16)\n", - "21 tensor(0.0268, dtype=torch.float16)\n", - "23 tensor(0.0268, dtype=torch.float16)\n", - "7 tensor(0.0260, dtype=torch.float16)\n", - "6 tensor(0.0215, dtype=torch.float16)\n", - "\u001b[92m39: Guess: $15.60 Truth: $12.33 Error: $3.27 SLE: 0.05 Item: Bella Tunno Happy Li...\u001b[0m\n", - "80 tensor(0.0274, dtype=torch.float16)\n", - "90 tensor(0.0266, dtype=torch.float16)\n", - "100 tensor(0.0266, dtype=torch.float16)\n", - "70 tensor(0.0266, dtype=torch.float16)\n", - 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"11 tensor(0.0631, dtype=torch.float16)\n", - "17 tensor(0.0611, dtype=torch.float16)\n", - "15 tensor(0.0557, dtype=torch.float16)\n", - "18 tensor(0.0523, dtype=torch.float16)\n", - "19 tensor(0.0447, dtype=torch.float16)\n", - "10 tensor(0.0420, dtype=torch.float16)\n", - "9 tensor(0.0383, dtype=torch.float16)\n", - "21 tensor(0.0359, dtype=torch.float16)\n", - "20 tensor(0.0289, dtype=torch.float16)\n", - "22 tensor(0.0280, dtype=torch.float16)\n", - "23 tensor(0.0247, dtype=torch.float16)\n", - "8 tensor(0.0239, dtype=torch.float16)\n", - "24 tensor(0.0211, dtype=torch.float16)\n", - "26 tensor(0.0175, dtype=torch.float16)\n", - "7 tensor(0.0159, dtype=torch.float16)\n", - "25 tensor(0.0141, dtype=torch.float16)\n", - "\u001b[92m41: Guess: $15.45 Truth: $15.99 Error: $0.54 SLE: 0.00 Item: DCPOWER AC Adapter C...\u001b[0m\n", - "45 tensor(0.0143, dtype=torch.float16)\n", - "44 tensor(0.0139, dtype=torch.float16)\n", - "42 tensor(0.0139, dtype=torch.float16)\n", - "34 tensor(0.0134, dtype=torch.float16)\n", - 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"350 tensor(0.0218, dtype=torch.float16)\n", - "498 tensor(0.0192, dtype=torch.float16)\n", - "348 tensor(0.0170, dtype=torch.float16)\n", - "330 tensor(0.0170, dtype=torch.float16)\n", - "450 tensor(0.0170, dtype=torch.float16)\n", - "500 tensor(0.0150, dtype=torch.float16)\n", - "380 tensor(0.0145, dtype=torch.float16)\n", - "280 tensor(0.0136, dtype=torch.float16)\n", - "448 tensor(0.0132, dtype=torch.float16)\n", - "430 tensor(0.0128, dtype=torch.float16)\n", - "480 tensor(0.0109, dtype=torch.float16)\n", - "428 tensor(0.0106, dtype=torch.float16)\n", - "368 tensor(0.0103, dtype=torch.float16)\n", - "358 tensor(0.0103, dtype=torch.float16)\n", - "370 tensor(0.0097, dtype=torch.float16)\n", - "340 tensor(0.0091, dtype=torch.float16)\n", - "\u001b[93m44: Guess: $385.47 Truth: $599.95 Error: $214.48 SLE: 0.19 Item: Sony SSCS8 2-Way Cen...\u001b[0m\n", - "249 tensor(0.0270, dtype=torch.float16)\n", - "250 tensor(0.0261, dtype=torch.float16)\n", - "199 tensor(0.0246, dtype=torch.float16)\n", - "300 tensor(0.0224, dtype=torch.float16)\n", - "299 tensor(0.0204, dtype=torch.float16)\n", - "219 tensor(0.0197, dtype=torch.float16)\n", - "229 tensor(0.0185, dtype=torch.float16)\n", - "239 tensor(0.0164, dtype=torch.float16)\n", - "189 tensor(0.0159, dtype=torch.float16)\n", - "240 tensor(0.0154, dtype=torch.float16)\n", - "209 tensor(0.0154, dtype=torch.float16)\n", - "220 tensor(0.0136, dtype=torch.float16)\n", - "179 tensor(0.0131, dtype=torch.float16)\n", - "279 tensor(0.0120, dtype=torch.float16)\n", - "269 tensor(0.0120, dtype=torch.float16)\n", - "200 tensor(0.0120, dtype=torch.float16)\n", - "259 tensor(0.0116, dtype=torch.float16)\n", - "230 tensor(0.0116, dtype=torch.float16)\n", - "270 tensor(0.0116, dtype=torch.float16)\n", - "260 tensor(0.0112, dtype=torch.float16)\n", - "\u001b[93m45: Guess: $240.18 Truth: $194.99 Error: $45.19 SLE: 0.04 Item: ASUS Chromebook CX1,...\u001b[0m\n", - "400 tensor(0.0546, dtype=torch.float16)\n", - "300 tensor(0.0467, dtype=torch.float16)\n", - "500 tensor(0.0331, dtype=torch.float16)\n", - "350 tensor(0.0292, dtype=torch.float16)\n", - "399 tensor(0.0292, dtype=torch.float16)\n", - "299 tensor(0.0275, dtype=torch.float16)\n", - "250 tensor(0.0250, dtype=torch.float16)\n", - "499 tensor(0.0235, dtype=torch.float16)\n", - "600 tensor(0.0207, dtype=torch.float16)\n", - "450 tensor(0.0195, dtype=torch.float16)\n", - "599 tensor(0.0161, dtype=torch.float16)\n", - "349 tensor(0.0152, dtype=torch.float16)\n", - "249 tensor(0.0134, dtype=torch.float16)\n", - "200 tensor(0.0118, dtype=torch.float16)\n", - "280 tensor(0.0114, dtype=torch.float16)\n", - "700 tensor(0.0107, dtype=torch.float16)\n", - "199 tensor(0.0107, dtype=torch.float16)\n", - "550 tensor(0.0092, dtype=torch.float16)\n", - "330 tensor(0.0092, dtype=torch.float16)\n", - "449 tensor(0.0089, dtype=torch.float16)\n", - "\u001b[92m46: Guess: $392.94 Truth: $344.95 Error: $47.99 SLE: 0.02 Item: FiiO X7 32GB Hi-Res ...\u001b[0m\n", - "40 tensor(0.0529, dtype=torch.float16)\n", - "45 tensor(0.0453, dtype=torch.float16)\n", - "35 tensor(0.0439, dtype=torch.float16)\n", - "34 tensor(0.0331, dtype=torch.float16)\n", - "50 tensor(0.0321, dtype=torch.float16)\n", - "38 tensor(0.0321, dtype=torch.float16)\n", - "42 tensor(0.0311, dtype=torch.float16)\n", - "30 tensor(0.0311, dtype=torch.float16)\n", - "44 tensor(0.0292, dtype=torch.float16)\n", - "39 tensor(0.0283, dtype=torch.float16)\n", - "36 tensor(0.0258, dtype=torch.float16)\n", - "43 tensor(0.0250, dtype=torch.float16)\n", - "41 tensor(0.0250, dtype=torch.float16)\n", - "55 tensor(0.0250, dtype=torch.float16)\n", - "49 tensor(0.0242, dtype=torch.float16)\n", - "37 tensor(0.0228, dtype=torch.float16)\n", - "48 tensor(0.0228, dtype=torch.float16)\n", - "32 tensor(0.0221, dtype=torch.float16)\n", - "33 tensor(0.0201, dtype=torch.float16)\n", - "29 tensor(0.0195, dtype=torch.float16)\n", - "\u001b[92m47: Guess: $40.13 Truth: $37.99 Error: $2.14 SLE: 0.00 Item: TORRO Leather Case C...\u001b[0m\n", - "205 tensor(0.0130, dtype=torch.float16)\n", - "193 tensor(0.0130, dtype=torch.float16)\n", - "215 tensor(0.0122, dtype=torch.float16)\n", - "216 tensor(0.0122, dtype=torch.float16)\n", - "209 tensor(0.0122, dtype=torch.float16)\n", - "194 tensor(0.0118, dtype=torch.float16)\n", - "206 tensor(0.0118, dtype=torch.float16)\n", - "196 tensor(0.0118, dtype=torch.float16)\n", - "214 tensor(0.0118, dtype=torch.float16)\n", - "197 tensor(0.0118, dtype=torch.float16)\n", - "217 tensor(0.0118, dtype=torch.float16)\n", - "204 tensor(0.0115, dtype=torch.float16)\n", - "203 tensor(0.0115, dtype=torch.float16)\n", - "192 tensor(0.0115, dtype=torch.float16)\n", - "232 tensor(0.0111, dtype=torch.float16)\n", - "212 tensor(0.0111, dtype=torch.float16)\n", - "224 tensor(0.0111, dtype=torch.float16)\n", - "198 tensor(0.0108, dtype=torch.float16)\n", - "233 tensor(0.0104, dtype=torch.float16)\n", - "207 tensor(0.0104, dtype=torch.float16)\n", - "\u001b[92m48: Guess: $208.10 Truth: $224.35 Error: $16.25 SLE: 0.01 Item: Universal Air Condit...\u001b[0m\n", - "800 tensor(0.0041, dtype=torch.float16)\n", - "700 tensor(0.0040, dtype=torch.float16)\n", - "799 tensor(0.0037, dtype=torch.float16)\n", - "798 tensor(0.0034, dtype=torch.float16)\n", - " tensor(0.0033, dtype=torch.float16)\n", - "675 tensor(0.0033, dtype=torch.float16)\n", - "900 tensor(0.0032, dtype=torch.float16)\n", - "699 tensor(0.0032, dtype=torch.float16)\n", - "749 tensor(0.0032, dtype=torch.float16)\n", - "750 tensor(0.0032, dtype=torch.float16)\n", - "784 tensor(0.0031, dtype=torch.float16)\n", - "850 tensor(0.0031, dtype=torch.float16)\n", - "705 tensor(0.0031, dtype=torch.float16)\n", - "770 tensor(0.0030, dtype=torch.float16)\n", - "786 tensor(0.0030, dtype=torch.float16)\n", - "783 tensor(0.0030, dtype=torch.float16)\n", - "849 tensor(0.0030, dtype=torch.float16)\n", - "779 tensor(0.0029, dtype=torch.float16)\n", - "729 tensor(0.0029, dtype=torch.float16)\n", - "899 tensor(0.0029, dtype=torch.float16)\n", - "\u001b[92m49: Guess: $777.96 Truth: $814.00 Error: $36.04 SLE: 0.00 Item: Street Series Stainl...\u001b[0m\n", - "399 tensor(0.0375, dtype=torch.float16)\n", - "400 tensor(0.0363, dtype=torch.float16)\n", - "300 tensor(0.0227, dtype=torch.float16)\n", - "350 tensor(0.0227, dtype=torch.float16)\n", - "299 tensor(0.0214, dtype=torch.float16)\n", - "349 tensor(0.0183, dtype=torch.float16)\n", - "379 tensor(0.0156, dtype=torch.float16)\n", - "450 tensor(0.0151, dtype=torch.float16)\n", - "380 tensor(0.0142, dtype=torch.float16)\n", - "330 tensor(0.0138, dtype=torch.float16)\n", - "370 tensor(0.0130, dtype=torch.float16)\n", - "359 tensor(0.0126, dtype=torch.float16)\n", - "369 tensor(0.0124, dtype=torch.float16)\n", - "329 tensor(0.0124, dtype=torch.float16)\n", - "389 tensor(0.0124, dtype=torch.float16)\n", - "390 tensor(0.0122, dtype=torch.float16)\n", - "449 tensor(0.0122, dtype=torch.float16)\n", - "499 tensor(0.0118, dtype=torch.float16)\n", - "360 tensor(0.0116, dtype=torch.float16)\n", - "340 tensor(0.0116, dtype=torch.float16)\n", - "\u001b[92m50: Guess: $372.98 Truth: $439.88 Error: $66.90 SLE: 0.03 Item: Lenovo IdeaPad 3 Lap...\u001b[0m\n", - "249 tensor(0.0696, dtype=torch.float16)\n", - "299 tensor(0.0272, dtype=torch.float16)\n", - "269 tensor(0.0233, dtype=torch.float16)\n", - "279 tensor(0.0233, dtype=torch.float16)\n", - "259 tensor(0.0226, dtype=torch.float16)\n", - "289 tensor(0.0199, dtype=torch.float16)\n", - "250 tensor(0.0187, dtype=torch.float16)\n", - "254 tensor(0.0155, dtype=torch.float16)\n", - "274 tensor(0.0146, dtype=torch.float16)\n", - "280 tensor(0.0146, dtype=torch.float16)\n", - "349 tensor(0.0141, dtype=torch.float16)\n", - "252 tensor(0.0129, dtype=torch.float16)\n", - "294 tensor(0.0125, dtype=torch.float16)\n", - "239 tensor(0.0114, dtype=torch.float16)\n", - "232 tensor(0.0110, dtype=torch.float16)\n", - "272 tensor(0.0103, dtype=torch.float16)\n", - "244 tensor(0.0103, dtype=torch.float16)\n", - "288 tensor(0.0103, dtype=torch.float16)\n", - "293 tensor(0.0103, dtype=torch.float16)\n", - "236 tensor(0.0103, dtype=torch.float16)\n", - "\u001b[93m51: Guess: $268.52 Truth: $341.43 Error: $72.91 SLE: 0.06 Item: Access Bed Covers To...\u001b[0m\n", - "45 tensor(0.0163, dtype=torch.float16)\n", - "65 tensor(0.0158, dtype=torch.float16)\n", - "55 tensor(0.0158, dtype=torch.float16)\n", - "75 tensor(0.0153, dtype=torch.float16)\n", - "60 tensor(0.0144, dtype=torch.float16)\n", - "50 tensor(0.0144, dtype=torch.float16)\n", - "85 tensor(0.0127, dtype=torch.float16)\n", - "63 tensor(0.0123, dtype=torch.float16)\n", - "48 tensor(0.0123, dtype=torch.float16)\n", - "54 tensor(0.0123, dtype=torch.float16)\n", - "70 tensor(0.0123, dtype=torch.float16)\n", - "49 tensor(0.0119, dtype=torch.float16)\n", - "40 tensor(0.0119, dtype=torch.float16)\n", - "44 tensor(0.0119, dtype=torch.float16)\n", - "80 tensor(0.0116, dtype=torch.float16)\n", - "64 tensor(0.0116, dtype=torch.float16)\n", - "72 tensor(0.0112, dtype=torch.float16)\n", - "68 tensor(0.0112, dtype=torch.float16)\n", - "58 tensor(0.0112, dtype=torch.float16)\n", - "52 tensor(0.0112, dtype=torch.float16)\n", - "\u001b[92m52: Guess: $59.71 Truth: $46.78 Error: $12.93 SLE: 0.06 Item: G.I. JOE Hasbro 3 3/...\u001b[0m\n", - "184 tensor(0.0062, dtype=torch.float16)\n", - "172 tensor(0.0058, dtype=torch.float16)\n", - "192 tensor(0.0056, dtype=torch.float16)\n", - "164 tensor(0.0056, dtype=torch.float16)\n", - "193 tensor(0.0056, dtype=torch.float16)\n", - "186 tensor(0.0055, dtype=torch.float16)\n", - "173 tensor(0.0055, dtype=torch.float16)\n", - "157 tensor(0.0055, dtype=torch.float16)\n", - "187 tensor(0.0055, dtype=torch.float16)\n", - "166 tensor(0.0055, dtype=torch.float16)\n", - "188 tensor(0.0053, dtype=torch.float16)\n", - "176 tensor(0.0053, dtype=torch.float16)\n", - "178 tensor(0.0053, dtype=torch.float16)\n", - "204 tensor(0.0053, dtype=torch.float16)\n", - "171 tensor(0.0053, dtype=torch.float16)\n", - "196 tensor(0.0053, dtype=torch.float16)\n", - "198 tensor(0.0053, dtype=torch.float16)\n", - "142 tensor(0.0053, dtype=torch.float16)\n", - "162 tensor(0.0051, dtype=torch.float16)\n", - "161 tensor(0.0051, dtype=torch.float16)\n", - "\u001b[92m53: Guess: $177.60 Truth: $171.44 Error: $6.16 SLE: 0.00 Item: T&S Brass Double Pan...\u001b[0m\n", - "300 tensor(0.0163, dtype=torch.float16)\n", - "400 tensor(0.0163, dtype=torch.float16)\n", - "260 tensor(0.0115, dtype=torch.float16)\n", - "290 tensor(0.0112, dtype=torch.float16)\n", - "500 tensor(0.0112, dtype=torch.float16)\n", - "250 tensor(0.0108, dtype=torch.float16)\n", - "360 tensor(0.0105, dtype=torch.float16)\n", - "350 tensor(0.0105, dtype=torch.float16)\n", - "390 tensor(0.0105, dtype=torch.float16)\n", - "280 tensor(0.0102, dtype=torch.float16)\n", - "240 tensor(0.0099, dtype=torch.float16)\n", - "600 tensor(0.0096, dtype=torch.float16)\n", - "270 tensor(0.0096, dtype=torch.float16)\n", - "450 tensor(0.0090, dtype=torch.float16)\n", - "380 tensor(0.0090, dtype=torch.float16)\n", - "330 tensor(0.0087, dtype=torch.float16)\n", - "299 tensor(0.0082, dtype=torch.float16)\n", - "320 tensor(0.0079, dtype=torch.float16)\n", - "399 tensor(0.0079, dtype=torch.float16)\n", - "340 tensor(0.0074, dtype=torch.float16)\n", - "\u001b[93m54: Guess: $349.46 Truth: $458.00 Error: $108.54 SLE: 0.07 Item: ZTUOAUMA Fuel Inject...\u001b[0m\n", - "250 tensor(0.0171, dtype=torch.float16)\n", - "150 tensor(0.0137, dtype=torch.float16)\n", - "200 tensor(0.0133, dtype=torch.float16)\n", - "175 tensor(0.0117, dtype=torch.float16)\n", - "300 tensor(0.0117, dtype=torch.float16)\n", - "170 tensor(0.0117, dtype=torch.float16)\n", - "180 tensor(0.0117, dtype=torch.float16)\n", - "145 tensor(0.0114, dtype=torch.float16)\n", - "140 tensor(0.0107, dtype=torch.float16)\n", - "149 tensor(0.0104, dtype=torch.float16)\n", - "160 tensor(0.0104, dtype=torch.float16)\n", - "155 tensor(0.0104, dtype=torch.float16)\n", - "165 tensor(0.0100, dtype=torch.float16)\n", - "169 tensor(0.0100, dtype=torch.float16)\n", - "230 tensor(0.0097, dtype=torch.float16)\n", - "185 tensor(0.0097, dtype=torch.float16)\n", - "135 tensor(0.0097, dtype=torch.float16)\n", - "144 tensor(0.0089, dtype=torch.float16)\n", - "190 tensor(0.0086, dtype=torch.float16)\n", - "240 tensor(0.0083, dtype=torch.float16)\n", - "\u001b[93m55: Guess: $183.52 Truth: $130.75 Error: $52.77 SLE: 0.11 Item: Hp Prime Graphing Ca...\u001b[0m\n", - "31 tensor(0.0175, dtype=torch.float16)\n", - "41 tensor(0.0175, dtype=torch.float16)\n", - "24 tensor(0.0169, dtype=torch.float16)\n", - "34 tensor(0.0164, dtype=torch.float16)\n", - "21 tensor(0.0159, dtype=torch.float16)\n", - "23 tensor(0.0159, dtype=torch.float16)\n", - "22 tensor(0.0154, dtype=torch.float16)\n", - "42 tensor(0.0154, dtype=torch.float16)\n", - "32 tensor(0.0150, dtype=torch.float16)\n", - "28 tensor(0.0150, dtype=torch.float16)\n", - "27 tensor(0.0150, dtype=torch.float16)\n", - "38 tensor(0.0140, dtype=torch.float16)\n", - "29 tensor(0.0140, dtype=torch.float16)\n", - "25 tensor(0.0140, dtype=torch.float16)\n", - "44 tensor(0.0136, dtype=torch.float16)\n", - "33 tensor(0.0136, dtype=torch.float16)\n", - "26 tensor(0.0136, dtype=torch.float16)\n", - "51 tensor(0.0128, dtype=torch.float16)\n", - "43 tensor(0.0128, dtype=torch.float16)\n", - "37 tensor(0.0124, dtype=torch.float16)\n", - "\u001b[93m56: Guess: $32.23 Truth: $83.81 Error: $51.58 SLE: 0.88 Item: Lowrance Nmea 2000 2...\u001b[0m\n", - "131 tensor(0.0090, dtype=torch.float16)\n", - "141 tensor(0.0090, dtype=torch.float16)\n", - "151 tensor(0.0079, dtype=torch.float16)\n", - "132 tensor(0.0079, dtype=torch.float16)\n", - "161 tensor(0.0077, dtype=torch.float16)\n", - "152 tensor(0.0077, dtype=torch.float16)\n", - "157 tensor(0.0075, dtype=torch.float16)\n", - "171 tensor(0.0075, dtype=torch.float16)\n", - "142 tensor(0.0075, dtype=torch.float16)\n", - "148 tensor(0.0075, dtype=torch.float16)\n", - "121 tensor(0.0072, dtype=torch.float16)\n", - "122 tensor(0.0072, dtype=torch.float16)\n", - "172 tensor(0.0070, dtype=torch.float16)\n", - "250 tensor(0.0070, dtype=torch.float16)\n", - "147 tensor(0.0070, dtype=torch.float16)\n", - "173 tensor(0.0068, dtype=torch.float16)\n", - "144 tensor(0.0068, dtype=torch.float16)\n", - "162 tensor(0.0068, dtype=torch.float16)\n", - "153 tensor(0.0068, dtype=torch.float16)\n", - "163 tensor(0.0066, dtype=torch.float16)\n", - "\u001b[91m57: Guess: $153.86 Truth: $386.39 Error: $232.53 SLE: 0.84 Item: Jeep Genuine Accesso...\u001b[0m\n", - "299 tensor(0.0257, dtype=torch.float16)\n", - "169 tensor(0.0234, dtype=torch.float16)\n", - "199 tensor(0.0234, dtype=torch.float16)\n", - "249 tensor(0.0227, dtype=torch.float16)\n", - "179 tensor(0.0213, dtype=torch.float16)\n", - "229 tensor(0.0200, dtype=torch.float16)\n", - "219 tensor(0.0194, dtype=torch.float16)\n", - "159 tensor(0.0188, dtype=torch.float16)\n", - "149 tensor(0.0182, dtype=torch.float16)\n", - "250 tensor(0.0161, dtype=torch.float16)\n", - "189 tensor(0.0156, dtype=torch.float16)\n", - "139 tensor(0.0156, dtype=torch.float16)\n", - "239 tensor(0.0156, dtype=torch.float16)\n", - "259 tensor(0.0142, dtype=torch.float16)\n", - "300 tensor(0.0133, dtype=torch.float16)\n", - "269 tensor(0.0133, dtype=torch.float16)\n", - "129 tensor(0.0129, dtype=torch.float16)\n", - "240 tensor(0.0125, dtype=torch.float16)\n", - "209 tensor(0.0125, dtype=torch.float16)\n", - "279 tensor(0.0118, dtype=torch.float16)\n", - "\u001b[93m58: Guess: $216.50 Truth: $169.00 Error: $47.50 SLE: 0.06 Item: GODOX CB-06 Hard Car...\u001b[0m\n", - "15 tensor(0.0675, dtype=torch.float16)\n", - "14 tensor(0.0614, dtype=torch.float16)\n", - "18 tensor(0.0577, dtype=torch.float16)\n", - "12 tensor(0.0559, dtype=torch.float16)\n", - "17 tensor(0.0509, dtype=torch.float16)\n", - "16 tensor(0.0509, dtype=torch.float16)\n", - "20 tensor(0.0494, dtype=torch.float16)\n", - "19 tensor(0.0479, dtype=torch.float16)\n", - "13 tensor(0.0450, dtype=torch.float16)\n", - "10 tensor(0.0409, dtype=torch.float16)\n", - "22 tensor(0.0397, dtype=torch.float16)\n", - "11 tensor(0.0397, dtype=torch.float16)\n", - "21 tensor(0.0339, dtype=torch.float16)\n", - "25 tensor(0.0319, dtype=torch.float16)\n", - "24 tensor(0.0299, dtype=torch.float16)\n", - "9 tensor(0.0299, dtype=torch.float16)\n", - "23 tensor(0.0281, dtype=torch.float16)\n", - "8 tensor(0.0241, dtype=torch.float16)\n", - "7 tensor(0.0187, dtype=torch.float16)\n", - "26 tensor(0.0155, dtype=torch.float16)\n", - "\u001b[92m59: Guess: $16.28 Truth: $17.95 Error: $1.67 SLE: 0.01 Item: Au-Tomotive Gold, IN...\u001b[0m\n", - "249 tensor(0.0241, dtype=torch.float16)\n", - "239 tensor(0.0206, dtype=torch.float16)\n", - "219 tensor(0.0200, dtype=torch.float16)\n", - "259 tensor(0.0188, dtype=torch.float16)\n", - "209 tensor(0.0182, dtype=torch.float16)\n", - "229 tensor(0.0156, dtype=torch.float16)\n", - "179 tensor(0.0151, dtype=torch.float16)\n", - "299 tensor(0.0146, dtype=torch.float16)\n", - "189 tensor(0.0146, dtype=torch.float16)\n", - "269 tensor(0.0142, dtype=torch.float16)\n", - "169 tensor(0.0137, dtype=torch.float16)\n", - "250 tensor(0.0133, dtype=torch.float16)\n", - "159 tensor(0.0121, dtype=torch.float16)\n", - "279 tensor(0.0114, dtype=torch.float16)\n", - "240 tensor(0.0114, dtype=torch.float16)\n", - "199 tensor(0.0110, dtype=torch.float16)\n", - "289 tensor(0.0104, dtype=torch.float16)\n", - "198 tensor(0.0086, dtype=torch.float16)\n", - "270 tensor(0.0083, dtype=torch.float16)\n", - "139 tensor(0.0081, dtype=torch.float16)\n", - "\u001b[92m60: Guess: $228.58 Truth: $269.00 Error: $40.42 SLE: 0.03 Item: Snailfly Black Roof ...\u001b[0m\n", - "69 tensor(0.0119, dtype=torch.float16)\n", - "59 tensor(0.0116, dtype=torch.float16)\n", - "49 tensor(0.0116, dtype=torch.float16)\n", - "56 tensor(0.0109, dtype=torch.float16)\n", - "66 tensor(0.0109, dtype=torch.float16)\n", - "46 tensor(0.0109, dtype=torch.float16)\n", - "39 tensor(0.0102, dtype=torch.float16)\n", - "76 tensor(0.0102, dtype=torch.float16)\n", - "53 tensor(0.0102, dtype=torch.float16)\n", - "48 tensor(0.0102, dtype=torch.float16)\n", - "58 tensor(0.0102, dtype=torch.float16)\n", - "68 tensor(0.0099, dtype=torch.float16)\n", - "55 tensor(0.0099, dtype=torch.float16)\n", - "36 tensor(0.0099, dtype=torch.float16)\n", - "54 tensor(0.0099, dtype=torch.float16)\n", - "43 tensor(0.0099, dtype=torch.float16)\n", - "47 tensor(0.0096, dtype=torch.float16)\n", - "44 tensor(0.0096, dtype=torch.float16)\n", - "60 tensor(0.0096, dtype=torch.float16)\n", - "38 tensor(0.0096, dtype=torch.float16)\n", - "\u001b[92m61: Guess: $53.46 Truth: $77.77 Error: $24.31 SLE: 0.14 Item: KING SHA Anti Glare ...\u001b[0m\n", - "81 tensor(0.0205, dtype=torch.float16)\n", - "91 tensor(0.0181, dtype=torch.float16)\n", - "71 tensor(0.0176, dtype=torch.float16)\n", - "73 tensor(0.0170, dtype=torch.float16)\n", - "93 tensor(0.0170, dtype=torch.float16)\n", - "83 tensor(0.0170, dtype=torch.float16)\n", - "87 tensor(0.0170, dtype=torch.float16)\n", - "82 tensor(0.0170, dtype=torch.float16)\n", - "74 tensor(0.0160, dtype=torch.float16)\n", - "86 tensor(0.0160, dtype=torch.float16)\n", - "84 tensor(0.0155, dtype=torch.float16)\n", - "72 tensor(0.0155, dtype=torch.float16)\n", - "103 tensor(0.0146, dtype=torch.float16)\n", - "77 tensor(0.0146, dtype=torch.float16)\n", - "94 tensor(0.0146, dtype=torch.float16)\n", - "101 tensor(0.0141, dtype=torch.float16)\n", - "61 tensor(0.0141, dtype=torch.float16)\n", - "92 tensor(0.0141, dtype=torch.float16)\n", - "104 tensor(0.0137, dtype=torch.float16)\n", - "76 tensor(0.0133, dtype=torch.float16)\n", - "\u001b[92m62: Guess: $84.02 Truth: $88.99 Error: $4.97 SLE: 0.00 Item: APS Compatible with ...\u001b[0m\n", - "299 tensor(0.0056, dtype=torch.float16)\n", - "265 tensor(0.0054, dtype=torch.float16)\n", - "289 tensor(0.0053, dtype=torch.float16)\n", - "240 tensor(0.0053, dtype=torch.float16)\n", - "238 tensor(0.0052, dtype=torch.float16)\n", - "270 tensor(0.0051, dtype=torch.float16)\n", - "237 tensor(0.0051, dtype=torch.float16)\n", - "250 tensor(0.0051, dtype=torch.float16)\n", - "260 tensor(0.0051, dtype=torch.float16)\n", - "279 tensor(0.0050, dtype=torch.float16)\n", - "300 tensor(0.0050, dtype=torch.float16)\n", - "239 tensor(0.0049, dtype=torch.float16)\n", - "236 tensor(0.0049, dtype=torch.float16)\n", - "269 tensor(0.0049, dtype=torch.float16)\n", - "266 tensor(0.0049, dtype=torch.float16)\n", - "268 tensor(0.0048, dtype=torch.float16)\n", - "255 tensor(0.0048, dtype=torch.float16)\n", - "249 tensor(0.0048, dtype=torch.float16)\n", - "278 tensor(0.0047, dtype=torch.float16)\n", - "274 tensor(0.0046, dtype=torch.float16)\n", - "\u001b[93m63: Guess: $263.22 Truth: $364.41 Error: $101.19 SLE: 0.11 Item: Wilwood Engineering ...\u001b[0m\n", - "141 tensor(0.0174, dtype=torch.float16)\n", - "154 tensor(0.0164, dtype=torch.float16)\n", - "151 tensor(0.0154, dtype=torch.float16)\n", - "131 tensor(0.0149, dtype=torch.float16)\n", - "147 tensor(0.0149, dtype=torch.float16)\n", - "142 tensor(0.0144, dtype=torch.float16)\n", - "152 tensor(0.0144, dtype=torch.float16)\n", - "153 tensor(0.0144, dtype=torch.float16)\n", - "163 tensor(0.0131, dtype=torch.float16)\n", - "171 tensor(0.0131, dtype=torch.float16)\n", - "157 tensor(0.0131, dtype=torch.float16)\n", - "121 tensor(0.0131, dtype=torch.float16)\n", - "161 tensor(0.0127, dtype=torch.float16)\n", - "132 tensor(0.0120, dtype=torch.float16)\n", - "156 tensor(0.0120, dtype=torch.float16)\n", - "123 tensor(0.0120, dtype=torch.float16)\n", - "164 tensor(0.0120, dtype=torch.float16)\n", - "148 tensor(0.0120, dtype=torch.float16)\n", - "122 tensor(0.0120, dtype=torch.float16)\n", - "144 tensor(0.0120, dtype=torch.float16)\n", - "\u001b[92m64: Guess: $146.76 Truth: $127.03 Error: $19.73 SLE: 0.02 Item: ACDelco Gold Starter...\u001b[0m\n", - "536 tensor(0.0084, dtype=torch.float16)\n", - "591 tensor(0.0074, dtype=torch.float16)\n", - "579 tensor(0.0074, dtype=torch.float16)\n", - "526 tensor(0.0074, dtype=torch.float16)\n", - "532 tensor(0.0070, dtype=torch.float16)\n", - "545 tensor(0.0070, dtype=torch.float16)\n", - "590 tensor(0.0068, dtype=torch.float16)\n", - "570 tensor(0.0068, dtype=torch.float16)\n", - "655 tensor(0.0067, dtype=torch.float16)\n", - "580 tensor(0.0067, dtype=torch.float16)\n", - "584 tensor(0.0066, dtype=torch.float16)\n", - "573 tensor(0.0065, dtype=torch.float16)\n", - "595 tensor(0.0064, dtype=torch.float16)\n", - "546 tensor(0.0064, dtype=torch.float16)\n", - "578 tensor(0.0063, dtype=torch.float16)\n", - "535 tensor(0.0062, dtype=torch.float16)\n", - "585 tensor(0.0061, dtype=torch.float16)\n", - "598 tensor(0.0060, dtype=torch.float16)\n", - "534 tensor(0.0060, dtype=torch.float16)\n", - "552 tensor(0.0059, dtype=torch.float16)\n", - "\u001b[93m65: Guess: $568.76 Truth: $778.95 Error: $210.19 SLE: 0.10 Item: UWS Matte Black Heav...\u001b[0m\n", - "198 tensor(0.0111, dtype=torch.float16)\n", - "250 tensor(0.0111, dtype=torch.float16)\n", - "189 tensor(0.0105, dtype=torch.float16)\n", - "195 tensor(0.0105, dtype=torch.float16)\n", - "199 tensor(0.0101, dtype=torch.float16)\n", - "219 tensor(0.0095, dtype=torch.float16)\n", - "194 tensor(0.0092, dtype=torch.float16)\n", - "229 tensor(0.0090, dtype=torch.float16)\n", - "209 tensor(0.0090, dtype=torch.float16)\n", - "249 tensor(0.0090, dtype=torch.float16)\n", - "186 tensor(0.0087, dtype=torch.float16)\n", - "240 tensor(0.0087, dtype=torch.float16)\n", - "196 tensor(0.0087, dtype=torch.float16)\n", - "185 tensor(0.0084, dtype=torch.float16)\n", - "197 tensor(0.0084, dtype=torch.float16)\n", - "193 tensor(0.0082, dtype=torch.float16)\n", - "215 tensor(0.0082, dtype=torch.float16)\n", - "225 tensor(0.0082, dtype=torch.float16)\n", - "192 tensor(0.0079, dtype=torch.float16)\n", - "210 tensor(0.0077, dtype=torch.float16)\n", - "\u001b[92m66: Guess: $208.71 Truth: $206.66 Error: $2.05 SLE: 0.00 Item: Dell Latitude E5440 ...\u001b[0m\n", - "46 tensor(0.0229, dtype=torch.float16)\n", - "36 tensor(0.0208, dtype=torch.float16)\n", - "47 tensor(0.0178, dtype=torch.float16)\n", - "56 tensor(0.0178, dtype=torch.float16)\n", - "39 tensor(0.0173, dtype=torch.float16)\n", - "37 tensor(0.0168, dtype=torch.float16)\n", - "43 tensor(0.0168, dtype=torch.float16)\n", - "49 tensor(0.0157, dtype=torch.float16)\n", - "66 tensor(0.0157, dtype=torch.float16)\n", - "38 tensor(0.0157, dtype=torch.float16)\n", - "33 tensor(0.0157, dtype=torch.float16)\n", - "44 tensor(0.0153, dtype=torch.float16)\n", - "26 tensor(0.0153, dtype=torch.float16)\n", - "29 tensor(0.0148, dtype=torch.float16)\n", - "41 tensor(0.0148, dtype=torch.float16)\n", - "34 tensor(0.0143, dtype=torch.float16)\n", - "53 tensor(0.0143, dtype=torch.float16)\n", - "40 tensor(0.0143, dtype=torch.float16)\n", - "57 tensor(0.0135, dtype=torch.float16)\n", - "48 tensor(0.0135, dtype=torch.float16)\n", - "\u001b[92m67: Guess: $43.07 Truth: $35.94 Error: $7.13 SLE: 0.03 Item: (Plug and Play) Spar...\u001b[0m\n", - "250 tensor(0.0164, dtype=torch.float16)\n", - "300 tensor(0.0136, dtype=torch.float16)\n", - "150 tensor(0.0096, dtype=torch.float16)\n", - "240 tensor(0.0088, dtype=torch.float16)\n", - "100 tensor(0.0085, dtype=torch.float16)\n", - "200 tensor(0.0082, dtype=torch.float16)\n", - "140 tensor(0.0080, dtype=torch.float16)\n", - "120 tensor(0.0071, dtype=torch.float16)\n", - "130 tensor(0.0071, dtype=torch.float16)\n", - "135 tensor(0.0068, dtype=torch.float16)\n", - "115 tensor(0.0066, dtype=torch.float16)\n", - "99 tensor(0.0066, dtype=torch.float16)\n", - "145 tensor(0.0066, dtype=torch.float16)\n", - "105 tensor(0.0064, dtype=torch.float16)\n", - "160 tensor(0.0064, dtype=torch.float16)\n", - "110 tensor(0.0064, dtype=torch.float16)\n", - "125 tensor(0.0062, dtype=torch.float16)\n", - "148 tensor(0.0062, dtype=torch.float16)\n", - "98 tensor(0.0062, dtype=torch.float16)\n", - "114 tensor(0.0060, dtype=torch.float16)\n", - "\u001b[92m68: Guess: $163.47 Truth: $149.00 Error: $14.47 SLE: 0.01 Item: The Ultimate Roadsid...\u001b[0m\n", - "209 tensor(0.0111, dtype=torch.float16)\n", - "239 tensor(0.0098, dtype=torch.float16)\n", - "208 tensor(0.0098, dtype=torch.float16)\n", - "215 tensor(0.0098, dtype=torch.float16)\n", - "216 tensor(0.0095, dtype=torch.float16)\n", - "240 tensor(0.0095, dtype=torch.float16)\n", - "206 tensor(0.0092, dtype=torch.float16)\n", - "198 tensor(0.0089, dtype=torch.float16)\n", - "236 tensor(0.0089, dtype=torch.float16)\n", - "186 tensor(0.0089, dtype=torch.float16)\n", - "218 tensor(0.0089, dtype=torch.float16)\n", - "224 tensor(0.0087, dtype=torch.float16)\n", - "250 tensor(0.0087, dtype=torch.float16)\n", - "235 tensor(0.0087, dtype=torch.float16)\n", - "197 tensor(0.0084, dtype=torch.float16)\n", - "232 tensor(0.0084, dtype=torch.float16)\n", - "205 tensor(0.0084, dtype=torch.float16)\n", - "207 tensor(0.0084, dtype=torch.float16)\n", - "245 tensor(0.0084, dtype=torch.float16)\n", - "196 tensor(0.0081, dtype=torch.float16)\n", - "\u001b[92m69: Guess: $218.10 Truth: $251.98 Error: $33.88 SLE: 0.02 Item: Brand New 18 x 8.5 R...\u001b[0m\n", - "250 tensor(0.0107, dtype=torch.float16)\n", - "240 tensor(0.0098, dtype=torch.float16)\n", - "239 tensor(0.0095, dtype=torch.float16)\n", - "260 tensor(0.0079, dtype=torch.float16)\n", - "249 tensor(0.0076, dtype=torch.float16)\n", - "198 tensor(0.0074, dtype=torch.float16)\n", - "209 tensor(0.0074, dtype=torch.float16)\n", - "186 tensor(0.0074, dtype=torch.float16)\n", - "270 tensor(0.0074, dtype=torch.float16)\n", - "215 tensor(0.0074, dtype=torch.float16)\n", - "300 tensor(0.0072, dtype=torch.float16)\n", - "196 tensor(0.0072, dtype=torch.float16)\n", - "259 tensor(0.0072, dtype=torch.float16)\n", - "255 tensor(0.0069, dtype=torch.float16)\n", - "235 tensor(0.0067, dtype=torch.float16)\n", - "265 tensor(0.0065, dtype=torch.float16)\n", - "238 tensor(0.0065, dtype=torch.float16)\n", - "290 tensor(0.0065, dtype=torch.float16)\n", - "197 tensor(0.0065, dtype=torch.float16)\n", - "236 tensor(0.0065, dtype=torch.float16)\n", - "\u001b[93m70: Guess: $239.42 Truth: $160.00 Error: $79.42 SLE: 0.16 Item: Headlight Headlamp L...\u001b[0m\n", - "35 tensor(0.0413, dtype=torch.float16)\n", - "25 tensor(0.0353, dtype=torch.float16)\n", - "34 tensor(0.0342, dtype=torch.float16)\n", - "32 tensor(0.0331, dtype=torch.float16)\n", - "28 tensor(0.0311, dtype=torch.float16)\n", - "30 tensor(0.0311, dtype=torch.float16)\n", - "31 tensor(0.0311, dtype=torch.float16)\n", - "38 tensor(0.0302, dtype=torch.float16)\n", - "40 tensor(0.0293, dtype=torch.float16)\n", - "33 tensor(0.0293, dtype=torch.float16)\n", - "27 tensor(0.0284, dtype=torch.float16)\n", - "29 tensor(0.0284, dtype=torch.float16)\n", - "42 tensor(0.0266, dtype=torch.float16)\n", - "36 tensor(0.0258, dtype=torch.float16)\n", - "45 tensor(0.0258, dtype=torch.float16)\n", - "37 tensor(0.0235, dtype=torch.float16)\n", - "41 tensor(0.0235, dtype=torch.float16)\n", - "26 tensor(0.0228, dtype=torch.float16)\n", - "22 tensor(0.0221, dtype=torch.float16)\n", - "24 tensor(0.0221, dtype=torch.float16)\n", - "\u001b[92m71: Guess: $32.77 Truth: $39.99 Error: $7.22 SLE: 0.04 Item: Lilo And Stitch Delu...\u001b[0m\n", - "250 tensor(0.0132, dtype=torch.float16)\n", - "252 tensor(0.0124, dtype=torch.float16)\n", - "260 tensor(0.0120, dtype=torch.float16)\n", - "242 tensor(0.0116, dtype=torch.float16)\n", - "255 tensor(0.0116, dtype=torch.float16)\n", - "265 tensor(0.0116, dtype=torch.float16)\n", - "233 tensor(0.0113, dtype=torch.float16)\n", - "264 tensor(0.0113, dtype=torch.float16)\n", - "232 tensor(0.0109, dtype=torch.float16)\n", - "245 tensor(0.0109, dtype=torch.float16)\n", - "251 tensor(0.0109, dtype=torch.float16)\n", - "263 tensor(0.0106, dtype=torch.float16)\n", - "259 tensor(0.0106, dtype=torch.float16)\n", - "249 tensor(0.0106, dtype=torch.float16)\n", - "241 tensor(0.0106, dtype=torch.float16)\n", - "261 tensor(0.0103, dtype=torch.float16)\n", - "256 tensor(0.0103, dtype=torch.float16)\n", - "266 tensor(0.0103, dtype=torch.float16)\n", - "253 tensor(0.0103, dtype=torch.float16)\n", - "270 tensor(0.0103, dtype=torch.float16)\n", - "\u001b[93m72: Guess: $253.22 Truth: $362.41 Error: $109.19 SLE: 0.13 Item: AC Compressor & A/C ...\u001b[0m\n", - "300 tensor(0.0044, dtype=torch.float16)\n", - "299 tensor(0.0042, dtype=torch.float16)\n", - "350 tensor(0.0040, dtype=torch.float16)\n", - "310 tensor(0.0040, dtype=torch.float16)\n", - "400 tensor(0.0039, dtype=torch.float16)\n", - "329 tensor(0.0039, dtype=torch.float16)\n", - "320 tensor(0.0039, dtype=torch.float16)\n", - "290 tensor(0.0039, dtype=torch.float16)\n", - "270 tensor(0.0039, dtype=torch.float16)\n", - "330 tensor(0.0038, dtype=torch.float16)\n", - "289 tensor(0.0037, dtype=torch.float16)\n", - "360 tensor(0.0037, dtype=torch.float16)\n", - "319 tensor(0.0037, dtype=torch.float16)\n", - "280 tensor(0.0036, dtype=torch.float16)\n", - "335 tensor(0.0036, dtype=torch.float16)\n", - "265 tensor(0.0036, dtype=torch.float16)\n", - "295 tensor(0.0036, dtype=torch.float16)\n", - "306 tensor(0.0035, dtype=torch.float16)\n", - "315 tensor(0.0035, dtype=torch.float16)\n", - "359 tensor(0.0035, dtype=torch.float16)\n", - "\u001b[92m73: Guess: $316.09 Truth: $344.00 Error: $27.91 SLE: 0.01 Item: House Of Troy Pinnac...\u001b[0m\n", - "51 tensor(0.0122, dtype=torch.float16)\n", - "61 tensor(0.0118, dtype=torch.float16)\n", - "71 tensor(0.0115, dtype=torch.float16)\n", - "52 tensor(0.0111, dtype=torch.float16)\n", - "54 tensor(0.0111, dtype=torch.float16)\n", - "42 tensor(0.0111, dtype=torch.float16)\n", - "41 tensor(0.0111, dtype=torch.float16)\n", - "62 tensor(0.0108, dtype=torch.float16)\n", - "63 tensor(0.0108, dtype=torch.float16)\n", - "81 tensor(0.0104, dtype=torch.float16)\n", - "64 tensor(0.0104, dtype=torch.float16)\n", - "48 tensor(0.0101, dtype=torch.float16)\n", - "72 tensor(0.0101, dtype=torch.float16)\n", - "53 tensor(0.0098, dtype=torch.float16)\n", - "44 tensor(0.0098, dtype=torch.float16)\n", - "58 tensor(0.0098, dtype=torch.float16)\n", - "47 tensor(0.0098, dtype=torch.float16)\n", - "57 tensor(0.0095, dtype=torch.float16)\n", - "74 tensor(0.0095, dtype=torch.float16)\n", - "73 tensor(0.0095, dtype=torch.float16)\n", - "\u001b[92m74: Guess: $58.25 Truth: $25.09 Error: $33.16 SLE: 0.67 Item: Juno T29 WH Floating...\u001b[0m\n", - "71 tensor(0.0214, dtype=torch.float16)\n", - "61 tensor(0.0207, dtype=torch.float16)\n", - "81 tensor(0.0201, dtype=torch.float16)\n", - "72 tensor(0.0195, dtype=torch.float16)\n", - "73 tensor(0.0189, dtype=torch.float16)\n", - "77 tensor(0.0172, dtype=torch.float16)\n", - "62 tensor(0.0172, dtype=torch.float16)\n", - "74 tensor(0.0172, dtype=torch.float16)\n", - "91 tensor(0.0172, dtype=torch.float16)\n", - "63 tensor(0.0166, dtype=torch.float16)\n", - "67 tensor(0.0161, dtype=torch.float16)\n", - "66 tensor(0.0156, dtype=torch.float16)\n", - "64 tensor(0.0156, dtype=torch.float16)\n", - "87 tensor(0.0152, dtype=torch.float16)\n", - "94 tensor(0.0142, dtype=torch.float16)\n", - "51 tensor(0.0142, dtype=torch.float16)\n", - "82 tensor(0.0142, dtype=torch.float16)\n", - "83 tensor(0.0142, dtype=torch.float16)\n", - "68 tensor(0.0138, dtype=torch.float16)\n", - "54 tensor(0.0138, dtype=torch.float16)\n", - "\u001b[91m75: Guess: $72.04 Truth: $175.95 Error: $103.91 SLE: 0.78 Item: Sherman GO-PARTS - f...\u001b[0m\n", - "300 tensor(0.0452, dtype=torch.float16)\n", - "400 tensor(0.0301, dtype=torch.float16)\n", - "250 tensor(0.0283, dtype=torch.float16)\n", - "350 tensor(0.0220, dtype=torch.float16)\n", - "280 tensor(0.0188, dtype=torch.float16)\n", - "240 tensor(0.0142, dtype=torch.float16)\n", - "270 tensor(0.0142, dtype=torch.float16)\n", - "330 tensor(0.0138, dtype=torch.float16)\n", - "200 tensor(0.0130, dtype=torch.float16)\n", - "230 tensor(0.0126, dtype=torch.float16)\n", - "299 tensor(0.0126, dtype=torch.float16)\n", - "500 tensor(0.0122, dtype=torch.float16)\n", - "260 tensor(0.0122, dtype=torch.float16)\n", - "450 tensor(0.0118, dtype=torch.float16)\n", - "220 tensor(0.0111, dtype=torch.float16)\n", - "290 tensor(0.0111, dtype=torch.float16)\n", - "320 tensor(0.0111, dtype=torch.float16)\n", - "380 tensor(0.0092, dtype=torch.float16)\n", - "340 tensor(0.0089, dtype=torch.float16)\n", - "210 tensor(0.0084, dtype=torch.float16)\n", - "\u001b[91m76: Guess: $307.95 Truth: $132.64 Error: $175.31 SLE: 0.70 Item: Roland RPU-3 Electro...\u001b[0m\n", - "300 tensor(0.0339, dtype=torch.float16)\n", - "400 tensor(0.0299, dtype=torch.float16)\n", - "250 tensor(0.0206, dtype=torch.float16)\n", - "350 tensor(0.0206, dtype=torch.float16)\n", - "330 tensor(0.0160, dtype=torch.float16)\n", - "270 tensor(0.0155, dtype=torch.float16)\n", - "280 tensor(0.0155, dtype=torch.float16)\n", - "260 tensor(0.0151, dtype=torch.float16)\n", - "240 tensor(0.0151, dtype=torch.float16)\n", - "290 tensor(0.0141, dtype=torch.float16)\n", - "500 tensor(0.0141, dtype=torch.float16)\n", - "320 tensor(0.0129, dtype=torch.float16)\n", - "450 tensor(0.0125, dtype=torch.float16)\n", - "360 tensor(0.0121, dtype=torch.float16)\n", - "380 tensor(0.0121, dtype=torch.float16)\n", - "340 tensor(0.0121, dtype=torch.float16)\n", - "370 tensor(0.0110, dtype=torch.float16)\n", - "390 tensor(0.0103, dtype=torch.float16)\n", - "310 tensor(0.0094, dtype=torch.float16)\n", - "600 tensor(0.0083, dtype=torch.float16)\n", - "\u001b[92m77: Guess: $340.08 Truth: $422.99 Error: $82.91 SLE: 0.05 Item: Rockland VMI14 12,00...\u001b[0m\n", - "147 tensor(0.0262, dtype=torch.float16)\n", - "141 tensor(0.0204, dtype=torch.float16)\n", - "151 tensor(0.0204, dtype=torch.float16)\n", - "154 tensor(0.0192, dtype=torch.float16)\n", - "153 tensor(0.0186, dtype=torch.float16)\n", - "157 tensor(0.0186, dtype=torch.float16)\n", - "152 tensor(0.0186, dtype=torch.float16)\n", - "142 tensor(0.0175, dtype=torch.float16)\n", - "144 tensor(0.0175, dtype=torch.float16)\n", - "163 tensor(0.0169, dtype=torch.float16)\n", - "132 tensor(0.0159, dtype=torch.float16)\n", - "148 tensor(0.0159, dtype=torch.float16)\n", - "131 tensor(0.0159, dtype=torch.float16)\n", - "162 tensor(0.0159, dtype=torch.float16)\n", - "156 tensor(0.0154, dtype=torch.float16)\n", - "161 tensor(0.0149, dtype=torch.float16)\n", - "172 tensor(0.0149, dtype=torch.float16)\n", - "158 tensor(0.0145, dtype=torch.float16)\n", - "164 tensor(0.0140, dtype=torch.float16)\n", - "143 tensor(0.0140, dtype=torch.float16)\n", - "\u001b[92m78: Guess: $151.17 Truth: $146.48 Error: $4.69 SLE: 0.00 Item: Max Advanced Brakes ...\u001b[0m\n", - "151 tensor(0.0203, dtype=torch.float16)\n", - "154 tensor(0.0203, dtype=torch.float16)\n", - "153 tensor(0.0191, dtype=torch.float16)\n", - "147 tensor(0.0191, dtype=torch.float16)\n", - "141 tensor(0.0185, dtype=torch.float16)\n", - "152 tensor(0.0185, dtype=torch.float16)\n", - "142 tensor(0.0168, dtype=torch.float16)\n", - "157 tensor(0.0168, dtype=torch.float16)\n", - "131 tensor(0.0168, dtype=torch.float16)\n", - "156 tensor(0.0158, dtype=torch.float16)\n", - "132 tensor(0.0153, dtype=torch.float16)\n", - "163 tensor(0.0149, dtype=torch.float16)\n", - "134 tensor(0.0149, dtype=torch.float16)\n", - "162 tensor(0.0144, dtype=torch.float16)\n", - "171 tensor(0.0144, dtype=torch.float16)\n", - "164 tensor(0.0140, dtype=torch.float16)\n", - "161 tensor(0.0140, dtype=torch.float16)\n", - "148 tensor(0.0140, dtype=torch.float16)\n", - "144 tensor(0.0135, dtype=torch.float16)\n", - "172 tensor(0.0131, dtype=torch.float16)\n", - "\u001b[92m79: Guess: $151.27 Truth: $156.83 Error: $5.56 SLE: 0.00 Item: Quality-Built 11030 ...\u001b[0m\n", - "150 tensor(0.0221, dtype=torch.float16)\n", - "160 tensor(0.0195, dtype=torch.float16)\n", - "130 tensor(0.0195, dtype=torch.float16)\n", - "180 tensor(0.0189, dtype=torch.float16)\n", - "120 tensor(0.0189, dtype=torch.float16)\n", - "170 tensor(0.0189, dtype=torch.float16)\n", - "250 tensor(0.0189, dtype=torch.float16)\n", - "140 tensor(0.0183, dtype=torch.float16)\n", - "200 tensor(0.0147, dtype=torch.float16)\n", - "110 tensor(0.0147, dtype=torch.float16)\n", - "190 tensor(0.0138, dtype=torch.float16)\n", - "100 tensor(0.0138, dtype=torch.float16)\n", - "135 tensor(0.0126, dtype=torch.float16)\n", - "175 tensor(0.0111, dtype=torch.float16)\n", - "145 tensor(0.0111, dtype=torch.float16)\n", - "115 tensor(0.0111, dtype=torch.float16)\n", - "300 tensor(0.0104, dtype=torch.float16)\n", - "169 tensor(0.0104, dtype=torch.float16)\n", - "149 tensor(0.0104, dtype=torch.float16)\n", - "210 tensor(0.0101, dtype=torch.float16)\n", - "\u001b[93m80: Guess: $163.04 Truth: $251.99 Error: $88.95 SLE: 0.19 Item: Lucida LG-510 Studen...\u001b[0m\n", - "141 tensor(0.0096, dtype=torch.float16)\n", - "131 tensor(0.0096, dtype=torch.float16)\n", - "142 tensor(0.0090, dtype=torch.float16)\n", - "157 tensor(0.0090, dtype=torch.float16)\n", - "132 tensor(0.0087, dtype=torch.float16)\n", - "122 tensor(0.0087, dtype=torch.float16)\n", - "152 tensor(0.0087, dtype=torch.float16)\n", - "123 tensor(0.0085, dtype=torch.float16)\n", - "153 tensor(0.0082, dtype=torch.float16)\n", - "121 tensor(0.0082, dtype=torch.float16)\n", - "172 tensor(0.0082, dtype=torch.float16)\n", - "127 tensor(0.0080, dtype=torch.float16)\n", - "147 tensor(0.0080, dtype=torch.float16)\n", - "161 tensor(0.0077, dtype=torch.float16)\n", - "148 tensor(0.0077, dtype=torch.float16)\n", - "171 tensor(0.0077, dtype=torch.float16)\n", - "151 tensor(0.0075, dtype=torch.float16)\n", - "162 tensor(0.0075, dtype=torch.float16)\n", - "144 tensor(0.0075, dtype=torch.float16)\n", - "154 tensor(0.0075, dtype=torch.float16)\n", - "\u001b[91m81: Guess: $145.06 Truth: $940.33 Error: $795.27 SLE: 3.47 Item: Longacre Aluminum Tu...\u001b[0m\n", - "61 tensor(0.0157, dtype=torch.float16)\n", - "71 tensor(0.0152, dtype=torch.float16)\n", - "81 tensor(0.0143, dtype=torch.float16)\n", - "51 tensor(0.0143, dtype=torch.float16)\n", - "72 tensor(0.0134, dtype=torch.float16)\n", - "52 tensor(0.0134, dtype=torch.float16)\n", - "91 tensor(0.0130, dtype=torch.float16)\n", - "62 tensor(0.0122, dtype=torch.float16)\n", - "41 tensor(0.0122, dtype=torch.float16)\n", - "74 tensor(0.0118, dtype=torch.float16)\n", - "63 tensor(0.0115, dtype=torch.float16)\n", - "64 tensor(0.0115, dtype=torch.float16)\n", - "92 tensor(0.0111, dtype=torch.float16)\n", - "73 tensor(0.0111, dtype=torch.float16)\n", - "67 tensor(0.0111, dtype=torch.float16)\n", - "77 tensor(0.0111, dtype=torch.float16)\n", - "82 tensor(0.0111, dtype=torch.float16)\n", - "54 tensor(0.0111, dtype=torch.float16)\n", - "57 tensor(0.0108, dtype=torch.float16)\n", - "78 tensor(0.0108, dtype=torch.float16)\n", - "\u001b[92m82: Guess: $67.93 Truth: $52.99 Error: $14.94 SLE: 0.06 Item: Motion Pro Adjustabl...\u001b[0m\n", - "250 tensor(0.0107, dtype=torch.float16)\n", - "300 tensor(0.0083, dtype=torch.float16)\n", - "149 tensor(0.0083, dtype=torch.float16)\n", - "144 tensor(0.0076, dtype=torch.float16)\n", - "139 tensor(0.0074, dtype=torch.float16)\n", - "99 tensor(0.0071, dtype=torch.float16)\n", - "134 tensor(0.0069, dtype=torch.float16)\n", - "148 tensor(0.0069, dtype=torch.float16)\n", - "129 tensor(0.0067, dtype=torch.float16)\n", - "124 tensor(0.0067, dtype=torch.float16)\n", - "150 tensor(0.0065, dtype=torch.float16)\n", - "146 tensor(0.0065, dtype=torch.float16)\n", - "114 tensor(0.0065, dtype=torch.float16)\n", - "132 tensor(0.0065, dtype=torch.float16)\n", - "138 tensor(0.0065, dtype=torch.float16)\n", - "135 tensor(0.0063, dtype=torch.float16)\n", - "168 tensor(0.0063, dtype=torch.float16)\n", - "155 tensor(0.0063, dtype=torch.float16)\n", - "145 tensor(0.0061, dtype=torch.float16)\n", - "240 tensor(0.0061, dtype=torch.float16)\n", - "\u001b[93m83: Guess: $160.68 Truth: $219.95 Error: $59.27 SLE: 0.10 Item: Glyph Thunderbolt 3 ...\u001b[0m\n", - "300 tensor(0.0082, dtype=torch.float16)\n", - "299 tensor(0.0074, dtype=torch.float16)\n", - "270 tensor(0.0069, dtype=torch.float16)\n", - "289 tensor(0.0068, dtype=torch.float16)\n", - "330 tensor(0.0066, dtype=torch.float16)\n", - "290 tensor(0.0064, dtype=torch.float16)\n", - "315 tensor(0.0061, dtype=torch.float16)\n", - "265 tensor(0.0060, dtype=torch.float16)\n", - "329 tensor(0.0059, dtype=torch.float16)\n", - "319 tensor(0.0059, dtype=torch.float16)\n", - "292 tensor(0.0058, dtype=torch.float16)\n", - "293 tensor(0.0058, dtype=torch.float16)\n", - "310 tensor(0.0058, dtype=torch.float16)\n", - "325 tensor(0.0057, dtype=torch.float16)\n", - "350 tensor(0.0057, dtype=torch.float16)\n", - "295 tensor(0.0057, dtype=torch.float16)\n", - "288 tensor(0.0056, dtype=torch.float16)\n", - "320 tensor(0.0056, dtype=torch.float16)\n", - "280 tensor(0.0056, dtype=torch.float16)\n", - "305 tensor(0.0056, dtype=torch.float16)\n", - "\u001b[93m84: Guess: $302.72 Truth: $441.03 Error: $138.31 SLE: 0.14 Item: TOYO Open Country MT...\u001b[0m\n", - "150 tensor(0.0349, dtype=torch.float16)\n", - "130 tensor(0.0255, dtype=torch.float16)\n", - "100 tensor(0.0240, dtype=torch.float16)\n", - "120 tensor(0.0225, dtype=torch.float16)\n", - "140 tensor(0.0218, dtype=torch.float16)\n", - "160 tensor(0.0192, dtype=torch.float16)\n", - "200 tensor(0.0170, dtype=torch.float16)\n", - "170 tensor(0.0155, dtype=torch.float16)\n", - "180 tensor(0.0141, dtype=torch.float16)\n", - "125 tensor(0.0141, dtype=torch.float16)\n", - "145 tensor(0.0136, dtype=torch.float16)\n", - "110 tensor(0.0128, dtype=torch.float16)\n", - "135 tensor(0.0128, dtype=torch.float16)\n", - "115 tensor(0.0128, dtype=torch.float16)\n", - "149 tensor(0.0117, dtype=torch.float16)\n", - "129 tensor(0.0117, dtype=torch.float16)\n", - "128 tensor(0.0110, dtype=torch.float16)\n", - "165 tensor(0.0100, dtype=torch.float16)\n", - "139 tensor(0.0097, dtype=torch.float16)\n", - "132 tensor(0.0097, dtype=torch.float16)\n", - "\u001b[92m85: Guess: $140.65 Truth: $168.98 Error: $28.33 SLE: 0.03 Item: Razer Seiren X USB S...\u001b[0m\n", - "4 tensor(0.2852, dtype=torch.float16)\n", - "3 tensor(0.2291, dtype=torch.float16)\n", - "5 tensor(0.1625, dtype=torch.float16)\n", - "2 tensor(0.0985, dtype=torch.float16)\n", - "6 tensor(0.0721, dtype=torch.float16)\n", - "7 tensor(0.0386, dtype=torch.float16)\n", - "8 tensor(0.0227, dtype=torch.float16)\n", - "1 tensor(0.0213, dtype=torch.float16)\n", - "9 tensor(0.0146, dtype=torch.float16)\n", - "10 tensor(0.0086, dtype=torch.float16)\n", - "11 tensor(0.0057, dtype=torch.float16)\n", - "12 tensor(0.0054, dtype=torch.float16)\n", - "14 tensor(0.0037, dtype=torch.float16)\n", - "13 tensor(0.0036, dtype=torch.float16)\n", - "15 tensor(0.0027, dtype=torch.float16)\n", - "16 tensor(0.0019, dtype=torch.float16)\n", - "18 tensor(0.0016, dtype=torch.float16)\n", - "17 tensor(0.0015, dtype=torch.float16)\n", - "19 tensor(0.0013, dtype=torch.float16)\n", - "20 tensor(0.0011, dtype=torch.float16)\n", - "\u001b[92m86: Guess: $4.44 Truth: $2.49 Error: $1.95 SLE: 0.20 Item: Happy Birthday to Da...\u001b[0m\n", - "100 tensor(0.0188, dtype=torch.float16)\n", - "90 tensor(0.0161, dtype=torch.float16)\n", - "80 tensor(0.0161, dtype=torch.float16)\n", - "110 tensor(0.0146, dtype=torch.float16)\n", - "85 tensor(0.0142, dtype=torch.float16)\n", - "95 tensor(0.0137, dtype=torch.float16)\n", - "75 tensor(0.0137, dtype=torch.float16)\n", - "70 tensor(0.0129, dtype=torch.float16)\n", - "120 tensor(0.0129, dtype=torch.float16)\n", - "99 tensor(0.0125, dtype=torch.float16)\n", - "65 tensor(0.0118, dtype=torch.float16)\n", - "98 tensor(0.0118, dtype=torch.float16)\n", - "130 tensor(0.0118, dtype=torch.float16)\n", - "115 tensor(0.0110, dtype=torch.float16)\n", - "60 tensor(0.0107, dtype=torch.float16)\n", - "87 tensor(0.0107, dtype=torch.float16)\n", - "105 tensor(0.0107, dtype=torch.float16)\n", - "97 tensor(0.0104, dtype=torch.float16)\n", - "88 tensor(0.0098, dtype=torch.float16)\n", - "150 tensor(0.0098, dtype=torch.float16)\n", - "\u001b[92m87: Guess: $95.30 Truth: $98.62 Error: $3.32 SLE: 0.00 Item: Little Tikes My Real...\u001b[0m\n", - "250 tensor(0.0213, dtype=torch.float16)\n", - "300 tensor(0.0213, dtype=torch.float16)\n", - "299 tensor(0.0166, dtype=torch.float16)\n", - "350 tensor(0.0155, dtype=torch.float16)\n", - "249 tensor(0.0142, dtype=torch.float16)\n", - "240 tensor(0.0137, dtype=torch.float16)\n", - "270 tensor(0.0133, dtype=torch.float16)\n", - "400 tensor(0.0121, dtype=torch.float16)\n", - "295 tensor(0.0121, dtype=torch.float16)\n", - "260 tensor(0.0117, dtype=torch.float16)\n", - "225 tensor(0.0114, dtype=torch.float16)\n", - "290 tensor(0.0114, dtype=torch.float16)\n", - "289 tensor(0.0107, dtype=torch.float16)\n", - "195 tensor(0.0107, dtype=torch.float16)\n", - "330 tensor(0.0104, dtype=torch.float16)\n", - "280 tensor(0.0104, dtype=torch.float16)\n", - "265 tensor(0.0100, dtype=torch.float16)\n", - "275 tensor(0.0100, dtype=torch.float16)\n", - "229 tensor(0.0089, dtype=torch.float16)\n", - "320 tensor(0.0089, dtype=torch.float16)\n", - "\u001b[92m88: Guess: $281.43 Truth: $256.95 Error: $24.48 SLE: 0.01 Item: Studio M Peace and H...\u001b[0m\n", - "20 tensor(0.0588, dtype=torch.float16)\n", - "19 tensor(0.0553, dtype=torch.float16)\n", - "18 tensor(0.0553, dtype=torch.float16)\n", - "21 tensor(0.0519, dtype=torch.float16)\n", - "17 tensor(0.0503, dtype=torch.float16)\n", - "16 tensor(0.0488, dtype=torch.float16)\n", - "26 tensor(0.0488, dtype=torch.float16)\n", - "23 tensor(0.0488, dtype=torch.float16)\n", - "22 tensor(0.0488, dtype=torch.float16)\n", - "24 tensor(0.0473, dtype=torch.float16)\n", - "25 tensor(0.0392, dtype=torch.float16)\n", - "27 tensor(0.0357, dtype=torch.float16)\n", - "28 tensor(0.0325, dtype=torch.float16)\n", - "29 tensor(0.0315, dtype=torch.float16)\n", - "14 tensor(0.0287, dtype=torch.float16)\n", - "15 tensor(0.0287, dtype=torch.float16)\n", - "30 tensor(0.0261, dtype=torch.float16)\n", - "13 tensor(0.0238, dtype=torch.float16)\n", - "31 tensor(0.0163, dtype=torch.float16)\n", - "12 tensor(0.0163, dtype=torch.float16)\n", - "\u001b[92m89: Guess: $21.37 Truth: $30.99 Error: $9.62 SLE: 0.13 Item: MyVolts 12V Power Su...\u001b[0m\n", - "500 tensor(0.0270, dtype=torch.float16)\n", - "600 tensor(0.0228, dtype=torch.float16)\n", - "400 tensor(0.0224, dtype=torch.float16)\n", - "450 tensor(0.0169, dtype=torch.float16)\n", - "700 tensor(0.0159, dtype=torch.float16)\n", - "550 tensor(0.0134, dtype=torch.float16)\n", - "499 tensor(0.0130, dtype=torch.float16)\n", - "800 tensor(0.0116, dtype=torch.float16)\n", - "650 tensor(0.0114, dtype=torch.float16)\n", - "599 tensor(0.0107, dtype=torch.float16)\n", - "350 tensor(0.0093, dtype=torch.float16)\n", - "399 tensor(0.0086, dtype=torch.float16)\n", - "699 tensor(0.0082, dtype=torch.float16)\n", - "750 tensor(0.0080, dtype=torch.float16)\n", - "300 tensor(0.0077, dtype=torch.float16)\n", - "480 tensor(0.0075, dtype=torch.float16)\n", - "549 tensor(0.0068, dtype=torch.float16)\n", - "490 tensor(0.0068, dtype=torch.float16)\n", - "900 tensor(0.0067, dtype=torch.float16)\n", - "430 tensor(0.0065, dtype=torch.float16)\n", - "\u001b[92m90: Guess: $547.44 Truth: $569.84 Error: $22.40 SLE: 0.00 Item: Dell Latitude 7212 R...\u001b[0m\n", - "161 tensor(0.0594, dtype=torch.float16)\n", - "159 tensor(0.0421, dtype=torch.float16)\n", - "167 tensor(0.0396, dtype=torch.float16)\n", - "151 tensor(0.0384, dtype=torch.float16)\n", - "154 tensor(0.0349, dtype=torch.float16)\n", - "171 tensor(0.0338, dtype=torch.float16)\n", - "157 tensor(0.0338, dtype=torch.float16)\n", - "163 tensor(0.0281, dtype=torch.float16)\n", - "168 tensor(0.0264, dtype=torch.float16)\n", - "147 tensor(0.0251, dtype=torch.float16)\n", - "173 tensor(0.0225, dtype=torch.float16)\n", - "164 tensor(0.0193, dtype=torch.float16)\n", - "158 tensor(0.0184, dtype=torch.float16)\n", - "169 tensor(0.0178, dtype=torch.float16)\n", - "162 tensor(0.0173, dtype=torch.float16)\n", - "177 tensor(0.0170, dtype=torch.float16)\n", - "155 tensor(0.0143, dtype=torch.float16)\n", - "156 tensor(0.0139, dtype=torch.float16)\n", - "172 tensor(0.0137, dtype=torch.float16)\n", - "181 tensor(0.0133, dtype=torch.float16)\n", - "\u001b[92m91: Guess: $162.10 Truth: $177.99 Error: $15.89 SLE: 0.01 Item: Covermates Contour F...\u001b[0m\n", - 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"250 tensor(0.0079, dtype=torch.float16)\n", - "240 tensor(0.0064, dtype=torch.float16)\n", - "300 tensor(0.0062, dtype=torch.float16)\n", - "195 tensor(0.0060, dtype=torch.float16)\n", - "165 tensor(0.0058, dtype=torch.float16)\n", - "144 tensor(0.0058, dtype=torch.float16)\n", - "185 tensor(0.0058, dtype=torch.float16)\n", - "225 tensor(0.0056, dtype=torch.float16)\n", - "175 tensor(0.0056, dtype=torch.float16)\n", - "158 tensor(0.0056, dtype=torch.float16)\n", - "164 tensor(0.0056, dtype=torch.float16)\n", - "198 tensor(0.0055, dtype=torch.float16)\n", - "148 tensor(0.0055, dtype=torch.float16)\n", - "168 tensor(0.0053, dtype=torch.float16)\n", - "166 tensor(0.0053, dtype=torch.float16)\n", - "215 tensor(0.0053, dtype=torch.float16)\n", - "178 tensor(0.0053, dtype=torch.float16)\n", - "155 tensor(0.0051, dtype=torch.float16)\n", - "172 tensor(0.0051, dtype=torch.float16)\n", - "184 tensor(0.0051, dtype=torch.float16)\n", - "\u001b[92m93: Guess: $191.64 Truth: $219.00 Error: $27.36 SLE: 0.02 Item: Fieldpiece JL2 Job L...\u001b[0m\n", - "300 tensor(0.0061, dtype=torch.float16)\n", - "250 tensor(0.0053, dtype=torch.float16)\n", - "299 tensor(0.0049, dtype=torch.float16)\n", - "400 tensor(0.0048, dtype=torch.float16)\n", - "270 tensor(0.0045, dtype=torch.float16)\n", - "280 tensor(0.0044, dtype=torch.float16)\n", - "290 tensor(0.0044, dtype=torch.float16)\n", - "240 tensor(0.0044, dtype=torch.float16)\n", - "350 tensor(0.0043, dtype=torch.float16)\n", - "249 tensor(0.0043, dtype=torch.float16)\n", - "260 tensor(0.0043, dtype=torch.float16)\n", - "265 tensor(0.0041, dtype=torch.float16)\n", - "330 tensor(0.0039, dtype=torch.float16)\n", - "275 tensor(0.0039, dtype=torch.float16)\n", - "310 tensor(0.0039, dtype=torch.float16)\n", - "229 tensor(0.0039, dtype=torch.float16)\n", - "289 tensor(0.0038, dtype=torch.float16)\n", - "320 tensor(0.0038, dtype=torch.float16)\n", - "295 tensor(0.0038, dtype=torch.float16)\n", - "238 tensor(0.0037, dtype=torch.float16)\n", - "\u001b[93m94: Guess: $287.60 Truth: $225.55 Error: $62.05 SLE: 0.06 Item: hansgrohe Talis S Mo...\u001b[0m\n", - "999 tensor(0.0321, dtype=torch.float16)\n", - "800 tensor(0.0293, dtype=torch.float16)\n", - "599 tensor(0.0284, dtype=torch.float16)\n", - "900 tensor(0.0258, dtype=torch.float16)\n", - "600 tensor(0.0258, dtype=torch.float16)\n", - "700 tensor(0.0250, dtype=torch.float16)\n", - "699 tensor(0.0250, dtype=torch.float16)\n", - "500 tensor(0.0235, dtype=torch.float16)\n", - "899 tensor(0.0214, dtype=torch.float16)\n", - "499 tensor(0.0207, dtype=torch.float16)\n", - "799 tensor(0.0189, dtype=torch.float16)\n", - "750 tensor(0.0157, dtype=torch.float16)\n", - "400 tensor(0.0157, dtype=torch.float16)\n", - "650 tensor(0.0147, dtype=torch.float16)\n", - "850 tensor(0.0118, dtype=torch.float16)\n", - "450 tensor(0.0106, dtype=torch.float16)\n", - "399 tensor(0.0101, dtype=torch.float16)\n", - "950 tensor(0.0099, dtype=torch.float16)\n", - "649 tensor(0.0096, dtype=torch.float16)\n", - "550 tensor(0.0095, dtype=torch.float16)\n", - "\u001b[91m95: Guess: $703.86 Truth: $495.95 Error: $207.91 SLE: 0.12 Item: G-Technology G-SPEED...\u001b[0m\n", - "900 tensor(0.0381, dtype=torch.float16)\n", - "800 tensor(0.0326, dtype=torch.float16)\n", - "780 tensor(0.0288, dtype=torch.float16)\n", - "840 tensor(0.0266, dtype=torch.float16)\n", - "880 tensor(0.0262, dtype=torch.float16)\n", - "850 tensor(0.0250, dtype=torch.float16)\n", - "820 tensor(0.0231, dtype=torch.float16)\n", - "700 tensor(0.0228, dtype=torch.float16)\n", - "760 tensor(0.0228, dtype=torch.float16)\n", - "860 tensor(0.0221, dtype=torch.float16)\n", - "940 tensor(0.0221, dtype=torch.float16)\n", - "770 tensor(0.0217, dtype=torch.float16)\n", - "740 tensor(0.0214, dtype=torch.float16)\n", - "870 tensor(0.0214, dtype=torch.float16)\n", - "830 tensor(0.0207, dtype=torch.float16)\n", - "790 tensor(0.0207, dtype=torch.float16)\n", - "730 tensor(0.0198, dtype=torch.float16)\n", - "890 tensor(0.0195, dtype=torch.float16)\n", - "970 tensor(0.0195, dtype=torch.float16)\n", - "950 tensor(0.0195, dtype=torch.float16)\n", - "\u001b[92m96: Guess: $833.56 Truth: $942.37 Error: $108.81 SLE: 0.02 Item: DreamLine Shower Doo...\u001b[0m\n", - "72 tensor(0.0101, dtype=torch.float16)\n", - "71 tensor(0.0101, dtype=torch.float16)\n", - "61 tensor(0.0101, dtype=torch.float16)\n", - "52 tensor(0.0095, dtype=torch.float16)\n", - "51 tensor(0.0095, dtype=torch.float16)\n", - "73 tensor(0.0095, dtype=torch.float16)\n", - "62 tensor(0.0095, dtype=torch.float16)\n", - "54 tensor(0.0095, dtype=torch.float16)\n", - "41 tensor(0.0095, dtype=torch.float16)\n", - "63 tensor(0.0095, dtype=torch.float16)\n", - "78 tensor(0.0092, dtype=torch.float16)\n", - "42 tensor(0.0092, dtype=torch.float16)\n", - "64 tensor(0.0092, dtype=torch.float16)\n", - "81 tensor(0.0092, dtype=torch.float16)\n", - "84 tensor(0.0092, dtype=torch.float16)\n", - "74 tensor(0.0089, dtype=torch.float16)\n", - "92 tensor(0.0089, dtype=torch.float16)\n", - "91 tensor(0.0087, dtype=torch.float16)\n", - "87 tensor(0.0087, dtype=torch.float16)\n", - "44 tensor(0.0087, dtype=torch.float16)\n", - "\u001b[93m97: Guess: $66.64 Truth: $1.94 Error: $64.70 SLE: 9.83 Item: Sanctuary Square Bac...\u001b[0m\n", - "300 tensor(0.0108, dtype=torch.float16)\n", - "280 tensor(0.0090, dtype=torch.float16)\n", - "250 tensor(0.0087, dtype=torch.float16)\n", - "299 tensor(0.0082, dtype=torch.float16)\n", - "270 tensor(0.0079, dtype=torch.float16)\n", - "260 tensor(0.0077, dtype=torch.float16)\n", - "290 tensor(0.0077, dtype=torch.float16)\n", - "330 tensor(0.0077, dtype=torch.float16)\n", - "240 tensor(0.0072, dtype=torch.float16)\n", - "289 tensor(0.0070, dtype=torch.float16)\n", - "265 tensor(0.0070, dtype=torch.float16)\n", - "320 tensor(0.0070, dtype=torch.float16)\n", - "350 tensor(0.0070, dtype=torch.float16)\n", - "310 tensor(0.0068, dtype=torch.float16)\n", - "295 tensor(0.0065, dtype=torch.float16)\n", - "275 tensor(0.0064, dtype=torch.float16)\n", - "249 tensor(0.0062, dtype=torch.float16)\n", - "255 tensor(0.0062, dtype=torch.float16)\n", - "269 tensor(0.0060, dtype=torch.float16)\n", - "285 tensor(0.0058, dtype=torch.float16)\n", - "\u001b[92m98: Guess: $284.53 Truth: $284.34 Error: $0.19 SLE: 0.00 Item: Pelican Protector 17...\u001b[0m\n", - "141 tensor(0.0154, dtype=torch.float16)\n", - "131 tensor(0.0144, dtype=torch.float16)\n", - "122 tensor(0.0120, dtype=torch.float16)\n", - "142 tensor(0.0120, dtype=torch.float16)\n", - "132 tensor(0.0120, dtype=torch.float16)\n", - "151 tensor(0.0116, dtype=torch.float16)\n", - "157 tensor(0.0116, dtype=torch.float16)\n", - "152 tensor(0.0116, dtype=torch.float16)\n", - "121 tensor(0.0112, dtype=torch.float16)\n", - "123 tensor(0.0112, dtype=torch.float16)\n", - "147 tensor(0.0112, dtype=torch.float16)\n", - "127 tensor(0.0109, dtype=torch.float16)\n", - "154 tensor(0.0109, dtype=torch.float16)\n", - "137 tensor(0.0099, dtype=torch.float16)\n", - "153 tensor(0.0099, dtype=torch.float16)\n", - "134 tensor(0.0096, dtype=torch.float16)\n", - "148 tensor(0.0096, dtype=torch.float16)\n", - "101 tensor(0.0096, dtype=torch.float16)\n", - "171 tensor(0.0093, dtype=torch.float16)\n", - "144 tensor(0.0093, dtype=torch.float16)\n", - "\u001b[92m99: Guess: $139.18 Truth: $171.90 Error: $32.72 SLE: 0.04 Item: Brock Replacement Dr...\u001b[0m\n", - "169 tensor(0.0221, dtype=torch.float16)\n", - "149 tensor(0.0208, dtype=torch.float16)\n", - "129 tensor(0.0195, dtype=torch.float16)\n", - "179 tensor(0.0189, dtype=torch.float16)\n", - "139 tensor(0.0189, dtype=torch.float16)\n", - "159 tensor(0.0189, dtype=torch.float16)\n", - "130 tensor(0.0177, dtype=torch.float16)\n", - "170 tensor(0.0177, dtype=torch.float16)\n", - "140 tensor(0.0172, dtype=torch.float16)\n", - "160 tensor(0.0172, dtype=torch.float16)\n", - "250 tensor(0.0167, dtype=torch.float16)\n", - "199 tensor(0.0167, dtype=torch.float16)\n", - "119 tensor(0.0157, dtype=torch.float16)\n", - "109 tensor(0.0152, dtype=torch.float16)\n", - "190 tensor(0.0147, dtype=torch.float16)\n", - "150 tensor(0.0147, dtype=torch.float16)\n", - "99 tensor(0.0143, dtype=torch.float16)\n", - "240 tensor(0.0143, dtype=torch.float16)\n", - "180 tensor(0.0138, dtype=torch.float16)\n", - "120 tensor(0.0138, dtype=torch.float16)\n", - "\u001b[92m100: Guess: $158.92 Truth: $144.99 Error: $13.93 SLE: 0.01 Item: Carlinkit Ai Box Min...\u001b[0m\n", - "400 tensor(0.0248, dtype=torch.float16)\n", - "300 tensor(0.0205, dtype=torch.float16)\n", - "500 tensor(0.0193, dtype=torch.float16)\n", - "350 tensor(0.0143, dtype=torch.float16)\n", - "399 tensor(0.0137, dtype=torch.float16)\n", - "600 tensor(0.0129, dtype=torch.float16)\n", - "299 tensor(0.0119, dtype=torch.float16)\n", - "499 tensor(0.0117, dtype=torch.float16)\n", - "450 tensor(0.0115, dtype=torch.float16)\n", - "250 tensor(0.0112, dtype=torch.float16)\n", - "599 tensor(0.0084, dtype=torch.float16)\n", - "700 tensor(0.0083, dtype=torch.float16)\n", - "349 tensor(0.0076, dtype=torch.float16)\n", - "800 tensor(0.0069, dtype=torch.float16)\n", - "550 tensor(0.0068, dtype=torch.float16)\n", - "449 tensor(0.0066, dtype=torch.float16)\n", - "249 tensor(0.0060, dtype=torch.float16)\n", - "330 tensor(0.0057, dtype=torch.float16)\n", - "650 tensor(0.0055, dtype=torch.float16)\n", - "280 tensor(0.0055, dtype=torch.float16)\n", - "\u001b[92m101: Guess: $435.90 Truth: $470.47 Error: $34.57 SLE: 0.01 Item: StarDot YouTube Live...\u001b[0m\n", - "61 tensor(0.0249, dtype=torch.float16)\n", - "62 tensor(0.0226, dtype=torch.float16)\n", - "71 tensor(0.0226, dtype=torch.float16)\n", - "51 tensor(0.0226, dtype=torch.float16)\n", - "63 tensor(0.0226, dtype=torch.float16)\n", - "52 tensor(0.0213, dtype=torch.float16)\n", - "72 tensor(0.0206, dtype=torch.float16)\n", - "64 tensor(0.0206, dtype=torch.float16)\n", - "54 tensor(0.0194, dtype=torch.float16)\n", - "58 tensor(0.0194, dtype=torch.float16)\n", - "67 tensor(0.0188, dtype=torch.float16)\n", - "66 tensor(0.0182, dtype=torch.float16)\n", - "53 tensor(0.0182, dtype=torch.float16)\n", - "57 tensor(0.0182, dtype=torch.float16)\n", - "68 tensor(0.0176, dtype=torch.float16)\n", - "59 tensor(0.0176, dtype=torch.float16)\n", - "56 tensor(0.0171, dtype=torch.float16)\n", - "65 tensor(0.0166, dtype=torch.float16)\n", - "74 tensor(0.0166, dtype=torch.float16)\n", - "73 tensor(0.0161, dtype=torch.float16)\n", - "\u001b[92m102: Guess: $62.11 Truth: $66.95 Error: $4.84 SLE: 0.01 Item: Atomic Compatible ME...\u001b[0m\n", - "92 tensor(0.0087, dtype=torch.float16)\n", - "118 tensor(0.0087, dtype=torch.float16)\n", - "98 tensor(0.0084, dtype=torch.float16)\n", - "121 tensor(0.0084, dtype=torch.float16)\n", - "124 tensor(0.0082, dtype=torch.float16)\n", - "104 tensor(0.0082, dtype=torch.float16)\n", - "91 tensor(0.0082, dtype=torch.float16)\n", - "103 tensor(0.0079, dtype=torch.float16)\n", - "93 tensor(0.0077, dtype=torch.float16)\n", - "122 tensor(0.0077, dtype=torch.float16)\n", - "94 tensor(0.0074, dtype=torch.float16)\n", - "117 tensor(0.0074, dtype=torch.float16)\n", - "116 tensor(0.0074, dtype=torch.float16)\n", - "114 tensor(0.0074, dtype=torch.float16)\n", - "108 tensor(0.0074, dtype=torch.float16)\n", - "102 tensor(0.0072, dtype=torch.float16)\n", - "88 tensor(0.0072, dtype=torch.float16)\n", - "138 tensor(0.0072, dtype=torch.float16)\n", - "97 tensor(0.0072, dtype=torch.float16)\n", - "87 tensor(0.0072, dtype=torch.float16)\n", - "\u001b[92m103: Guess: $106.39 Truth: $117.00 Error: $10.61 SLE: 0.01 Item: Bandai Awakening of ...\u001b[0m\n", - "171 tensor(0.0105, dtype=torch.float16)\n", - "193 tensor(0.0102, dtype=torch.float16)\n", - "162 tensor(0.0099, dtype=torch.float16)\n", - "192 tensor(0.0099, dtype=torch.float16)\n", - "186 tensor(0.0096, dtype=torch.float16)\n", - "163 tensor(0.0096, dtype=torch.float16)\n", - "196 tensor(0.0093, dtype=torch.float16)\n", - "172 tensor(0.0093, dtype=torch.float16)\n", - "184 tensor(0.0090, dtype=torch.float16)\n", - "178 tensor(0.0090, dtype=torch.float16)\n", - "176 tensor(0.0087, dtype=torch.float16)\n", - "173 tensor(0.0087, dtype=torch.float16)\n", - "187 tensor(0.0085, dtype=torch.float16)\n", - "161 tensor(0.0085, dtype=torch.float16)\n", - "203 tensor(0.0085, dtype=torch.float16)\n", - "197 tensor(0.0085, dtype=torch.float16)\n", - "183 tensor(0.0085, dtype=torch.float16)\n", - "198 tensor(0.0085, dtype=torch.float16)\n", - "164 tensor(0.0082, dtype=torch.float16)\n", - "182 tensor(0.0079, dtype=torch.float16)\n", - "\u001b[92m104: Guess: $180.93 Truth: $172.14 Error: $8.79 SLE: 0.00 Item: Fit System 62135G Pa...\u001b[0m\n", - "384 tensor(0.0206, dtype=torch.float16)\n", - "383 tensor(0.0144, dtype=torch.float16)\n", - "387 tensor(0.0135, dtype=torch.float16)\n", - "399 tensor(0.0125, dtype=torch.float16)\n", - "366 tensor(0.0119, dtype=torch.float16)\n", - "373 tensor(0.0117, dtype=torch.float16)\n", - "386 tensor(0.0117, dtype=torch.float16)\n", - "385 tensor(0.0115, dtype=torch.float16)\n", - "379 tensor(0.0114, dtype=torch.float16)\n", - "372 tensor(0.0112, dtype=torch.float16)\n", - "376 tensor(0.0108, dtype=torch.float16)\n", - "374 tensor(0.0100, dtype=torch.float16)\n", - "368 tensor(0.0099, dtype=torch.float16)\n", - "392 tensor(0.0097, dtype=torch.float16)\n", - "388 tensor(0.0097, dtype=torch.float16)\n", - "364 tensor(0.0097, dtype=torch.float16)\n", - "349 tensor(0.0097, dtype=torch.float16)\n", - "382 tensor(0.0094, dtype=torch.float16)\n", - "394 tensor(0.0094, dtype=torch.float16)\n", - "369 tensor(0.0094, dtype=torch.float16)\n", - "\u001b[92m105: Guess: $379.12 Truth: $392.74 Error: $13.62 SLE: 0.00 Item: Black Horse Black Al...\u001b[0m\n", - "14 tensor(0.0416, dtype=torch.float16)\n", - "12 tensor(0.0416, dtype=torch.float16)\n", - "11 tensor(0.0403, dtype=torch.float16)\n", - "16 tensor(0.0391, dtype=torch.float16)\n", - "13 tensor(0.0391, dtype=torch.float16)\n", - "17 tensor(0.0367, dtype=torch.float16)\n", - "18 tensor(0.0367, dtype=torch.float16)\n", - "19 tensor(0.0356, dtype=torch.float16)\n", - "9 tensor(0.0345, dtype=torch.float16)\n", - "15 tensor(0.0334, dtype=torch.float16)\n", - "10 tensor(0.0324, dtype=torch.float16)\n", - "21 tensor(0.0304, dtype=torch.float16)\n", - "8 tensor(0.0304, dtype=torch.float16)\n", - "22 tensor(0.0286, dtype=torch.float16)\n", - "23 tensor(0.0277, dtype=torch.float16)\n", - "20 tensor(0.0268, dtype=torch.float16)\n", - "24 tensor(0.0260, dtype=torch.float16)\n", - "7 tensor(0.0260, dtype=torch.float16)\n", - "6 tensor(0.0237, dtype=torch.float16)\n", - "25 tensor(0.0216, dtype=torch.float16)\n", - "\u001b[92m106: Guess: $15.22 Truth: $16.99 Error: $1.77 SLE: 0.01 Item: Dearsun Twinkle Star...\u001b[0m\n", - "1 tensor(0.3589, dtype=torch.float16)\n", - "2 tensor(0.3269, dtype=torch.float16)\n", - "3 tensor(0.1696, dtype=torch.float16)\n", - "4 tensor(0.0664, dtype=torch.float16)\n", - "5 tensor(0.0268, dtype=torch.float16)\n", - "6 tensor(0.0144, dtype=torch.float16)\n", - "7 tensor(0.0077, dtype=torch.float16)\n", - "8 tensor(0.0050, dtype=torch.float16)\n", - "9 tensor(0.0031, dtype=torch.float16)\n", - "11 tensor(0.0012, dtype=torch.float16)\n", - "10 tensor(0.0012, dtype=torch.float16)\n", - "12 tensor(0.0010, dtype=torch.float16)\n", - "13 tensor(0.0005, dtype=torch.float16)\n", - "14 tensor(0.0005, dtype=torch.float16)\n", - "18 tensor(0.0004, dtype=torch.float16)\n", - "15 tensor(0.0004, dtype=torch.float16)\n", - "16 tensor(0.0004, dtype=torch.float16)\n", - "17 tensor(0.0004, dtype=torch.float16)\n", - "21 tensor(0.0003, dtype=torch.float16)\n", - "19 tensor(0.0003, dtype=torch.float16)\n", - "\u001b[92m107: Guess: $2.25 Truth: $1.34 Error: $0.91 SLE: 0.11 Item: Pokemon - Gallade Sp...\u001b[0m\n", - "250 tensor(0.1081, dtype=torch.float16)\n", - "230 tensor(0.0676, dtype=torch.float16)\n", - "200 tensor(0.0561, dtype=torch.float16)\n", - "300 tensor(0.0527, dtype=torch.float16)\n", - "180 tensor(0.0450, dtype=torch.float16)\n", - "220 tensor(0.0437, dtype=torch.float16)\n", - "240 tensor(0.0398, dtype=torch.float16)\n", - "210 tensor(0.0385, dtype=torch.float16)\n", - "170 tensor(0.0340, dtype=torch.float16)\n", - "150 tensor(0.0330, dtype=torch.float16)\n", - "190 tensor(0.0310, dtype=torch.float16)\n", - "280 tensor(0.0291, dtype=torch.float16)\n", - "270 tensor(0.0273, dtype=torch.float16)\n", - "160 tensor(0.0234, dtype=torch.float16)\n", - "260 tensor(0.0200, dtype=torch.float16)\n", - "350 tensor(0.0200, dtype=torch.float16)\n", - "330 tensor(0.0151, dtype=torch.float16)\n", - "130 tensor(0.0129, dtype=torch.float16)\n", - "140 tensor(0.0125, dtype=torch.float16)\n", - "400 tensor(0.0121, dtype=torch.float16)\n", - "\u001b[93m108: Guess: $230.35 Truth: $349.98 Error: $119.63 SLE: 0.17 Item: Ibanez GIO Series Cl...\u001b[0m\n", - "240 tensor(0.0209, dtype=torch.float16)\n", - "300 tensor(0.0163, dtype=torch.float16)\n", - "250 tensor(0.0158, dtype=torch.float16)\n", - "260 tensor(0.0158, dtype=torch.float16)\n", - "270 tensor(0.0144, dtype=torch.float16)\n", - "280 tensor(0.0139, dtype=torch.float16)\n", - "320 tensor(0.0123, dtype=torch.float16)\n", - "290 tensor(0.0119, dtype=torch.float16)\n", - "330 tensor(0.0109, dtype=torch.float16)\n", - "360 tensor(0.0105, dtype=torch.float16)\n", - "340 tensor(0.0093, dtype=torch.float16)\n", - "350 tensor(0.0085, dtype=torch.float16)\n", - "400 tensor(0.0085, dtype=torch.float16)\n", - "310 tensor(0.0079, dtype=torch.float16)\n", - "255 tensor(0.0077, dtype=torch.float16)\n", - "245 tensor(0.0075, dtype=torch.float16)\n", - "235 tensor(0.0075, dtype=torch.float16)\n", - "265 tensor(0.0072, dtype=torch.float16)\n", - "238 tensor(0.0066, dtype=torch.float16)\n", - "215 tensor(0.0064, dtype=torch.float16)\n", - "\u001b[93m109: Guess: $286.21 Truth: $370.71 Error: $84.50 SLE: 0.07 Item: Set 2 Heavy Duty 12 ...\u001b[0m\n", - "53 tensor(0.0187, dtype=torch.float16)\n", - "44 tensor(0.0181, dtype=torch.float16)\n", - "46 tensor(0.0181, dtype=torch.float16)\n", - "43 tensor(0.0176, dtype=torch.float16)\n", - "40 tensor(0.0176, dtype=torch.float16)\n", - "56 tensor(0.0170, dtype=torch.float16)\n", - "47 tensor(0.0170, dtype=torch.float16)\n", - "55 tensor(0.0170, dtype=torch.float16)\n", - "50 tensor(0.0170, dtype=torch.float16)\n", - "48 tensor(0.0165, dtype=torch.float16)\n", - "49 tensor(0.0165, dtype=torch.float16)\n", - "54 tensor(0.0165, dtype=torch.float16)\n", - "42 tensor(0.0160, dtype=torch.float16)\n", - "57 tensor(0.0160, dtype=torch.float16)\n", - "60 tensor(0.0160, dtype=torch.float16)\n", - "45 tensor(0.0160, dtype=torch.float16)\n", - "38 tensor(0.0155, dtype=torch.float16)\n", - "51 tensor(0.0150, dtype=torch.float16)\n", - "36 tensor(0.0150, dtype=torch.float16)\n", - "41 tensor(0.0150, dtype=torch.float16)\n", - "\u001b[92m110: Guess: $47.82 Truth: $65.88 Error: $18.06 SLE: 0.10 Item: Hairpin Table Legs 2...\u001b[0m\n", - "240 tensor(0.0264, dtype=torch.float16)\n", - "250 tensor(0.0248, dtype=torch.float16)\n", - "300 tensor(0.0225, dtype=torch.float16)\n", - "260 tensor(0.0205, dtype=torch.float16)\n", - "270 tensor(0.0187, dtype=torch.float16)\n", - "280 tensor(0.0176, dtype=torch.float16)\n", - "290 tensor(0.0155, dtype=torch.float16)\n", - "330 tensor(0.0103, dtype=torch.float16)\n", - "249 tensor(0.0097, dtype=torch.float16)\n", - "210 tensor(0.0094, dtype=torch.float16)\n", - "220 tensor(0.0094, dtype=torch.float16)\n", - "239 tensor(0.0094, dtype=torch.float16)\n", - "400 tensor(0.0094, dtype=torch.float16)\n", - "230 tensor(0.0088, dtype=torch.float16)\n", - "320 tensor(0.0088, dtype=torch.float16)\n", - "219 tensor(0.0086, dtype=torch.float16)\n", - "350 tensor(0.0083, dtype=torch.float16)\n", - "186 tensor(0.0083, dtype=torch.float16)\n", - "299 tensor(0.0083, dtype=torch.float16)\n", - "209 tensor(0.0080, dtype=torch.float16)\n", - "\u001b[92m111: Guess: $267.53 Truth: $229.99 Error: $37.54 SLE: 0.02 Item: Marada Racing Seat w...\u001b[0m\n", - "4 tensor(0.1378, dtype=torch.float16)\n", - "3 tensor(0.1255, dtype=torch.float16)\n", - "5 tensor(0.1216, dtype=torch.float16)\n", - "6 tensor(0.1040, dtype=torch.float16)\n", - "7 tensor(0.0862, dtype=torch.float16)\n", - "2 tensor(0.0785, dtype=torch.float16)\n", - "8 tensor(0.0630, dtype=torch.float16)\n", - "9 tensor(0.0476, dtype=torch.float16)\n", - "11 tensor(0.0307, dtype=torch.float16)\n", - "10 tensor(0.0271, dtype=torch.float16)\n", - "12 tensor(0.0205, dtype=torch.float16)\n", - "1 tensor(0.0205, dtype=torch.float16)\n", - "13 tensor(0.0155, dtype=torch.float16)\n", - "14 tensor(0.0136, dtype=torch.float16)\n", - "16 tensor(0.0083, dtype=torch.float16)\n", - "15 tensor(0.0083, dtype=torch.float16)\n", - "17 tensor(0.0066, dtype=torch.float16)\n", - "18 tensor(0.0064, dtype=torch.float16)\n", - "21 tensor(0.0052, dtype=torch.float16)\n", - "19 tensor(0.0052, dtype=torch.float16)\n", - "\u001b[92m112: Guess: $6.24 Truth: $9.14 Error: $2.90 SLE: 0.11 Item: Remington Industries...\u001b[0m\n", - "400 tensor(0.0584, dtype=torch.float16)\n", - "500 tensor(0.0427, dtype=torch.float16)\n", - "499 tensor(0.0365, dtype=torch.float16)\n", - "399 tensor(0.0354, dtype=torch.float16)\n", - "600 tensor(0.0303, dtype=torch.float16)\n", - "599 tensor(0.0284, dtype=torch.float16)\n", - "300 tensor(0.0236, dtype=torch.float16)\n", - "699 tensor(0.0190, dtype=torch.float16)\n", - "350 tensor(0.0184, dtype=torch.float16)\n", - "450 tensor(0.0184, dtype=torch.float16)\n", - "700 tensor(0.0178, dtype=torch.float16)\n", - "299 tensor(0.0167, dtype=torch.float16)\n", - "800 tensor(0.0157, dtype=torch.float16)\n", - "349 tensor(0.0119, dtype=torch.float16)\n", - "449 tensor(0.0115, dtype=torch.float16)\n", - "550 tensor(0.0108, dtype=torch.float16)\n", - "799 tensor(0.0098, dtype=torch.float16)\n", - "549 tensor(0.0092, dtype=torch.float16)\n", - "999 tensor(0.0092, dtype=torch.float16)\n", - "650 tensor(0.0087, dtype=torch.float16)\n", - "\u001b[91m113: Guess: $509.61 Truth: $199.00 Error: $310.61 SLE: 0.88 Item: Acer Ultrabook, Inte...\u001b[0m\n", - "250 tensor(0.0457, dtype=torch.float16)\n", - "300 tensor(0.0443, dtype=torch.float16)\n", - "240 tensor(0.0430, dtype=torch.float16)\n", - "260 tensor(0.0314, dtype=torch.float16)\n", - "270 tensor(0.0305, dtype=torch.float16)\n", - "280 tensor(0.0286, dtype=torch.float16)\n", - "290 tensor(0.0223, dtype=torch.float16)\n", - "160 tensor(0.0158, dtype=torch.float16)\n", - "400 tensor(0.0158, dtype=torch.float16)\n", - "220 tensor(0.0158, dtype=torch.float16)\n", - "190 tensor(0.0158, dtype=torch.float16)\n", - "170 tensor(0.0153, dtype=torch.float16)\n", - "230 tensor(0.0144, dtype=torch.float16)\n", - "200 tensor(0.0144, dtype=torch.float16)\n", - "180 tensor(0.0140, dtype=torch.float16)\n", - "350 tensor(0.0131, dtype=torch.float16)\n", - "330 tensor(0.0123, dtype=torch.float16)\n", - "210 tensor(0.0119, dtype=torch.float16)\n", - "320 tensor(0.0116, dtype=torch.float16)\n", - "150 tensor(0.0102, dtype=torch.float16)\n", - "\u001b[91m114: Guess: $255.59 Truth: $109.99 Error: $145.60 SLE: 0.70 Item: ICBEAMER 7 RGB LED H...\u001b[0m\n", - "376 tensor(0.0056, dtype=torch.float16)\n", - "364 tensor(0.0056, dtype=torch.float16)\n", - "359 tensor(0.0056, dtype=torch.float16)\n", - "368 tensor(0.0054, dtype=torch.float16)\n", - "343 tensor(0.0054, dtype=torch.float16)\n", - "350 tensor(0.0054, dtype=torch.float16)\n", - "360 tensor(0.0054, dtype=torch.float16)\n", - "328 tensor(0.0053, dtype=torch.float16)\n", - "352 tensor(0.0052, dtype=torch.float16)\n", - "356 tensor(0.0052, dtype=torch.float16)\n", - "388 tensor(0.0051, dtype=torch.float16)\n", - "354 tensor(0.0051, dtype=torch.float16)\n", - "341 tensor(0.0051, dtype=torch.float16)\n", - "366 tensor(0.0051, dtype=torch.float16)\n", - "339 tensor(0.0051, dtype=torch.float16)\n", - "386 tensor(0.0051, dtype=torch.float16)\n", - "369 tensor(0.0051, dtype=torch.float16)\n", - "334 tensor(0.0051, dtype=torch.float16)\n", - "327 tensor(0.0051, dtype=torch.float16)\n", - "320 tensor(0.0050, dtype=torch.float16)\n", - "\u001b[93m115: Guess: $354.17 Truth: $570.42 Error: $216.25 SLE: 0.23 Item: R1 Concepts Front Re...\u001b[0m\n", - "300 tensor(0.0666, dtype=torch.float16)\n", - "250 tensor(0.0606, dtype=torch.float16)\n", - "240 tensor(0.0487, dtype=torch.float16)\n", - "260 tensor(0.0457, dtype=torch.float16)\n", - "270 tensor(0.0457, dtype=torch.float16)\n", - "280 tensor(0.0443, dtype=torch.float16)\n", - "290 tensor(0.0305, dtype=torch.float16)\n", - "230 tensor(0.0295, dtype=torch.float16)\n", - "220 tensor(0.0269, dtype=torch.float16)\n", - "330 tensor(0.0245, dtype=torch.float16)\n", - "400 tensor(0.0237, dtype=torch.float16)\n", - "190 tensor(0.0223, dtype=torch.float16)\n", - "200 tensor(0.0216, dtype=torch.float16)\n", - "210 tensor(0.0210, dtype=torch.float16)\n", - "170 tensor(0.0197, dtype=torch.float16)\n", - "180 tensor(0.0179, dtype=torch.float16)\n", - "350 tensor(0.0179, dtype=torch.float16)\n", - "320 tensor(0.0179, dtype=torch.float16)\n", - "160 tensor(0.0153, dtype=torch.float16)\n", - "310 tensor(0.0135, dtype=torch.float16)\n", - "\u001b[92m116: Guess: $261.86 Truth: $279.99 Error: $18.13 SLE: 0.00 Item: Camplux 2.64 GPM Tan...\u001b[0m\n", - "37 tensor(0.0404, dtype=torch.float16)\n", - "36 tensor(0.0392, dtype=torch.float16)\n", - "33 tensor(0.0380, dtype=torch.float16)\n", - "34 tensor(0.0357, dtype=torch.float16)\n", - "38 tensor(0.0346, dtype=torch.float16)\n", - "31 tensor(0.0346, dtype=torch.float16)\n", - "32 tensor(0.0335, dtype=torch.float16)\n", - "43 tensor(0.0335, dtype=torch.float16)\n", - "46 tensor(0.0305, dtype=torch.float16)\n", - "29 tensor(0.0287, dtype=torch.float16)\n", - "41 tensor(0.0287, dtype=torch.float16)\n", - "39 tensor(0.0278, dtype=torch.float16)\n", - "44 tensor(0.0269, dtype=torch.float16)\n", - "47 tensor(0.0261, dtype=torch.float16)\n", - "28 tensor(0.0261, dtype=torch.float16)\n", - "27 tensor(0.0261, dtype=torch.float16)\n", - "35 tensor(0.0261, dtype=torch.float16)\n", - "40 tensor(0.0261, dtype=torch.float16)\n", - "30 tensor(0.0253, dtype=torch.float16)\n", - "26 tensor(0.0253, dtype=torch.float16)\n", - "\u001b[92m117: Guess: $35.86 Truth: $30.99 Error: $4.87 SLE: 0.02 Item: KNOKLOCK 10 Pack 3.7...\u001b[0m\n", - "45 tensor(0.0263, dtype=torch.float16)\n", - "42 tensor(0.0239, dtype=torch.float16)\n", - "40 tensor(0.0232, dtype=torch.float16)\n", - "41 tensor(0.0225, dtype=torch.float16)\n", - "43 tensor(0.0225, dtype=torch.float16)\n", - "44 tensor(0.0225, dtype=torch.float16)\n", - "35 tensor(0.0225, dtype=torch.float16)\n", - "52 tensor(0.0218, dtype=torch.float16)\n", - "38 tensor(0.0211, dtype=torch.float16)\n", - "48 tensor(0.0211, dtype=torch.float16)\n", - "50 tensor(0.0198, dtype=torch.float16)\n", - "34 tensor(0.0192, dtype=torch.float16)\n", - "47 tensor(0.0192, dtype=torch.float16)\n", - "55 tensor(0.0192, dtype=torch.float16)\n", - "51 tensor(0.0186, dtype=torch.float16)\n", - "54 tensor(0.0181, dtype=torch.float16)\n", - "53 tensor(0.0181, dtype=torch.float16)\n", - "49 tensor(0.0181, dtype=torch.float16)\n", - "37 tensor(0.0175, dtype=torch.float16)\n", - "32 tensor(0.0175, dtype=torch.float16)\n", - "\u001b[92m118: Guess: $44.37 Truth: $31.99 Error: $12.38 SLE: 0.10 Item: Valley Enterprises Y...\u001b[0m\n", - "29 tensor(0.0178, dtype=torch.float16)\n", - "26 tensor(0.0172, dtype=torch.float16)\n", - "36 tensor(0.0167, dtype=torch.float16)\n", - "19 tensor(0.0157, dtype=torch.float16)\n", - "24 tensor(0.0152, dtype=torch.float16)\n", - "18 tensor(0.0147, dtype=torch.float16)\n", - "21 tensor(0.0147, dtype=torch.float16)\n", - "39 tensor(0.0147, dtype=torch.float16)\n", - "23 tensor(0.0147, dtype=torch.float16)\n", - "27 tensor(0.0143, dtype=torch.float16)\n", - "16 tensor(0.0143, dtype=torch.float16)\n", - "46 tensor(0.0138, dtype=torch.float16)\n", - "28 tensor(0.0138, dtype=torch.float16)\n", - "30 tensor(0.0138, dtype=torch.float16)\n", - "22 tensor(0.0138, dtype=torch.float16)\n", - "33 tensor(0.0138, dtype=torch.float16)\n", - "20 tensor(0.0138, dtype=torch.float16)\n", - "17 tensor(0.0138, dtype=torch.float16)\n", - "38 tensor(0.0134, dtype=torch.float16)\n", - "37 tensor(0.0130, dtype=torch.float16)\n", - "\u001b[92m119: Guess: $27.38 Truth: $15.90 Error: $11.48 SLE: 0.27 Item: G9 LED Light 100W re...\u001b[0m\n", - "70 tensor(0.0300, dtype=torch.float16)\n", - "86 tensor(0.0291, dtype=torch.float16)\n", - "90 tensor(0.0273, dtype=torch.float16)\n", - "76 tensor(0.0265, dtype=torch.float16)\n", - "66 tensor(0.0241, dtype=torch.float16)\n", - "80 tensor(0.0241, dtype=torch.float16)\n", - "110 tensor(0.0200, dtype=torch.float16)\n", - "60 tensor(0.0193, dtype=torch.float16)\n", - "96 tensor(0.0182, dtype=torch.float16)\n", - "69 tensor(0.0160, dtype=torch.float16)\n", - "100 tensor(0.0155, dtype=torch.float16)\n", - "130 tensor(0.0155, dtype=torch.float16)\n", - "106 tensor(0.0151, dtype=torch.float16)\n", - "56 tensor(0.0146, dtype=torch.float16)\n", - "120 tensor(0.0137, dtype=torch.float16)\n", - "116 tensor(0.0133, dtype=torch.float16)\n", - "126 tensor(0.0129, dtype=torch.float16)\n", - "73 tensor(0.0129, dtype=torch.float16)\n", - "79 tensor(0.0129, dtype=torch.float16)\n", - "99 tensor(0.0125, dtype=torch.float16)\n", - "\u001b[93m120: Guess: $87.81 Truth: $45.99 Error: $41.82 SLE: 0.41 Item: ZCHAOZ 4 Lights Anti...\u001b[0m\n", - "250 tensor(0.0121, dtype=torch.float16)\n", - "300 tensor(0.0091, dtype=torch.float16)\n", - "175 tensor(0.0080, dtype=torch.float16)\n", - "185 tensor(0.0075, dtype=torch.float16)\n", - "200 tensor(0.0071, dtype=torch.float16)\n", - "165 tensor(0.0071, dtype=torch.float16)\n", - "240 tensor(0.0069, dtype=torch.float16)\n", - "148 tensor(0.0069, dtype=torch.float16)\n", - "150 tensor(0.0067, dtype=torch.float16)\n", - "168 tensor(0.0067, dtype=torch.float16)\n", - "155 tensor(0.0067, dtype=torch.float16)\n", - "145 tensor(0.0063, dtype=torch.float16)\n", - "195 tensor(0.0063, dtype=torch.float16)\n", - "220 tensor(0.0063, dtype=torch.float16)\n", - "198 tensor(0.0063, dtype=torch.float16)\n", - "144 tensor(0.0063, dtype=torch.float16)\n", - "230 tensor(0.0061, dtype=torch.float16)\n", - "164 tensor(0.0061, dtype=torch.float16)\n", - "146 tensor(0.0061, dtype=torch.float16)\n", - "180 tensor(0.0061, dtype=torch.float16)\n", - "\u001b[93m121: Guess: $192.33 Truth: $113.52 Error: $78.81 SLE: 0.27 Item: Honeywell Honeywell ...\u001b[0m\n", - "300 tensor(0.0203, dtype=torch.float16)\n", - "400 tensor(0.0194, dtype=torch.float16)\n", - "350 tensor(0.0148, dtype=torch.float16)\n", - "360 tensor(0.0109, dtype=torch.float16)\n", - "330 tensor(0.0107, dtype=torch.float16)\n", - "340 tensor(0.0096, dtype=torch.float16)\n", - "320 tensor(0.0090, dtype=torch.float16)\n", - "290 tensor(0.0090, dtype=torch.float16)\n", - "390 tensor(0.0087, dtype=torch.float16)\n", - "380 tensor(0.0087, dtype=torch.float16)\n", - "270 tensor(0.0086, dtype=torch.float16)\n", - "280 tensor(0.0082, dtype=torch.float16)\n", - "310 tensor(0.0082, dtype=torch.float16)\n", - "450 tensor(0.0079, dtype=torch.float16)\n", - "250 tensor(0.0077, dtype=torch.float16)\n", - "370 tensor(0.0075, dtype=torch.float16)\n", - "315 tensor(0.0068, dtype=torch.float16)\n", - "260 tensor(0.0066, dtype=torch.float16)\n", - "240 tensor(0.0065, dtype=torch.float16)\n", - "500 tensor(0.0059, dtype=torch.float16)\n", - "\u001b[93m122: Guess: $337.07 Truth: $516.99 Error: $179.92 SLE: 0.18 Item: Patriot Exhaust 1-7/...\u001b[0m\n", - "91 tensor(0.0127, dtype=torch.float16)\n", - "81 tensor(0.0123, dtype=torch.float16)\n", - "71 tensor(0.0119, dtype=torch.float16)\n", - "101 tensor(0.0116, dtype=torch.float16)\n", - "121 tensor(0.0109, dtype=torch.float16)\n", - "102 tensor(0.0105, dtype=torch.float16)\n", - "94 tensor(0.0102, dtype=torch.float16)\n", - "103 tensor(0.0102, dtype=torch.float16)\n", - "107 tensor(0.0099, dtype=torch.float16)\n", - "104 tensor(0.0099, dtype=torch.float16)\n", - "84 tensor(0.0096, dtype=torch.float16)\n", - "114 tensor(0.0096, dtype=torch.float16)\n", - "92 tensor(0.0096, dtype=torch.float16)\n", - "74 tensor(0.0096, dtype=torch.float16)\n", - "87 tensor(0.0093, dtype=torch.float16)\n", - "77 tensor(0.0093, dtype=torch.float16)\n", - "79 tensor(0.0093, dtype=torch.float16)\n", - "131 tensor(0.0093, dtype=torch.float16)\n", - "93 tensor(0.0093, dtype=torch.float16)\n", - "97 tensor(0.0093, dtype=torch.float16)\n", - "\u001b[91m123: Guess: $94.93 Truth: $196.99 Error: $102.06 SLE: 0.53 Item: Fitrite Autopart New...\u001b[0m\n", - "41 tensor(0.0103, dtype=torch.float16)\n", - "42 tensor(0.0097, dtype=torch.float16)\n", - "44 tensor(0.0091, dtype=torch.float16)\n", - "34 tensor(0.0091, dtype=torch.float16)\n", - "38 tensor(0.0091, dtype=torch.float16)\n", - "32 tensor(0.0091, dtype=torch.float16)\n", - "43 tensor(0.0091, dtype=torch.float16)\n", - "51 tensor(0.0091, dtype=torch.float16)\n", - "31 tensor(0.0088, dtype=torch.float16)\n", - "61 tensor(0.0088, dtype=torch.float16)\n", - "54 tensor(0.0085, dtype=torch.float16)\n", - "52 tensor(0.0085, dtype=torch.float16)\n", - "24 tensor(0.0085, dtype=torch.float16)\n", - "23 tensor(0.0083, dtype=torch.float16)\n", - "48 tensor(0.0083, dtype=torch.float16)\n", - "47 tensor(0.0083, dtype=torch.float16)\n", - "53 tensor(0.0080, dtype=torch.float16)\n", - "21 tensor(0.0080, dtype=torch.float16)\n", - "63 tensor(0.0080, dtype=torch.float16)\n", - "33 tensor(0.0080, dtype=torch.float16)\n", - "\u001b[92m124: Guess: $41.73 Truth: $46.55 Error: $4.82 SLE: 0.01 Item: Technical Precision ...\u001b[0m\n", - "293 tensor(0.0584, dtype=torch.float16)\n", - "330 tensor(0.0333, dtype=torch.float16)\n", - "370 tensor(0.0294, dtype=torch.float16)\n", - "357 tensor(0.0222, dtype=torch.float16)\n", - "356 tensor(0.0202, dtype=torch.float16)\n", - "329 tensor(0.0190, dtype=torch.float16)\n", - "340 tensor(0.0157, dtype=torch.float16)\n", - "367 tensor(0.0152, dtype=torch.float16)\n", - "366 tensor(0.0148, dtype=torch.float16)\n", - "387 tensor(0.0148, dtype=torch.float16)\n", - "350 tensor(0.0130, dtype=torch.float16)\n", - "310 tensor(0.0115, dtype=torch.float16)\n", - "343 tensor(0.0115, dtype=torch.float16)\n", - "327 tensor(0.0111, dtype=torch.float16)\n", - "331 tensor(0.0108, dtype=torch.float16)\n", - "365 tensor(0.0105, dtype=torch.float16)\n", - "270 tensor(0.0105, dtype=torch.float16)\n", - "371 tensor(0.0101, dtype=torch.float16)\n", - "369 tensor(0.0095, dtype=torch.float16)\n", - "359 tensor(0.0095, dtype=torch.float16)\n", - "\u001b[92m125: Guess: $339.04 Truth: $356.99 Error: $17.95 SLE: 0.00 Item: Covercraft Carhartt ...\u001b[0m\n", - "299 tensor(0.0260, dtype=torch.float16)\n", - "300 tensor(0.0252, dtype=torch.float16)\n", - "400 tensor(0.0237, dtype=torch.float16)\n", - "399 tensor(0.0202, dtype=torch.float16)\n", - "350 tensor(0.0196, dtype=torch.float16)\n", - "349 tensor(0.0184, dtype=torch.float16)\n", - "249 tensor(0.0158, dtype=torch.float16)\n", - "250 tensor(0.0158, dtype=torch.float16)\n", - "329 tensor(0.0108, dtype=torch.float16)\n", - "499 tensor(0.0099, dtype=torch.float16)\n", - "330 tensor(0.0099, dtype=torch.float16)\n", - "450 tensor(0.0093, dtype=torch.float16)\n", - "280 tensor(0.0090, dtype=torch.float16)\n", - "500 tensor(0.0087, dtype=torch.float16)\n", - "279 tensor(0.0084, dtype=torch.float16)\n", - "369 tensor(0.0082, dtype=torch.float16)\n", - "229 tensor(0.0082, dtype=torch.float16)\n", - "379 tensor(0.0077, dtype=torch.float16)\n", - "270 tensor(0.0077, dtype=torch.float16)\n", - "359 tensor(0.0074, dtype=torch.float16)\n", - "\u001b[92m126: Guess: $339.97 Truth: $319.95 Error: $20.02 SLE: 0.00 Item: Sennheiser SD Pro 2 ...\u001b[0m\n", - "101 tensor(0.0140, dtype=torch.float16)\n", - "121 tensor(0.0131, dtype=torch.float16)\n", - "131 tensor(0.0131, dtype=torch.float16)\n", - "141 tensor(0.0127, dtype=torch.float16)\n", - "123 tensor(0.0123, dtype=torch.float16)\n", - "111 tensor(0.0119, dtype=torch.float16)\n", - "91 tensor(0.0119, dtype=torch.float16)\n", - "102 tensor(0.0112, dtype=torch.float16)\n", - "103 tensor(0.0112, dtype=torch.float16)\n", - "122 tensor(0.0112, dtype=torch.float16)\n", - "81 tensor(0.0112, dtype=torch.float16)\n", - "127 tensor(0.0105, dtype=torch.float16)\n", - "132 tensor(0.0102, dtype=torch.float16)\n", - "107 tensor(0.0099, dtype=torch.float16)\n", - "142 tensor(0.0099, dtype=torch.float16)\n", - "94 tensor(0.0096, dtype=torch.float16)\n", - "71 tensor(0.0096, dtype=torch.float16)\n", - "152 tensor(0.0093, dtype=torch.float16)\n", - "151 tensor(0.0093, dtype=torch.float16)\n", - "92 tensor(0.0093, dtype=torch.float16)\n", - "\u001b[92m127: Guess: $114.70 Truth: $96.06 Error: $18.64 SLE: 0.03 Item: Hitachi Mass Air Flo...\u001b[0m\n", - "250 tensor(0.0073, dtype=torch.float16)\n", - "184 tensor(0.0068, dtype=torch.float16)\n", - "240 tensor(0.0064, dtype=torch.float16)\n", - "164 tensor(0.0060, dtype=torch.float16)\n", - "176 tensor(0.0060, dtype=torch.float16)\n", - "186 tensor(0.0060, dtype=torch.float16)\n", - "215 tensor(0.0058, dtype=torch.float16)\n", - "188 tensor(0.0058, dtype=torch.float16)\n", - "168 tensor(0.0058, dtype=torch.float16)\n", - "185 tensor(0.0058, dtype=torch.float16)\n", - "196 tensor(0.0058, dtype=torch.float16)\n", - "178 tensor(0.0058, dtype=torch.float16)\n", - "209 tensor(0.0056, dtype=torch.float16)\n", - "195 tensor(0.0056, dtype=torch.float16)\n", - "166 tensor(0.0056, dtype=torch.float16)\n", - "171 tensor(0.0056, dtype=torch.float16)\n", - "172 tensor(0.0056, dtype=torch.float16)\n", - "198 tensor(0.0056, dtype=torch.float16)\n", - "187 tensor(0.0055, dtype=torch.float16)\n", - "183 tensor(0.0055, dtype=torch.float16)\n", - "\u001b[92m128: Guess: $191.46 Truth: $190.99 Error: $0.47 SLE: 0.00 Item: AmScope LED Cordless...\u001b[0m\n", - "61 tensor(0.0210, dtype=torch.float16)\n", - "51 tensor(0.0203, dtype=torch.float16)\n", - "71 tensor(0.0203, dtype=torch.float16)\n", - "64 tensor(0.0179, dtype=torch.float16)\n", - "52 tensor(0.0179, dtype=torch.float16)\n", - "72 tensor(0.0174, dtype=torch.float16)\n", - "62 tensor(0.0174, dtype=torch.float16)\n", - "63 tensor(0.0174, dtype=torch.float16)\n", - "67 tensor(0.0163, dtype=torch.float16)\n", - "57 tensor(0.0163, dtype=torch.float16)\n", - "74 tensor(0.0163, dtype=torch.float16)\n", - "54 tensor(0.0163, dtype=torch.float16)\n", - "58 tensor(0.0158, dtype=torch.float16)\n", - "66 tensor(0.0158, dtype=torch.float16)\n", - "73 tensor(0.0158, dtype=torch.float16)\n", - "77 tensor(0.0153, dtype=torch.float16)\n", - "81 tensor(0.0153, dtype=torch.float16)\n", - "68 tensor(0.0153, dtype=torch.float16)\n", - "53 tensor(0.0149, dtype=torch.float16)\n", - "56 tensor(0.0149, dtype=torch.float16)\n", - "\u001b[91m129: Guess: $63.82 Truth: $257.95 Error: $194.13 SLE: 1.92 Item: Front Left Driver Si...\u001b[0m\n", - "114 tensor(0.0138, dtype=torch.float16)\n", - "104 tensor(0.0134, dtype=torch.float16)\n", - "113 tensor(0.0130, dtype=torch.float16)\n", - "127 tensor(0.0130, dtype=torch.float16)\n", - "121 tensor(0.0130, dtype=torch.float16)\n", - "117 tensor(0.0126, dtype=torch.float16)\n", - "123 tensor(0.0126, dtype=torch.float16)\n", - "124 tensor(0.0122, dtype=torch.float16)\n", - "107 tensor(0.0122, dtype=torch.float16)\n", - "118 tensor(0.0118, dtype=torch.float16)\n", - "103 tensor(0.0118, dtype=torch.float16)\n", - "112 tensor(0.0118, dtype=torch.float16)\n", - "111 tensor(0.0114, dtype=torch.float16)\n", - "122 tensor(0.0111, dtype=torch.float16)\n", - "116 tensor(0.0111, dtype=torch.float16)\n", - "126 tensor(0.0111, dtype=torch.float16)\n", - "128 tensor(0.0107, dtype=torch.float16)\n", - "144 tensor(0.0104, dtype=torch.float16)\n", - "131 tensor(0.0104, dtype=torch.float16)\n", - "108 tensor(0.0104, dtype=torch.float16)\n", - "\u001b[93m130: Guess: $118.13 Truth: $62.95 Error: $55.18 SLE: 0.39 Item: Premium Replica Hubc...\u001b[0m\n", - "63 tensor(0.0130, dtype=torch.float16)\n", - "54 tensor(0.0130, dtype=torch.float16)\n", - "61 tensor(0.0130, dtype=torch.float16)\n", - "64 tensor(0.0122, dtype=torch.float16)\n", - "71 tensor(0.0118, dtype=torch.float16)\n", - "62 tensor(0.0118, dtype=torch.float16)\n", - "72 tensor(0.0118, dtype=torch.float16)\n", - "48 tensor(0.0118, dtype=torch.float16)\n", - "58 tensor(0.0118, dtype=torch.float16)\n", - "57 tensor(0.0115, dtype=torch.float16)\n", - "44 tensor(0.0115, dtype=torch.float16)\n", - "53 tensor(0.0115, dtype=torch.float16)\n", - "52 tensor(0.0115, dtype=torch.float16)\n", - "42 tensor(0.0115, dtype=torch.float16)\n", - "51 tensor(0.0115, dtype=torch.float16)\n", - "47 tensor(0.0111, dtype=torch.float16)\n", - "49 tensor(0.0111, dtype=torch.float16)\n", - "73 tensor(0.0111, dtype=torch.float16)\n", - "41 tensor(0.0111, dtype=torch.float16)\n", - "43 tensor(0.0108, dtype=torch.float16)\n", - "\u001b[92m131: Guess: $55.46 Truth: $47.66 Error: $7.80 SLE: 0.02 Item: Excellerations Phoni...\u001b[0m\n", - "250 tensor(0.0326, dtype=torch.float16)\n", - "300 tensor(0.0306, dtype=torch.float16)\n", - "240 tensor(0.0246, dtype=torch.float16)\n", - "280 tensor(0.0224, dtype=torch.float16)\n", - "270 tensor(0.0224, dtype=torch.float16)\n", - "350 tensor(0.0204, dtype=torch.float16)\n", - "400 tensor(0.0204, dtype=torch.float16)\n", - "260 tensor(0.0204, dtype=torch.float16)\n", - "290 tensor(0.0164, dtype=torch.float16)\n", - "320 tensor(0.0145, dtype=torch.float16)\n", - "330 tensor(0.0145, dtype=torch.float16)\n", - "310 tensor(0.0116, dtype=torch.float16)\n", - "340 tensor(0.0113, dtype=torch.float16)\n", - "360 tensor(0.0109, dtype=torch.float16)\n", - "210 tensor(0.0106, dtype=torch.float16)\n", - "220 tensor(0.0103, dtype=torch.float16)\n", - "230 tensor(0.0099, dtype=torch.float16)\n", - "370 tensor(0.0091, dtype=torch.float16)\n", - "380 tensor(0.0088, dtype=torch.float16)\n", - "450 tensor(0.0085, dtype=torch.float16)\n", - "\u001b[93m132: Guess: $299.82 Truth: $226.99 Error: $72.83 SLE: 0.08 Item: RC4WD BigDog Dual Ax...\u001b[0m\n", - "250 tensor(0.0202, dtype=torch.float16)\n", - "300 tensor(0.0184, dtype=torch.float16)\n", - "240 tensor(0.0153, dtype=torch.float16)\n", - "270 tensor(0.0139, dtype=torch.float16)\n", - "260 tensor(0.0126, dtype=torch.float16)\n", - "290 tensor(0.0123, dtype=torch.float16)\n", - "400 tensor(0.0123, dtype=torch.float16)\n", - "249 tensor(0.0119, dtype=torch.float16)\n", - "280 tensor(0.0115, dtype=torch.float16)\n", - "350 tensor(0.0108, dtype=torch.float16)\n", - "299 tensor(0.0108, dtype=torch.float16)\n", - "330 tensor(0.0105, dtype=torch.float16)\n", - "239 tensor(0.0093, dtype=torch.float16)\n", - "289 tensor(0.0082, dtype=torch.float16)\n", - "279 tensor(0.0077, dtype=torch.float16)\n", - "259 tensor(0.0077, dtype=torch.float16)\n", - "209 tensor(0.0074, dtype=torch.float16)\n", - "320 tensor(0.0074, dtype=torch.float16)\n", - "399 tensor(0.0072, dtype=torch.float16)\n", - "360 tensor(0.0070, dtype=torch.float16)\n", - "\u001b[92m133: Guess: $289.82 Truth: $359.95 Error: $70.13 SLE: 0.05 Item: Unknown Stage 2 Clut...\u001b[0m\n", - "41 tensor(0.0119, dtype=torch.float16)\n", - "71 tensor(0.0115, dtype=torch.float16)\n", - "61 tensor(0.0115, dtype=torch.float16)\n", - "51 tensor(0.0115, dtype=torch.float16)\n", - "81 tensor(0.0108, dtype=torch.float16)\n", - "52 tensor(0.0105, dtype=torch.float16)\n", - "72 tensor(0.0102, dtype=torch.float16)\n", - "91 tensor(0.0102, dtype=torch.float16)\n", - "74 tensor(0.0099, dtype=torch.float16)\n", - "42 tensor(0.0093, dtype=torch.float16)\n", - "62 tensor(0.0090, dtype=torch.float16)\n", - "47 tensor(0.0090, dtype=torch.float16)\n", - "64 tensor(0.0090, dtype=torch.float16)\n", - "77 tensor(0.0090, dtype=torch.float16)\n", - "94 tensor(0.0090, dtype=torch.float16)\n", - "73 tensor(0.0090, dtype=torch.float16)\n", - "82 tensor(0.0087, dtype=torch.float16)\n", - "44 tensor(0.0087, dtype=torch.float16)\n", - "92 tensor(0.0087, dtype=torch.float16)\n", - "63 tensor(0.0087, dtype=torch.float16)\n", - "\u001b[92m134: Guess: $66.27 Truth: $78.40 Error: $12.13 SLE: 0.03 Item: Dodge Ram 1500 Mopar...\u001b[0m\n", - "154 tensor(0.0331, dtype=torch.float16)\n", - "157 tensor(0.0321, dtype=torch.float16)\n", - "161 tensor(0.0321, dtype=torch.float16)\n", - "163 tensor(0.0311, dtype=torch.float16)\n", - "147 tensor(0.0311, dtype=torch.float16)\n", - "151 tensor(0.0311, dtype=torch.float16)\n", - "162 tensor(0.0292, dtype=torch.float16)\n", - "153 tensor(0.0292, dtype=torch.float16)\n", - "152 tensor(0.0292, dtype=torch.float16)\n", - "164 tensor(0.0274, dtype=torch.float16)\n", - "167 tensor(0.0258, dtype=torch.float16)\n", - "172 tensor(0.0250, dtype=torch.float16)\n", - "171 tensor(0.0235, dtype=torch.float16)\n", - "166 tensor(0.0235, dtype=torch.float16)\n", - "156 tensor(0.0201, dtype=torch.float16)\n", - "158 tensor(0.0194, dtype=torch.float16)\n", - "168 tensor(0.0188, dtype=torch.float16)\n", - "174 tensor(0.0188, dtype=torch.float16)\n", - "173 tensor(0.0183, dtype=torch.float16)\n", - "148 tensor(0.0177, dtype=torch.float16)\n", - "\u001b[92m135: Guess: $160.15 Truth: $172.77 Error: $12.62 SLE: 0.01 Item: Pro Comp Alloys Seri...\u001b[0m\n", - "330 tensor(0.0107, dtype=torch.float16)\n", - "319 tensor(0.0098, dtype=torch.float16)\n", - "320 tensor(0.0095, dtype=torch.float16)\n", - "329 tensor(0.0095, dtype=torch.float16)\n", - "350 tensor(0.0089, dtype=torch.float16)\n", - "299 tensor(0.0089, dtype=torch.float16)\n", - "300 tensor(0.0089, dtype=torch.float16)\n", - "339 tensor(0.0081, dtype=torch.float16)\n", - "315 tensor(0.0081, dtype=torch.float16)\n", - "325 tensor(0.0081, dtype=torch.float16)\n", - "359 tensor(0.0079, dtype=torch.float16)\n", - "328 tensor(0.0079, dtype=torch.float16)\n", - "290 tensor(0.0079, dtype=torch.float16)\n", - "349 tensor(0.0076, dtype=torch.float16)\n", - "340 tensor(0.0074, dtype=torch.float16)\n", - "326 tensor(0.0072, dtype=torch.float16)\n", - "289 tensor(0.0072, dtype=torch.float16)\n", - "327 tensor(0.0072, dtype=torch.float16)\n", - "310 tensor(0.0072, dtype=torch.float16)\n", - "360 tensor(0.0072, dtype=torch.float16)\n", - "\u001b[92m136: Guess: $325.02 Truth: $316.45 Error: $8.57 SLE: 0.00 Item: Detroit Axle - Front...\u001b[0m\n", - "91 tensor(0.0145, dtype=torch.float16)\n", - "81 tensor(0.0140, dtype=torch.float16)\n", - "103 tensor(0.0132, dtype=torch.float16)\n", - "71 tensor(0.0132, dtype=torch.float16)\n", - "87 tensor(0.0128, dtype=torch.float16)\n", - "94 tensor(0.0124, dtype=torch.float16)\n", - "72 tensor(0.0120, dtype=torch.float16)\n", - "73 tensor(0.0120, dtype=torch.float16)\n", - "92 tensor(0.0120, dtype=torch.float16)\n", - "83 tensor(0.0116, dtype=torch.float16)\n", - "82 tensor(0.0116, dtype=torch.float16)\n", - "101 tensor(0.0116, dtype=torch.float16)\n", - "121 tensor(0.0113, dtype=torch.float16)\n", - "93 tensor(0.0113, dtype=torch.float16)\n", - "77 tensor(0.0113, dtype=torch.float16)\n", - "107 tensor(0.0113, dtype=torch.float16)\n", - "102 tensor(0.0113, dtype=torch.float16)\n", - "122 tensor(0.0109, dtype=torch.float16)\n", - "88 tensor(0.0109, dtype=torch.float16)\n", - "97 tensor(0.0106, dtype=torch.float16)\n", - "\u001b[92m137: Guess: $91.43 Truth: $87.99 Error: $3.44 SLE: 0.00 Item: ECCPP Rear Wheel Axl...\u001b[0m\n", - "250 tensor(0.0137, dtype=torch.float16)\n", - "300 tensor(0.0114, dtype=torch.float16)\n", - "240 tensor(0.0094, dtype=torch.float16)\n", - "299 tensor(0.0094, dtype=torch.float16)\n", - "249 tensor(0.0089, dtype=torch.float16)\n", - "198 tensor(0.0086, dtype=torch.float16)\n", - "229 tensor(0.0086, dtype=torch.float16)\n", - "209 tensor(0.0083, dtype=torch.float16)\n", - "225 tensor(0.0083, dtype=torch.float16)\n", - "219 tensor(0.0083, dtype=torch.float16)\n", - "215 tensor(0.0081, dtype=torch.float16)\n", - "195 tensor(0.0081, dtype=torch.float16)\n", - "239 tensor(0.0076, dtype=torch.float16)\n", - "199 tensor(0.0076, dtype=torch.float16)\n", - "235 tensor(0.0073, dtype=torch.float16)\n", - "205 tensor(0.0073, dtype=torch.float16)\n", - "220 tensor(0.0073, dtype=torch.float16)\n", - "270 tensor(0.0071, dtype=torch.float16)\n", - "189 tensor(0.0069, dtype=torch.float16)\n", - "194 tensor(0.0069, dtype=torch.float16)\n", - "\u001b[92m138: Guess: $232.10 Truth: $226.63 Error: $5.47 SLE: 0.00 Item: Dell Latitude E6520 ...\u001b[0m\n", - "21 tensor(0.0360, dtype=torch.float16)\n", - "31 tensor(0.0328, dtype=torch.float16)\n", - "23 tensor(0.0318, dtype=torch.float16)\n", - "22 tensor(0.0318, dtype=torch.float16)\n", - "24 tensor(0.0299, dtype=torch.float16)\n", - "27 tensor(0.0289, dtype=torch.float16)\n", - "26 tensor(0.0281, dtype=torch.float16)\n", - "28 tensor(0.0281, dtype=torch.float16)\n", - "32 tensor(0.0264, dtype=torch.float16)\n", - "34 tensor(0.0264, dtype=torch.float16)\n", - "29 tensor(0.0248, dtype=torch.float16)\n", - "18 tensor(0.0240, dtype=torch.float16)\n", - "41 tensor(0.0225, dtype=torch.float16)\n", - "17 tensor(0.0225, dtype=torch.float16)\n", - "33 tensor(0.0225, dtype=torch.float16)\n", - "19 tensor(0.0219, dtype=torch.float16)\n", - "25 tensor(0.0212, dtype=torch.float16)\n", - "16 tensor(0.0193, dtype=torch.float16)\n", - "37 tensor(0.0193, dtype=torch.float16)\n", - "14 tensor(0.0187, dtype=torch.float16)\n", - "\u001b[92m139: Guess: $25.91 Truth: $31.49 Error: $5.58 SLE: 0.04 Item: F FIERCE CYCLE 251pc...\u001b[0m\n", - "240 tensor(0.0084, dtype=torch.float16)\n", - "184 tensor(0.0076, dtype=torch.float16)\n", - "196 tensor(0.0069, dtype=torch.float16)\n", - "204 tensor(0.0067, dtype=torch.float16)\n", - "198 tensor(0.0067, dtype=torch.float16)\n", - "182 tensor(0.0065, dtype=torch.float16)\n", - "192 tensor(0.0065, dtype=torch.float16)\n", - "188 tensor(0.0065, dtype=torch.float16)\n", - "164 tensor(0.0065, dtype=torch.float16)\n", - "209 tensor(0.0065, dtype=torch.float16)\n", - "250 tensor(0.0063, dtype=torch.float16)\n", - "168 tensor(0.0063, dtype=torch.float16)\n", - "215 tensor(0.0063, dtype=torch.float16)\n", - "207 tensor(0.0063, dtype=torch.float16)\n", - "206 tensor(0.0063, dtype=torch.float16)\n", - "172 tensor(0.0061, dtype=torch.float16)\n", - "186 tensor(0.0061, dtype=torch.float16)\n", - "203 tensor(0.0061, dtype=torch.float16)\n", - "193 tensor(0.0061, dtype=torch.float16)\n", - "178 tensor(0.0061, dtype=torch.float16)\n", - "\u001b[92m140: Guess: $197.33 Truth: $196.00 Error: $1.33 SLE: 0.00 Item: Flash Furniture 4 Pk...\u001b[0m\n", - "81 tensor(0.0132, dtype=torch.float16)\n", - "91 tensor(0.0124, dtype=torch.float16)\n", - "101 tensor(0.0124, dtype=torch.float16)\n", - "121 tensor(0.0120, dtype=torch.float16)\n", - "141 tensor(0.0120, dtype=torch.float16)\n", - "123 tensor(0.0117, dtype=torch.float16)\n", - "71 tensor(0.0117, dtype=torch.float16)\n", - "131 tensor(0.0117, dtype=torch.float16)\n", - "102 tensor(0.0113, dtype=torch.float16)\n", - "103 tensor(0.0113, dtype=torch.float16)\n", - "94 tensor(0.0113, dtype=torch.float16)\n", - "122 tensor(0.0113, dtype=torch.float16)\n", - "74 tensor(0.0106, dtype=torch.float16)\n", - "111 tensor(0.0106, dtype=torch.float16)\n", - "72 tensor(0.0106, dtype=torch.float16)\n", - "92 tensor(0.0103, dtype=torch.float16)\n", - "132 tensor(0.0100, dtype=torch.float16)\n", - "82 tensor(0.0100, dtype=torch.float16)\n", - "77 tensor(0.0100, dtype=torch.float16)\n", - "127 tensor(0.0097, dtype=torch.float16)\n", - "\u001b[92m141: Guess: $102.49 Truth: $78.40 Error: $24.09 SLE: 0.07 Item: B&M 30287 Throttle V...\u001b[0m\n", - "141 tensor(0.0154, dtype=torch.float16)\n", - "131 tensor(0.0131, dtype=torch.float16)\n", - "142 tensor(0.0123, dtype=torch.float16)\n", - "123 tensor(0.0123, dtype=torch.float16)\n", - "157 tensor(0.0116, dtype=torch.float16)\n", - "121 tensor(0.0112, dtype=torch.float16)\n", - "147 tensor(0.0112, dtype=torch.float16)\n", - "122 tensor(0.0109, dtype=torch.float16)\n", - "152 tensor(0.0109, dtype=torch.float16)\n", - "151 tensor(0.0109, dtype=torch.float16)\n", - "132 tensor(0.0106, dtype=torch.float16)\n", - "127 tensor(0.0102, dtype=torch.float16)\n", - "153 tensor(0.0102, dtype=torch.float16)\n", - "163 tensor(0.0102, dtype=torch.float16)\n", - "154 tensor(0.0099, dtype=torch.float16)\n", - "161 tensor(0.0093, dtype=torch.float16)\n", - "171 tensor(0.0093, dtype=torch.float16)\n", - "172 tensor(0.0090, dtype=torch.float16)\n", - "164 tensor(0.0090, dtype=torch.float16)\n", - "144 tensor(0.0090, dtype=torch.float16)\n", - "\u001b[92m142: Guess: $145.28 Truth: $116.25 Error: $29.03 SLE: 0.05 Item: Gates TCK226 PowerGr...\u001b[0m\n", - "141 tensor(0.0320, dtype=torch.float16)\n", - "147 tensor(0.0265, dtype=torch.float16)\n", - "142 tensor(0.0241, dtype=torch.float16)\n", - "131 tensor(0.0241, dtype=torch.float16)\n", - "122 tensor(0.0227, dtype=torch.float16)\n", - "132 tensor(0.0227, dtype=torch.float16)\n", - "123 tensor(0.0227, dtype=torch.float16)\n", - "121 tensor(0.0220, dtype=torch.float16)\n", - "151 tensor(0.0213, dtype=torch.float16)\n", - "152 tensor(0.0200, dtype=torch.float16)\n", - "154 tensor(0.0188, dtype=torch.float16)\n", - "153 tensor(0.0177, dtype=torch.float16)\n", - "134 tensor(0.0177, dtype=torch.float16)\n", - "157 tensor(0.0177, dtype=torch.float16)\n", - "144 tensor(0.0171, dtype=torch.float16)\n", - "127 tensor(0.0171, dtype=torch.float16)\n", - "148 tensor(0.0161, dtype=torch.float16)\n", - "124 tensor(0.0151, dtype=torch.float16)\n", - "162 tensor(0.0146, dtype=torch.float16)\n", - "137 tensor(0.0146, dtype=torch.float16)\n", - "\u001b[92m143: Guess: $139.58 Truth: $112.78 Error: $26.80 SLE: 0.04 Item: Monroe Shocks & Stru...\u001b[0m\n", - "41 tensor(0.0145, dtype=torch.float16)\n", - "42 tensor(0.0140, dtype=torch.float16)\n", - "61 tensor(0.0140, dtype=torch.float16)\n", - "44 tensor(0.0140, dtype=torch.float16)\n", - "51 tensor(0.0140, dtype=torch.float16)\n", - "54 tensor(0.0136, dtype=torch.float16)\n", - "47 tensor(0.0132, dtype=torch.float16)\n", - "52 tensor(0.0132, dtype=torch.float16)\n", - "43 tensor(0.0132, dtype=torch.float16)\n", - "63 tensor(0.0132, dtype=torch.float16)\n", - "53 tensor(0.0132, dtype=torch.float16)\n", - "48 tensor(0.0128, dtype=torch.float16)\n", - "57 tensor(0.0128, dtype=torch.float16)\n", - "62 tensor(0.0124, dtype=torch.float16)\n", - "64 tensor(0.0124, dtype=torch.float16)\n", - "58 tensor(0.0120, dtype=torch.float16)\n", - "71 tensor(0.0120, dtype=torch.float16)\n", - "72 tensor(0.0116, dtype=torch.float16)\n", - "56 tensor(0.0113, dtype=torch.float16)\n", - "38 tensor(0.0113, dtype=torch.float16)\n", - "\u001b[92m144: Guess: $53.57 Truth: $27.32 Error: $26.25 SLE: 0.43 Item: Feit Electric 35W EQ...\u001b[0m\n", - "104 tensor(0.0103, dtype=torch.float16)\n", - "123 tensor(0.0097, dtype=torch.float16)\n", - "103 tensor(0.0097, dtype=torch.float16)\n", - "92 tensor(0.0097, dtype=torch.float16)\n", - "91 tensor(0.0094, dtype=torch.float16)\n", - "102 tensor(0.0094, dtype=torch.float16)\n", - "101 tensor(0.0094, dtype=torch.float16)\n", - "114 tensor(0.0091, dtype=torch.float16)\n", - "94 tensor(0.0091, dtype=torch.float16)\n", - "124 tensor(0.0091, dtype=torch.float16)\n", - "111 tensor(0.0091, dtype=torch.float16)\n", - "121 tensor(0.0088, dtype=torch.float16)\n", - "113 tensor(0.0088, dtype=torch.float16)\n", - "122 tensor(0.0088, dtype=torch.float16)\n", - "112 tensor(0.0088, dtype=torch.float16)\n", - "118 tensor(0.0086, dtype=torch.float16)\n", - "107 tensor(0.0086, dtype=torch.float16)\n", - "116 tensor(0.0086, dtype=torch.float16)\n", - "117 tensor(0.0083, dtype=torch.float16)\n", - "87 tensor(0.0083, dtype=torch.float16)\n", - "\u001b[92m145: Guess: $108.46 Truth: $145.91 Error: $37.45 SLE: 0.09 Item: Yellow Jacket 2806 C...\u001b[0m\n", - "141 tensor(0.0107, dtype=torch.float16)\n", - "147 tensor(0.0101, dtype=torch.float16)\n", - "131 tensor(0.0101, dtype=torch.float16)\n", - "171 tensor(0.0098, dtype=torch.float16)\n", - "151 tensor(0.0095, dtype=torch.float16)\n", - "154 tensor(0.0095, dtype=torch.float16)\n", - "153 tensor(0.0092, dtype=torch.float16)\n", - "157 tensor(0.0092, dtype=torch.float16)\n", - "152 tensor(0.0092, dtype=torch.float16)\n", - "142 tensor(0.0089, dtype=torch.float16)\n", - "161 tensor(0.0089, dtype=torch.float16)\n", - "163 tensor(0.0089, dtype=torch.float16)\n", - "121 tensor(0.0086, dtype=torch.float16)\n", - "172 tensor(0.0086, dtype=torch.float16)\n", - "123 tensor(0.0083, dtype=torch.float16)\n", - "164 tensor(0.0083, dtype=torch.float16)\n", - "132 tensor(0.0078, dtype=torch.float16)\n", - "144 tensor(0.0078, dtype=torch.float16)\n", - "148 tensor(0.0078, dtype=torch.float16)\n", - "162 tensor(0.0076, dtype=torch.float16)\n", - "\u001b[92m146: Guess: $149.47 Truth: $171.09 Error: $21.62 SLE: 0.02 Item: Garage-Pro Tailgate ...\u001b[0m\n", - "144 tensor(0.0072, dtype=torch.float16)\n", - "142 tensor(0.0070, dtype=torch.float16)\n", - "132 tensor(0.0066, dtype=torch.float16)\n", - "123 tensor(0.0066, dtype=torch.float16)\n", - "121 tensor(0.0064, dtype=torch.float16)\n", - "158 tensor(0.0064, dtype=torch.float16)\n", - "122 tensor(0.0064, dtype=torch.float16)\n", - "147 tensor(0.0064, dtype=torch.float16)\n", - "127 tensor(0.0064, dtype=torch.float16)\n", - "124 tensor(0.0064, dtype=torch.float16)\n", - "141 tensor(0.0062, dtype=torch.float16)\n", - "128 tensor(0.0062, dtype=torch.float16)\n", - "153 tensor(0.0062, dtype=torch.float16)\n", - "164 tensor(0.0062, dtype=torch.float16)\n", - "157 tensor(0.0062, dtype=torch.float16)\n", - "148 tensor(0.0062, dtype=torch.float16)\n", - "117 tensor(0.0060, dtype=torch.float16)\n", - "114 tensor(0.0060, dtype=torch.float16)\n", - "136 tensor(0.0060, dtype=torch.float16)\n", - "134 tensor(0.0060, dtype=torch.float16)\n", - "\u001b[92m147: Guess: $136.68 Truth: $167.95 Error: $31.27 SLE: 0.04 Item: 3M Perfect It Buffin...\u001b[0m\n", - "46 tensor(0.0196, dtype=torch.float16)\n", - "43 tensor(0.0196, dtype=torch.float16)\n", - "36 tensor(0.0196, dtype=torch.float16)\n", - "39 tensor(0.0178, dtype=torch.float16)\n", - "38 tensor(0.0173, dtype=torch.float16)\n", - "37 tensor(0.0173, dtype=torch.float16)\n", - "33 tensor(0.0168, dtype=torch.float16)\n", - "34 tensor(0.0162, dtype=torch.float16)\n", - "41 tensor(0.0162, dtype=torch.float16)\n", - "47 tensor(0.0162, dtype=torch.float16)\n", - "53 tensor(0.0157, dtype=torch.float16)\n", - "44 tensor(0.0157, dtype=torch.float16)\n", - "56 tensor(0.0157, dtype=torch.float16)\n", - "49 tensor(0.0157, dtype=torch.float16)\n", - "48 tensor(0.0148, dtype=torch.float16)\n", - "29 tensor(0.0148, dtype=torch.float16)\n", - "42 tensor(0.0143, dtype=torch.float16)\n", - "66 tensor(0.0139, dtype=torch.float16)\n", - "57 tensor(0.0135, dtype=torch.float16)\n", - "31 tensor(0.0135, dtype=torch.float16)\n", - "\u001b[92m148: Guess: $43.16 Truth: $28.49 Error: $14.67 SLE: 0.16 Item: Chinese Style Dollho...\u001b[0m\n", - "61 tensor(0.0157, dtype=torch.float16)\n", - "71 tensor(0.0157, dtype=torch.float16)\n", - "72 tensor(0.0147, dtype=torch.float16)\n", - "51 tensor(0.0147, dtype=torch.float16)\n", - "81 tensor(0.0147, dtype=torch.float16)\n", - "74 tensor(0.0139, dtype=torch.float16)\n", - "52 tensor(0.0134, dtype=torch.float16)\n", - "62 tensor(0.0130, dtype=torch.float16)\n", - "77 tensor(0.0130, dtype=torch.float16)\n", - "91 tensor(0.0130, dtype=torch.float16)\n", - "73 tensor(0.0126, dtype=torch.float16)\n", - "64 tensor(0.0126, dtype=torch.float16)\n", - "63 tensor(0.0122, dtype=torch.float16)\n", - "41 tensor(0.0119, dtype=torch.float16)\n", - "54 tensor(0.0119, dtype=torch.float16)\n", - "67 tensor(0.0119, dtype=torch.float16)\n", - "78 tensor(0.0115, dtype=torch.float16)\n", - "47 tensor(0.0111, dtype=torch.float16)\n", - "53 tensor(0.0111, dtype=torch.float16)\n", - "57 tensor(0.0111, dtype=torch.float16)\n", - "\u001b[93m149: Guess: $64.88 Truth: $122.23 Error: $57.35 SLE: 0.39 Item: Generic NRG Innovati...\u001b[0m\n", - "40 tensor(0.0466, dtype=torch.float16)\n", - "50 tensor(0.0411, dtype=torch.float16)\n", - "35 tensor(0.0341, dtype=torch.float16)\n", - "30 tensor(0.0320, dtype=torch.float16)\n", - "45 tensor(0.0310, dtype=torch.float16)\n", - "60 tensor(0.0265, dtype=torch.float16)\n", - "38 tensor(0.0227, dtype=torch.float16)\n", - "55 tensor(0.0220, dtype=torch.float16)\n", - "43 tensor(0.0207, dtype=torch.float16)\n", - "37 tensor(0.0194, dtype=torch.float16)\n", - "48 tensor(0.0194, dtype=torch.float16)\n", - "42 tensor(0.0188, dtype=torch.float16)\n", - "44 tensor(0.0188, dtype=torch.float16)\n", - "34 tensor(0.0188, dtype=torch.float16)\n", - "49 tensor(0.0182, dtype=torch.float16)\n", - "33 tensor(0.0182, dtype=torch.float16)\n", - "39 tensor(0.0177, dtype=torch.float16)\n", - "36 tensor(0.0177, dtype=torch.float16)\n", - "25 tensor(0.0177, dtype=torch.float16)\n", - "32 tensor(0.0171, dtype=torch.float16)\n", - "\u001b[92m150: Guess: $41.24 Truth: $32.99 Error: $8.25 SLE: 0.05 Item: Learning Resources C...\u001b[0m\n", - "101 tensor(0.0125, dtype=torch.float16)\n", - "131 tensor(0.0118, dtype=torch.float16)\n", - "81 tensor(0.0114, dtype=torch.float16)\n", - "121 tensor(0.0114, dtype=torch.float16)\n", - "103 tensor(0.0114, dtype=torch.float16)\n", - "91 tensor(0.0114, dtype=torch.float16)\n", - "141 tensor(0.0111, dtype=torch.float16)\n", - "123 tensor(0.0111, dtype=torch.float16)\n", - "111 tensor(0.0107, dtype=torch.float16)\n", - "102 tensor(0.0107, dtype=torch.float16)\n", - "122 tensor(0.0104, dtype=torch.float16)\n", - "94 tensor(0.0101, dtype=torch.float16)\n", - "104 tensor(0.0101, dtype=torch.float16)\n", - "92 tensor(0.0101, dtype=torch.float16)\n", - "127 tensor(0.0101, dtype=torch.float16)\n", - "71 tensor(0.0098, dtype=torch.float16)\n", - "132 tensor(0.0092, dtype=torch.float16)\n", - "107 tensor(0.0089, dtype=torch.float16)\n", - "87 tensor(0.0089, dtype=torch.float16)\n", - "74 tensor(0.0089, dtype=torch.float16)\n", - "\u001b[92m151: Guess: $106.22 Truth: $71.20 Error: $35.02 SLE: 0.16 Item: Bosch Automotive 154...\u001b[0m\n", - "61 tensor(0.0132, dtype=torch.float16)\n", - "54 tensor(0.0124, dtype=torch.float16)\n", - "72 tensor(0.0124, dtype=torch.float16)\n", - "63 tensor(0.0120, dtype=torch.float16)\n", - "71 tensor(0.0120, dtype=torch.float16)\n", - "81 tensor(0.0120, dtype=torch.float16)\n", - "62 tensor(0.0116, dtype=torch.float16)\n", - "64 tensor(0.0113, dtype=torch.float16)\n", - "51 tensor(0.0113, dtype=torch.float16)\n", - "73 tensor(0.0113, dtype=torch.float16)\n", - "57 tensor(0.0113, dtype=torch.float16)\n", - "84 tensor(0.0109, dtype=torch.float16)\n", - "53 tensor(0.0109, dtype=torch.float16)\n", - "52 tensor(0.0109, dtype=torch.float16)\n", - "74 tensor(0.0106, dtype=torch.float16)\n", - "48 tensor(0.0106, dtype=torch.float16)\n", - "58 tensor(0.0106, dtype=torch.float16)\n", - "87 tensor(0.0103, dtype=torch.float16)\n", - "78 tensor(0.0103, dtype=torch.float16)\n", - "91 tensor(0.0099, dtype=torch.float16)\n", - "\u001b[93m152: Guess: $66.45 Truth: $112.75 Error: $46.30 SLE: 0.27 Item: Case of 24-2 Inch Bl...\u001b[0m\n", - "103 tensor(0.0163, dtype=torch.float16)\n", - "107 tensor(0.0163, dtype=torch.float16)\n", - "101 tensor(0.0149, dtype=torch.float16)\n", - "91 tensor(0.0149, dtype=torch.float16)\n", - "94 tensor(0.0144, dtype=torch.float16)\n", - "104 tensor(0.0140, dtype=torch.float16)\n", - "123 tensor(0.0140, dtype=torch.float16)\n", - "121 tensor(0.0140, dtype=torch.float16)\n", - "102 tensor(0.0135, dtype=torch.float16)\n", - "113 tensor(0.0135, dtype=torch.float16)\n", - "93 tensor(0.0135, dtype=torch.float16)\n", - "117 tensor(0.0131, dtype=torch.float16)\n", - "106 tensor(0.0131, dtype=torch.float16)\n", - "97 tensor(0.0127, dtype=torch.float16)\n", - "127 tensor(0.0127, dtype=torch.float16)\n", - "114 tensor(0.0123, dtype=torch.float16)\n", - "126 tensor(0.0123, dtype=torch.float16)\n", - "122 tensor(0.0123, dtype=torch.float16)\n", - "92 tensor(0.0123, dtype=torch.float16)\n", - "111 tensor(0.0123, dtype=torch.float16)\n", - "\u001b[92m153: Guess: $107.87 Truth: $142.43 Error: $34.56 SLE: 0.08 Item: MOCA Engine Water Pu...\u001b[0m\n", - "299 tensor(0.0701, dtype=torch.float16)\n", - "399 tensor(0.0467, dtype=torch.float16)\n", - "279 tensor(0.0467, dtype=torch.float16)\n", - "289 tensor(0.0399, dtype=torch.float16)\n", - "249 tensor(0.0363, dtype=torch.float16)\n", - "269 tensor(0.0352, dtype=torch.float16)\n", - "319 tensor(0.0341, dtype=torch.float16)\n", - "329 tensor(0.0341, dtype=torch.float16)\n", - "349 tensor(0.0321, dtype=torch.float16)\n", - "259 tensor(0.0301, dtype=torch.float16)\n", - "369 tensor(0.0258, dtype=torch.float16)\n", - "309 tensor(0.0250, dtype=torch.float16)\n", - "239 tensor(0.0235, dtype=torch.float16)\n", - "359 tensor(0.0235, dtype=torch.float16)\n", - "339 tensor(0.0228, dtype=torch.float16)\n", - "379 tensor(0.0172, dtype=torch.float16)\n", - "389 tensor(0.0161, dtype=torch.float16)\n", - "300 tensor(0.0156, dtype=torch.float16)\n", - "219 tensor(0.0138, dtype=torch.float16)\n", - "229 tensor(0.0130, dtype=torch.float16)\n", - "\u001b[93m154: Guess: $309.57 Truth: $398.99 Error: $89.42 SLE: 0.06 Item: SAREMAS Foot Step Ba...\u001b[0m\n", - "600 tensor(0.0825, dtype=torch.float16)\n", - "500 tensor(0.0775, dtype=torch.float16)\n", - "400 tensor(0.0684, dtype=torch.float16)\n", - "700 tensor(0.0585, dtype=torch.float16)\n", - "800 tensor(0.0470, dtype=torch.float16)\n", - "599 tensor(0.0366, dtype=torch.float16)\n", - "900 tensor(0.0333, dtype=torch.float16)\n", - "499 tensor(0.0285, dtype=torch.float16)\n", - "450 tensor(0.0268, dtype=torch.float16)\n", - "699 tensor(0.0236, dtype=torch.float16)\n", - "399 tensor(0.0202, dtype=torch.float16)\n", - "300 tensor(0.0202, dtype=torch.float16)\n", - "550 tensor(0.0190, dtype=torch.float16)\n", - "799 tensor(0.0184, dtype=torch.float16)\n", - "650 tensor(0.0178, dtype=torch.float16)\n", - "750 tensor(0.0143, dtype=torch.float16)\n", - "899 tensor(0.0135, dtype=torch.float16)\n", - "999 tensor(0.0135, dtype=torch.float16)\n", - "350 tensor(0.0126, dtype=torch.float16)\n", - "549 tensor(0.0115, dtype=torch.float16)\n", - "\u001b[93m155: Guess: $600.79 Truth: $449.00 Error: $151.79 SLE: 0.08 Item: Gretsch G9210 Square...\u001b[0m\n", - "250 tensor(0.0332, dtype=torch.float16)\n", - "170 tensor(0.0312, dtype=torch.float16)\n", - "130 tensor(0.0302, dtype=torch.float16)\n", - "160 tensor(0.0293, dtype=torch.float16)\n", - "140 tensor(0.0275, dtype=torch.float16)\n", - "150 tensor(0.0258, dtype=torch.float16)\n", - "180 tensor(0.0251, dtype=torch.float16)\n", - "190 tensor(0.0243, dtype=torch.float16)\n", - "240 tensor(0.0243, dtype=torch.float16)\n", - "120 tensor(0.0235, dtype=torch.float16)\n", - "300 tensor(0.0214, dtype=torch.float16)\n", - "200 tensor(0.0214, dtype=torch.float16)\n", - "110 tensor(0.0201, dtype=torch.float16)\n", - "220 tensor(0.0201, dtype=torch.float16)\n", - "230 tensor(0.0189, dtype=torch.float16)\n", - "260 tensor(0.0178, dtype=torch.float16)\n", - "270 tensor(0.0172, dtype=torch.float16)\n", - "100 tensor(0.0162, dtype=torch.float16)\n", - "280 tensor(0.0152, dtype=torch.float16)\n", - "210 tensor(0.0147, dtype=torch.float16)\n", - "\u001b[92m156: Guess: $191.60 Truth: $189.00 Error: $2.60 SLE: 0.00 Item: NikoMaku Mirror Dash...\u001b[0m\n", - "130 tensor(0.0296, dtype=torch.float16)\n", - "140 tensor(0.0296, dtype=torch.float16)\n", - "160 tensor(0.0253, dtype=torch.float16)\n", - "110 tensor(0.0253, dtype=torch.float16)\n", - "150 tensor(0.0253, dtype=torch.float16)\n", - "120 tensor(0.0238, dtype=torch.float16)\n", - "170 tensor(0.0231, dtype=torch.float16)\n", - "100 tensor(0.0217, dtype=torch.float16)\n", - "180 tensor(0.0185, dtype=torch.float16)\n", - "190 tensor(0.0149, dtype=torch.float16)\n", - "115 tensor(0.0144, dtype=torch.float16)\n", - "135 tensor(0.0140, dtype=torch.float16)\n", - "200 tensor(0.0136, dtype=torch.float16)\n", - "145 tensor(0.0127, dtype=torch.float16)\n", - "90 tensor(0.0112, dtype=torch.float16)\n", - "155 tensor(0.0106, dtype=torch.float16)\n", - "250 tensor(0.0106, dtype=torch.float16)\n", - "105 tensor(0.0102, dtype=torch.float16)\n", - "125 tensor(0.0102, dtype=torch.float16)\n", - "210 tensor(0.0102, dtype=torch.float16)\n", - "\u001b[92m157: Guess: $145.49 Truth: $120.91 Error: $24.58 SLE: 0.03 Item: Fenix HP25R v2.0 USB...\u001b[0m\n", - "172 tensor(0.1027, dtype=torch.float16)\n", - "173 tensor(0.0500, dtype=torch.float16)\n", - "171 tensor(0.0485, dtype=torch.float16)\n", - "168 tensor(0.0428, dtype=torch.float16)\n", - "167 tensor(0.0390, dtype=torch.float16)\n", - "157 tensor(0.0323, dtype=torch.float16)\n", - "154 tensor(0.0323, dtype=torch.float16)\n", - "162 tensor(0.0294, dtype=torch.float16)\n", - "163 tensor(0.0294, dtype=torch.float16)\n", - "184 tensor(0.0276, dtype=torch.float16)\n", - "174 tensor(0.0276, dtype=torch.float16)\n", - "164 tensor(0.0268, dtype=torch.float16)\n", - "158 tensor(0.0260, dtype=torch.float16)\n", - "177 tensor(0.0229, dtype=torch.float16)\n", - "178 tensor(0.0209, dtype=torch.float16)\n", - "161 tensor(0.0209, dtype=torch.float16)\n", - "193 tensor(0.0202, dtype=torch.float16)\n", - "176 tensor(0.0202, dtype=torch.float16)\n", - "183 tensor(0.0202, dtype=torch.float16)\n", - "181 tensor(0.0202, dtype=torch.float16)\n", - "\u001b[92m158: Guess: $170.09 Truth: $203.53 Error: $33.44 SLE: 0.03 Item: R&L Racing Heavy Dut...\u001b[0m\n", - "300 tensor(0.0558, dtype=torch.float16)\n", - "250 tensor(0.0558, dtype=torch.float16)\n", - "280 tensor(0.0308, dtype=torch.float16)\n", - "240 tensor(0.0289, dtype=torch.float16)\n", - "270 tensor(0.0280, dtype=torch.float16)\n", - "260 tensor(0.0247, dtype=torch.float16)\n", - "230 tensor(0.0240, dtype=torch.float16)\n", - "350 tensor(0.0225, dtype=torch.float16)\n", - "220 tensor(0.0218, dtype=torch.float16)\n", - "290 tensor(0.0187, dtype=torch.float16)\n", - "330 tensor(0.0170, dtype=torch.float16)\n", - "320 tensor(0.0150, dtype=torch.float16)\n", - "210 tensor(0.0141, dtype=torch.float16)\n", - "200 tensor(0.0141, dtype=torch.float16)\n", - "400 tensor(0.0137, dtype=torch.float16)\n", - "225 tensor(0.0124, dtype=torch.float16)\n", - "310 tensor(0.0121, dtype=torch.float16)\n", - "275 tensor(0.0110, dtype=torch.float16)\n", - "340 tensor(0.0094, dtype=torch.float16)\n", - "245 tensor(0.0088, dtype=torch.float16)\n", - "\u001b[93m159: Guess: $274.56 Truth: $349.99 Error: $75.43 SLE: 0.06 Item: Garmin GPSMAP 64sx, ...\u001b[0m\n", - "11 tensor(0.0562, dtype=torch.float16)\n", - "12 tensor(0.0545, dtype=torch.float16)\n", - "13 tensor(0.0512, dtype=torch.float16)\n", - "14 tensor(0.0496, dtype=torch.float16)\n", - "9 tensor(0.0496, dtype=torch.float16)\n", - "8 tensor(0.0481, dtype=torch.float16)\n", - "7 tensor(0.0466, dtype=torch.float16)\n", - "10 tensor(0.0424, dtype=torch.float16)\n", - "6 tensor(0.0386, dtype=torch.float16)\n", - "16 tensor(0.0363, dtype=torch.float16)\n", - "15 tensor(0.0363, dtype=torch.float16)\n", - "18 tensor(0.0352, dtype=torch.float16)\n", - "17 tensor(0.0352, dtype=torch.float16)\n", - "5 tensor(0.0331, dtype=torch.float16)\n", - "4 tensor(0.0301, dtype=torch.float16)\n", - "21 tensor(0.0283, dtype=torch.float16)\n", - "19 tensor(0.0283, dtype=torch.float16)\n", - "3 tensor(0.0249, dtype=torch.float16)\n", - "22 tensor(0.0249, dtype=torch.float16)\n", - "23 tensor(0.0200, dtype=torch.float16)\n", - "\u001b[92m160: Guess: $12.09 Truth: $34.35 Error: $22.26 SLE: 0.99 Item: Brown 5-7/8 X 8-1/2 ...\u001b[0m\n", - "299 tensor(0.0420, dtype=torch.float16)\n", - "399 tensor(0.0298, dtype=torch.float16)\n", - "249 tensor(0.0263, dtype=torch.float16)\n", - "300 tensor(0.0239, dtype=torch.float16)\n", - "349 tensor(0.0218, dtype=torch.float16)\n", - "329 tensor(0.0192, dtype=torch.float16)\n", - "259 tensor(0.0186, dtype=torch.float16)\n", - "279 tensor(0.0170, dtype=torch.float16)\n", - "269 tensor(0.0164, dtype=torch.float16)\n", - "400 tensor(0.0159, dtype=torch.float16)\n", - "250 tensor(0.0154, dtype=torch.float16)\n", - "270 tensor(0.0150, dtype=torch.float16)\n", - "229 tensor(0.0150, dtype=torch.float16)\n", - "239 tensor(0.0145, dtype=torch.float16)\n", - "289 tensor(0.0145, dtype=torch.float16)\n", - "499 tensor(0.0132, dtype=torch.float16)\n", - "330 tensor(0.0132, dtype=torch.float16)\n", - "369 tensor(0.0128, dtype=torch.float16)\n", - "199 tensor(0.0120, dtype=torch.float16)\n", - "260 tensor(0.0120, dtype=torch.float16)\n", - "\u001b[93m161: Guess: $305.53 Truth: $384.99 Error: $79.46 SLE: 0.05 Item: GAOMON PD2200 Pen Di...\u001b[0m\n", - "186 tensor(0.0240, dtype=torch.float16)\n", - "187 tensor(0.0199, dtype=torch.float16)\n", - "181 tensor(0.0193, dtype=torch.float16)\n", - "193 tensor(0.0176, dtype=torch.float16)\n", - "176 tensor(0.0176, dtype=torch.float16)\n", - "192 tensor(0.0170, dtype=torch.float16)\n", - "182 tensor(0.0165, dtype=torch.float16)\n", - "196 tensor(0.0165, dtype=torch.float16)\n", - "178 tensor(0.0165, dtype=torch.float16)\n", - "184 tensor(0.0165, dtype=torch.float16)\n", - "188 tensor(0.0165, dtype=torch.float16)\n", - "183 tensor(0.0155, dtype=torch.float16)\n", - "172 tensor(0.0155, dtype=torch.float16)\n", - "198 tensor(0.0150, dtype=torch.float16)\n", - "177 tensor(0.0150, dtype=torch.float16)\n", - "194 tensor(0.0146, dtype=torch.float16)\n", - "163 tensor(0.0146, dtype=torch.float16)\n", - "191 tensor(0.0141, dtype=torch.float16)\n", - "174 tensor(0.0137, dtype=torch.float16)\n", - "171 tensor(0.0137, dtype=torch.float16)\n", - "\u001b[92m162: Guess: $183.59 Truth: $211.00 Error: $27.41 SLE: 0.02 Item: VXMOTOR for 97-03 Fo...\u001b[0m\n", - "250 tensor(0.0116, dtype=torch.float16)\n", - "300 tensor(0.0106, dtype=torch.float16)\n", - "139 tensor(0.0096, dtype=torch.float16)\n", - "149 tensor(0.0096, dtype=torch.float16)\n", - "150 tensor(0.0090, dtype=torch.float16)\n", - "145 tensor(0.0090, dtype=torch.float16)\n", - "189 tensor(0.0090, dtype=torch.float16)\n", - "199 tensor(0.0088, dtype=torch.float16)\n", - "135 tensor(0.0085, dtype=torch.float16)\n", - "144 tensor(0.0085, dtype=torch.float16)\n", - "129 tensor(0.0085, dtype=torch.float16)\n", - "124 tensor(0.0082, dtype=torch.float16)\n", - "200 tensor(0.0082, dtype=torch.float16)\n", - "125 tensor(0.0082, dtype=torch.float16)\n", - "148 tensor(0.0082, dtype=torch.float16)\n", - "138 tensor(0.0080, dtype=torch.float16)\n", - "175 tensor(0.0080, dtype=torch.float16)\n", - "128 tensor(0.0077, dtype=torch.float16)\n", - "198 tensor(0.0077, dtype=torch.float16)\n", - "195 tensor(0.0077, dtype=torch.float16)\n", - "\u001b[93m163: Guess: $170.81 Truth: $129.00 Error: $41.81 SLE: 0.08 Item: HP EliteBook 2540p I...\u001b[0m\n", - "24 tensor(0.0244, dtype=torch.float16)\n", - "34 tensor(0.0229, dtype=torch.float16)\n", - "22 tensor(0.0229, dtype=torch.float16)\n", - "23 tensor(0.0222, dtype=torch.float16)\n", - "29 tensor(0.0222, dtype=torch.float16)\n", - "21 tensor(0.0215, dtype=torch.float16)\n", - "28 tensor(0.0215, dtype=torch.float16)\n", - "32 tensor(0.0208, dtype=torch.float16)\n", - "31 tensor(0.0208, dtype=torch.float16)\n", - "27 tensor(0.0208, dtype=torch.float16)\n", - "26 tensor(0.0196, dtype=torch.float16)\n", - "25 tensor(0.0190, dtype=torch.float16)\n", - "33 tensor(0.0184, dtype=torch.float16)\n", - "38 tensor(0.0178, dtype=torch.float16)\n", - "41 tensor(0.0173, dtype=torch.float16)\n", - "18 tensor(0.0173, dtype=torch.float16)\n", - "42 tensor(0.0173, dtype=torch.float16)\n", - "19 tensor(0.0168, dtype=torch.float16)\n", - "44 tensor(0.0162, dtype=torch.float16)\n", - "36 tensor(0.0162, dtype=torch.float16)\n", - "\u001b[91m164: Guess: $29.25 Truth: $111.45 Error: $82.20 SLE: 1.72 Item: Green EPX Mixing Noz...\u001b[0m\n", - "31 tensor(0.0208, dtype=torch.float16)\n", - "41 tensor(0.0195, dtype=torch.float16)\n", - "34 tensor(0.0189, dtype=torch.float16)\n", - "42 tensor(0.0183, dtype=torch.float16)\n", - "32 tensor(0.0178, dtype=torch.float16)\n", - "24 tensor(0.0167, dtype=torch.float16)\n", - "28 tensor(0.0167, dtype=torch.float16)\n", - "38 tensor(0.0162, dtype=torch.float16)\n", - "21 tensor(0.0162, dtype=torch.float16)\n", - "44 tensor(0.0157, dtype=torch.float16)\n", - "22 tensor(0.0157, dtype=torch.float16)\n", - "29 tensor(0.0157, dtype=torch.float16)\n", - "27 tensor(0.0152, dtype=torch.float16)\n", - "23 tensor(0.0152, dtype=torch.float16)\n", - "48 tensor(0.0147, dtype=torch.float16)\n", - "43 tensor(0.0147, dtype=torch.float16)\n", - "39 tensor(0.0147, dtype=torch.float16)\n", - "51 tensor(0.0147, dtype=torch.float16)\n", - "33 tensor(0.0147, dtype=torch.float16)\n", - "37 tensor(0.0143, dtype=torch.float16)\n", - "\u001b[93m165: Guess: $34.25 Truth: $81.12 Error: $46.87 SLE: 0.72 Item: Box Partners 6 1/4 x...\u001b[0m\n", - "400 tensor(0.0064, dtype=torch.float16)\n", - "300 tensor(0.0058, dtype=torch.float16)\n", - "350 tensor(0.0047, dtype=torch.float16)\n", - "500 tensor(0.0046, dtype=torch.float16)\n", - "360 tensor(0.0042, dtype=torch.float16)\n", - "450 tensor(0.0041, dtype=torch.float16)\n", - "330 tensor(0.0039, dtype=torch.float16)\n", - "380 tensor(0.0037, dtype=torch.float16)\n", - "290 tensor(0.0037, dtype=torch.float16)\n", - "310 tensor(0.0036, dtype=torch.float16)\n", - "340 tensor(0.0035, dtype=torch.float16)\n", - "320 tensor(0.0034, dtype=torch.float16)\n", - "390 tensor(0.0034, dtype=torch.float16)\n", - "270 tensor(0.0034, dtype=torch.float16)\n", - "370 tensor(0.0033, dtype=torch.float16)\n", - "399 tensor(0.0033, dtype=torch.float16)\n", - "420 tensor(0.0033, dtype=torch.float16)\n", - "600 tensor(0.0032, dtype=torch.float16)\n", - "315 tensor(0.0032, dtype=torch.float16)\n", - "430 tensor(0.0031, dtype=torch.float16)\n", - "\u001b[92m166: Guess: $375.35 Truth: $457.08 Error: $81.73 SLE: 0.04 Item: Vixen Air 1/2 NPT Ai...\u001b[0m\n", - "90 tensor(0.0276, dtype=torch.float16)\n", - "70 tensor(0.0267, dtype=torch.float16)\n", - "80 tensor(0.0259, dtype=torch.float16)\n", - "110 tensor(0.0215, dtype=torch.float16)\n", - "60 tensor(0.0215, dtype=torch.float16)\n", - "100 tensor(0.0208, dtype=torch.float16)\n", - "130 tensor(0.0196, dtype=torch.float16)\n", - "120 tensor(0.0184, dtype=torch.float16)\n", - "76 tensor(0.0152, dtype=torch.float16)\n", - "99 tensor(0.0143, dtype=torch.float16)\n", - "86 tensor(0.0143, dtype=torch.float16)\n", - "66 tensor(0.0135, dtype=torch.float16)\n", - "140 tensor(0.0135, dtype=torch.float16)\n", - "69 tensor(0.0135, dtype=torch.float16)\n", - "79 tensor(0.0122, dtype=torch.float16)\n", - "150 tensor(0.0115, dtype=torch.float16)\n", - "50 tensor(0.0115, dtype=torch.float16)\n", - "89 tensor(0.0111, dtype=torch.float16)\n", - "96 tensor(0.0108, dtype=torch.float16)\n", - "65 tensor(0.0108, dtype=torch.float16)\n", - "\u001b[93m167: Guess: $91.01 Truth: $49.49 Error: $41.52 SLE: 0.36 Item: Smart Floor Lamp, Mu...\u001b[0m\n", - "31 tensor(0.0186, dtype=torch.float16)\n", - "41 tensor(0.0175, dtype=torch.float16)\n", - "34 tensor(0.0175, dtype=torch.float16)\n", - "36 tensor(0.0170, dtype=torch.float16)\n", - "32 tensor(0.0164, dtype=torch.float16)\n", - "28 tensor(0.0164, dtype=torch.float16)\n", - "26 tensor(0.0164, dtype=torch.float16)\n", - "29 tensor(0.0164, dtype=torch.float16)\n", - "33 tensor(0.0159, dtype=torch.float16)\n", - "37 tensor(0.0159, dtype=torch.float16)\n", - "43 tensor(0.0159, dtype=torch.float16)\n", - "38 tensor(0.0159, dtype=torch.float16)\n", - "21 tensor(0.0159, dtype=torch.float16)\n", - "27 tensor(0.0159, dtype=torch.float16)\n", - "24 tensor(0.0154, dtype=torch.float16)\n", - "39 tensor(0.0150, dtype=torch.float16)\n", - "22 tensor(0.0150, dtype=torch.float16)\n", - "42 tensor(0.0150, dtype=torch.float16)\n", - "23 tensor(0.0150, dtype=torch.float16)\n", - "46 tensor(0.0145, dtype=torch.float16)\n", - "\u001b[93m168: Guess: $32.59 Truth: $80.56 Error: $47.97 SLE: 0.79 Item: SOZG 324mm Wheelbase...\u001b[0m\n", - "289 tensor(0.0076, dtype=torch.float16)\n", - "290 tensor(0.0072, dtype=torch.float16)\n", - "292 tensor(0.0070, dtype=torch.float16)\n", - "300 tensor(0.0069, dtype=torch.float16)\n", - "274 tensor(0.0069, dtype=torch.float16)\n", - "293 tensor(0.0069, dtype=torch.float16)\n", - "270 tensor(0.0069, dtype=torch.float16)\n", - "299 tensor(0.0068, dtype=torch.float16)\n", - "294 tensor(0.0068, dtype=torch.float16)\n", - "268 tensor(0.0067, dtype=torch.float16)\n", - "306 tensor(0.0067, dtype=torch.float16)\n", - "266 tensor(0.0067, dtype=torch.float16)\n", - "288 tensor(0.0067, dtype=torch.float16)\n", - "280 tensor(0.0065, dtype=torch.float16)\n", - "265 tensor(0.0065, dtype=torch.float16)\n", - "304 tensor(0.0065, dtype=torch.float16)\n", - "310 tensor(0.0065, dtype=torch.float16)\n", - "267 tensor(0.0065, dtype=torch.float16)\n", - "305 tensor(0.0064, dtype=torch.float16)\n", - "272 tensor(0.0064, dtype=torch.float16)\n", - "\u001b[92m169: Guess: $286.64 Truth: $278.39 Error: $8.25 SLE: 0.00 Item: Mickey Thompson ET S...\u001b[0m\n", - "255 tensor(0.0080, dtype=torch.float16)\n", - "265 tensor(0.0078, dtype=torch.float16)\n", - "299 tensor(0.0073, dtype=torch.float16)\n", - "270 tensor(0.0073, dtype=torch.float16)\n", - "290 tensor(0.0071, dtype=torch.float16)\n", - "238 tensor(0.0071, dtype=torch.float16)\n", - "250 tensor(0.0071, dtype=torch.float16)\n", - "263 tensor(0.0071, dtype=torch.float16)\n", - "289 tensor(0.0071, dtype=torch.float16)\n", - "293 tensor(0.0068, dtype=torch.float16)\n", - "239 tensor(0.0068, dtype=torch.float16)\n", - "237 tensor(0.0068, dtype=torch.float16)\n", - "264 tensor(0.0068, dtype=torch.float16)\n", - "292 tensor(0.0066, dtype=torch.float16)\n", - "245 tensor(0.0066, dtype=torch.float16)\n", - "252 tensor(0.0066, dtype=torch.float16)\n", - "274 tensor(0.0066, dtype=torch.float16)\n", - "236 tensor(0.0066, dtype=torch.float16)\n", - "266 tensor(0.0066, dtype=torch.float16)\n", - "268 tensor(0.0066, dtype=torch.float16)\n", - "\u001b[93m170: Guess: $264.35 Truth: $364.50 Error: $100.15 SLE: 0.10 Item: Pirelli 106W XL RFT ...\u001b[0m\n", - "300 tensor(0.0075, dtype=torch.float16)\n", - "250 tensor(0.0061, dtype=torch.float16)\n", - "265 tensor(0.0059, dtype=torch.float16)\n", - "270 tensor(0.0058, dtype=torch.float16)\n", - "290 tensor(0.0058, dtype=torch.float16)\n", - "240 tensor(0.0057, dtype=torch.float16)\n", - "260 tensor(0.0055, dtype=torch.float16)\n", - "255 tensor(0.0052, dtype=torch.float16)\n", - "280 tensor(0.0050, dtype=torch.float16)\n", - "350 tensor(0.0050, dtype=torch.float16)\n", - "315 tensor(0.0048, dtype=torch.float16)\n", - "320 tensor(0.0048, dtype=torch.float16)\n", - "325 tensor(0.0047, dtype=torch.float16)\n", - "275 tensor(0.0047, dtype=torch.float16)\n", - "295 tensor(0.0047, dtype=torch.float16)\n", - "285 tensor(0.0047, dtype=torch.float16)\n", - "288 tensor(0.0046, dtype=torch.float16)\n", - "268 tensor(0.0045, dtype=torch.float16)\n", - "305 tensor(0.0045, dtype=torch.float16)\n", - "299 tensor(0.0045, dtype=torch.float16)\n", - "\u001b[93m171: Guess: $285.60 Truth: $378.99 Error: $93.39 SLE: 0.08 Item: Torklift C3212 Rear ...\u001b[0m\n", - "193 tensor(0.0091, dtype=torch.float16)\n", - "171 tensor(0.0088, dtype=torch.float16)\n", - "192 tensor(0.0086, dtype=torch.float16)\n", - "186 tensor(0.0078, dtype=torch.float16)\n", - "172 tensor(0.0078, dtype=torch.float16)\n", - "174 tensor(0.0075, dtype=torch.float16)\n", - "173 tensor(0.0073, dtype=torch.float16)\n", - "161 tensor(0.0073, dtype=torch.float16)\n", - "163 tensor(0.0071, dtype=torch.float16)\n", - "204 tensor(0.0071, dtype=torch.float16)\n", - "201 tensor(0.0071, dtype=torch.float16)\n", - "202 tensor(0.0071, dtype=torch.float16)\n", - "203 tensor(0.0071, dtype=torch.float16)\n", - "194 tensor(0.0071, dtype=torch.float16)\n", - "176 tensor(0.0071, dtype=torch.float16)\n", - "177 tensor(0.0069, dtype=torch.float16)\n", - "152 tensor(0.0069, dtype=torch.float16)\n", - "191 tensor(0.0069, dtype=torch.float16)\n", - "205 tensor(0.0069, dtype=torch.float16)\n", - "197 tensor(0.0067, dtype=torch.float16)\n", - "\u001b[92m172: Guess: $184.23 Truth: $165.28 Error: $18.95 SLE: 0.01 Item: Cardone Remanufactur...\u001b[0m\n", - "41 tensor(0.0143, dtype=torch.float16)\n", - "51 tensor(0.0143, dtype=torch.float16)\n", - "61 tensor(0.0143, dtype=torch.float16)\n", - "44 tensor(0.0130, dtype=torch.float16)\n", - "42 tensor(0.0130, dtype=torch.float16)\n", - "52 tensor(0.0126, dtype=torch.float16)\n", - "71 tensor(0.0126, dtype=torch.float16)\n", - "63 tensor(0.0126, dtype=torch.float16)\n", - "54 tensor(0.0126, dtype=torch.float16)\n", - "53 tensor(0.0122, dtype=torch.float16)\n", - "47 tensor(0.0122, dtype=torch.float16)\n", - "43 tensor(0.0122, dtype=torch.float16)\n", - "64 tensor(0.0118, dtype=torch.float16)\n", - "62 tensor(0.0118, dtype=torch.float16)\n", - "48 tensor(0.0118, dtype=torch.float16)\n", - "58 tensor(0.0115, dtype=torch.float16)\n", - "57 tensor(0.0115, dtype=torch.float16)\n", - "72 tensor(0.0111, dtype=torch.float16)\n", - "38 tensor(0.0111, dtype=torch.float16)\n", - "49 tensor(0.0108, dtype=torch.float16)\n", - "\u001b[92m173: Guess: $53.38 Truth: $56.74 Error: $3.36 SLE: 0.00 Item: Kidde AccessPoint 00...\u001b[0m\n", - "300 tensor(0.0082, dtype=torch.float16)\n", - "350 tensor(0.0066, dtype=torch.float16)\n", - "250 tensor(0.0066, dtype=torch.float16)\n", - "400 tensor(0.0064, dtype=torch.float16)\n", - "270 tensor(0.0056, dtype=torch.float16)\n", - "240 tensor(0.0055, dtype=torch.float16)\n", - "299 tensor(0.0054, dtype=torch.float16)\n", - "290 tensor(0.0053, dtype=torch.float16)\n", - "280 tensor(0.0053, dtype=torch.float16)\n", - "260 tensor(0.0050, dtype=torch.float16)\n", - "330 tensor(0.0048, dtype=torch.float16)\n", - "320 tensor(0.0048, dtype=torch.float16)\n", - "310 tensor(0.0047, dtype=torch.float16)\n", - "265 tensor(0.0045, dtype=torch.float16)\n", - "360 tensor(0.0044, dtype=torch.float16)\n", - "340 tensor(0.0043, dtype=torch.float16)\n", - "399 tensor(0.0041, dtype=torch.float16)\n", - "275 tensor(0.0041, dtype=torch.float16)\n", - "249 tensor(0.0041, dtype=torch.float16)\n", - "450 tensor(0.0040, dtype=torch.float16)\n", - "\u001b[92m174: Guess: $310.11 Truth: $307.95 Error: $2.16 SLE: 0.00 Item: 3M Protecta Self Ret...\u001b[0m\n", - "49 tensor(0.0166, dtype=torch.float16)\n", - "52 tensor(0.0161, dtype=torch.float16)\n", - "54 tensor(0.0156, dtype=torch.float16)\n", - "51 tensor(0.0156, dtype=torch.float16)\n", - "58 tensor(0.0151, dtype=torch.float16)\n", - "61 tensor(0.0151, dtype=torch.float16)\n", - "63 tensor(0.0147, dtype=torch.float16)\n", - "62 tensor(0.0147, dtype=torch.float16)\n", - "53 tensor(0.0142, dtype=torch.float16)\n", - "48 tensor(0.0142, dtype=torch.float16)\n", - "72 tensor(0.0138, dtype=torch.float16)\n", - "59 tensor(0.0138, dtype=torch.float16)\n", - "65 tensor(0.0134, dtype=torch.float16)\n", - "57 tensor(0.0134, dtype=torch.float16)\n", - "69 tensor(0.0134, dtype=torch.float16)\n", - "55 tensor(0.0134, dtype=torch.float16)\n", - "47 tensor(0.0134, dtype=torch.float16)\n", - "64 tensor(0.0134, dtype=torch.float16)\n", - "42 tensor(0.0129, dtype=torch.float16)\n", - "44 tensor(0.0129, dtype=torch.float16)\n", - "\u001b[92m175: Guess: $56.20 Truth: $38.00 Error: $18.20 SLE: 0.15 Item: Plantronics Wired He...\u001b[0m\n", - "100 tensor(0.0266, dtype=torch.float16)\n", - "150 tensor(0.0214, dtype=torch.float16)\n", - "80 tensor(0.0172, dtype=torch.float16)\n", - "90 tensor(0.0166, dtype=torch.float16)\n", - "75 tensor(0.0156, dtype=torch.float16)\n", - "70 tensor(0.0138, dtype=torch.float16)\n", - "99 tensor(0.0134, dtype=torch.float16)\n", - "250 tensor(0.0134, dtype=torch.float16)\n", - "300 tensor(0.0130, dtype=torch.float16)\n", - "200 tensor(0.0126, dtype=torch.float16)\n", - "50 tensor(0.0126, dtype=torch.float16)\n", - "130 tensor(0.0122, dtype=torch.float16)\n", - "60 tensor(0.0122, dtype=torch.float16)\n", - "95 tensor(0.0118, dtype=torch.float16)\n", - "120 tensor(0.0118, dtype=torch.float16)\n", - "125 tensor(0.0114, dtype=torch.float16)\n", - "85 tensor(0.0114, dtype=torch.float16)\n", - "65 tensor(0.0104, dtype=torch.float16)\n", - "140 tensor(0.0101, dtype=torch.float16)\n", - "98 tensor(0.0098, dtype=torch.float16)\n", - "\u001b[93m176: Guess: $118.60 Truth: $53.00 Error: $65.60 SLE: 0.63 Item: Logitech K750 Wirele...\u001b[0m\n", - "400 tensor(0.0363, dtype=torch.float16)\n", - "498 tensor(0.0352, dtype=torch.float16)\n", - "398 tensor(0.0316, dtype=torch.float16)\n", - "499 tensor(0.0274, dtype=torch.float16)\n", - "500 tensor(0.0250, dtype=torch.float16)\n", - "450 tensor(0.0224, dtype=torch.float16)\n", - "399 tensor(0.0214, dtype=torch.float16)\n", - "448 tensor(0.0172, dtype=torch.float16)\n", - "449 tensor(0.0154, dtype=torch.float16)\n", - "497 tensor(0.0151, dtype=torch.float16)\n", - "598 tensor(0.0145, dtype=torch.float16)\n", - "600 tensor(0.0132, dtype=torch.float16)\n", - "550 tensor(0.0126, dtype=torch.float16)\n", - "548 tensor(0.0126, dtype=torch.float16)\n", - "350 tensor(0.0122, dtype=torch.float16)\n", - "397 tensor(0.0122, dtype=torch.float16)\n", - "599 tensor(0.0120, dtype=torch.float16)\n", - "428 tensor(0.0104, dtype=torch.float16)\n", - "549 tensor(0.0104, dtype=torch.float16)\n", - "480 tensor(0.0088, dtype=torch.float16)\n", - "\u001b[92m177: Guess: $471.84 Truth: $498.00 Error: $26.16 SLE: 0.00 Item: Olympus PEN E-PL9 Bo...\u001b[0m\n", - "141 tensor(0.0178, dtype=torch.float16)\n", - "123 tensor(0.0143, dtype=torch.float16)\n", - "131 tensor(0.0143, dtype=torch.float16)\n", - "142 tensor(0.0138, dtype=torch.float16)\n", - "157 tensor(0.0134, dtype=torch.float16)\n", - "122 tensor(0.0134, dtype=torch.float16)\n", - "121 tensor(0.0134, dtype=torch.float16)\n", - "147 tensor(0.0130, dtype=torch.float16)\n", - "151 tensor(0.0126, dtype=torch.float16)\n", - "132 tensor(0.0122, dtype=torch.float16)\n", - "152 tensor(0.0122, dtype=torch.float16)\n", - "154 tensor(0.0118, dtype=torch.float16)\n", - "127 tensor(0.0115, dtype=torch.float16)\n", - "163 tensor(0.0111, dtype=torch.float16)\n", - "153 tensor(0.0111, dtype=torch.float16)\n", - "161 tensor(0.0104, dtype=torch.float16)\n", - "171 tensor(0.0101, dtype=torch.float16)\n", - "144 tensor(0.0101, dtype=torch.float16)\n", - "164 tensor(0.0101, dtype=torch.float16)\n", - "162 tensor(0.0098, dtype=torch.float16)\n", - "\u001b[91m178: Guess: $144.60 Truth: $53.99 Error: $90.61 SLE: 0.95 Item: Beck/Arnley Hub & Be...\u001b[0m\n", - "350 tensor(0.5254, dtype=torch.float16)\n", - "343 tensor(0.0711, dtype=torch.float16)\n", - "348 tensor(0.0231, dtype=torch.float16)\n", - "349 tensor(0.0210, dtype=torch.float16)\n", - "346 tensor(0.0210, dtype=torch.float16)\n", - "330 tensor(0.0191, dtype=torch.float16)\n", - "345 tensor(0.0185, dtype=torch.float16)\n", - "315 tensor(0.0131, dtype=torch.float16)\n", - "338 tensor(0.0112, dtype=torch.float16)\n", - "342 tensor(0.0109, dtype=torch.float16)\n", - "344 tensor(0.0106, dtype=torch.float16)\n", - "325 tensor(0.0090, dtype=torch.float16)\n", - "320 tensor(0.0082, dtype=torch.float16)\n", - "300 tensor(0.0075, dtype=torch.float16)\n", - "340 tensor(0.0075, dtype=torch.float16)\n", - "351 tensor(0.0070, dtype=torch.float16)\n", - "347 tensor(0.0062, dtype=torch.float16)\n", - "250 tensor(0.0062, dtype=torch.float16)\n", - "326 tensor(0.0058, dtype=torch.float16)\n", - "354 tensor(0.0057, dtype=torch.float16)\n", - "\u001b[92m179: Guess: $345.62 Truth: $350.00 Error: $4.38 SLE: 0.00 Item: Eibach Pro-Kit Perfo...\u001b[0m\n", - "400 tensor(0.0403, dtype=torch.float16)\n", - "300 tensor(0.0345, dtype=torch.float16)\n", - "350 tensor(0.0277, dtype=torch.float16)\n", - "250 tensor(0.0230, dtype=torch.float16)\n", - "500 tensor(0.0185, dtype=torch.float16)\n", - "450 tensor(0.0163, dtype=torch.float16)\n", - "280 tensor(0.0144, dtype=torch.float16)\n", - "270 tensor(0.0131, dtype=torch.float16)\n", - "330 tensor(0.0127, dtype=torch.float16)\n", - "240 tensor(0.0123, dtype=torch.float16)\n", - "600 tensor(0.0116, dtype=torch.float16)\n", - "380 tensor(0.0105, dtype=torch.float16)\n", - "260 tensor(0.0105, dtype=torch.float16)\n", - "290 tensor(0.0102, dtype=torch.float16)\n", - "320 tensor(0.0099, dtype=torch.float16)\n", - "340 tensor(0.0099, dtype=torch.float16)\n", - "360 tensor(0.0096, dtype=torch.float16)\n", - "370 tensor(0.0093, dtype=torch.float16)\n", - "230 tensor(0.0087, dtype=torch.float16)\n", - "220 tensor(0.0079, dtype=torch.float16)\n", - "\u001b[92m180: Guess: $344.16 Truth: $299.95 Error: $44.21 SLE: 0.02 Item: LEGO DC Batman 1989 ...\u001b[0m\n", - "91 tensor(0.0180, dtype=torch.float16)\n", - "81 tensor(0.0180, dtype=torch.float16)\n", - "71 tensor(0.0174, dtype=torch.float16)\n", - "92 tensor(0.0169, dtype=torch.float16)\n", - "84 tensor(0.0164, dtype=torch.float16)\n", - "87 tensor(0.0164, dtype=torch.float16)\n", - "94 tensor(0.0164, dtype=torch.float16)\n", - "101 tensor(0.0159, dtype=torch.float16)\n", - "82 tensor(0.0159, dtype=torch.float16)\n", - "104 tensor(0.0154, dtype=torch.float16)\n", - "83 tensor(0.0154, dtype=torch.float16)\n", - "78 tensor(0.0149, dtype=torch.float16)\n", - "102 tensor(0.0149, dtype=torch.float16)\n", - "103 tensor(0.0144, dtype=torch.float16)\n", - "72 tensor(0.0144, dtype=torch.float16)\n", - "73 tensor(0.0140, dtype=torch.float16)\n", - "88 tensor(0.0140, dtype=torch.float16)\n", - "74 tensor(0.0140, dtype=torch.float16)\n", - "77 tensor(0.0136, dtype=torch.float16)\n", - "93 tensor(0.0131, dtype=torch.float16)\n", - "\u001b[92m181: Guess: $86.55 Truth: $94.93 Error: $8.38 SLE: 0.01 Item: Kingston Brass Resto...\u001b[0m\n", - "299 tensor(0.0583, dtype=torch.float16)\n", - "399 tensor(0.0440, dtype=torch.float16)\n", - "349 tensor(0.0388, dtype=torch.float16)\n", - "249 tensor(0.0343, dtype=torch.float16)\n", - "499 tensor(0.0276, dtype=torch.float16)\n", - "300 tensor(0.0221, dtype=torch.float16)\n", - "199 tensor(0.0215, dtype=torch.float16)\n", - "400 tensor(0.0215, dtype=torch.float16)\n", - "279 tensor(0.0178, dtype=torch.float16)\n", - "350 tensor(0.0172, dtype=torch.float16)\n", - "229 tensor(0.0167, dtype=torch.float16)\n", - "329 tensor(0.0162, dtype=torch.float16)\n", - "449 tensor(0.0157, dtype=torch.float16)\n", - "250 tensor(0.0152, dtype=torch.float16)\n", - "599 tensor(0.0134, dtype=torch.float16)\n", - "269 tensor(0.0115, dtype=torch.float16)\n", - "219 tensor(0.0111, dtype=torch.float16)\n", - "280 tensor(0.0108, dtype=torch.float16)\n", - "239 tensor(0.0098, dtype=torch.float16)\n", - "379 tensor(0.0095, dtype=torch.float16)\n", - "\u001b[92m182: Guess: $332.24 Truth: $379.00 Error: $46.76 SLE: 0.02 Item: Polk Vanishing Serie...\u001b[0m\n", - "250 tensor(0.0463, dtype=torch.float16)\n", - "300 tensor(0.0396, dtype=torch.float16)\n", - "260 tensor(0.0384, dtype=torch.float16)\n", - "240 tensor(0.0384, dtype=torch.float16)\n", - "270 tensor(0.0339, dtype=torch.float16)\n", - "290 tensor(0.0281, dtype=torch.float16)\n", - "280 tensor(0.0272, dtype=torch.float16)\n", - "330 tensor(0.0165, dtype=torch.float16)\n", - "265 tensor(0.0150, dtype=torch.float16)\n", - "350 tensor(0.0141, dtype=torch.float16)\n", - "320 tensor(0.0137, dtype=torch.float16)\n", - "249 tensor(0.0129, dtype=torch.float16)\n", - "255 tensor(0.0129, dtype=torch.float16)\n", - "310 tensor(0.0125, dtype=torch.float16)\n", - "235 tensor(0.0121, dtype=torch.float16)\n", - "245 tensor(0.0117, dtype=torch.float16)\n", - "275 tensor(0.0110, dtype=torch.float16)\n", - "259 tensor(0.0107, dtype=torch.float16)\n", - "239 tensor(0.0097, dtype=torch.float16)\n", - "400 tensor(0.0097, dtype=torch.float16)\n", - "\u001b[92m183: Guess: $276.06 Truth: $299.95 Error: $23.89 SLE: 0.01 Item: Spec-D Tuning LED Pr...\u001b[0m\n", - "15 tensor(0.0765, dtype=torch.float16)\n", - "20 tensor(0.0741, dtype=torch.float16)\n", - "10 tensor(0.0526, dtype=torch.float16)\n", - "18 tensor(0.0494, dtype=torch.float16)\n", - "12 tensor(0.0494, dtype=torch.float16)\n", - "25 tensor(0.0479, dtype=torch.float16)\n", - "14 tensor(0.0450, dtype=torch.float16)\n", - "16 tensor(0.0409, dtype=torch.float16)\n", - "17 tensor(0.0397, dtype=torch.float16)\n", - "19 tensor(0.0397, dtype=torch.float16)\n", - "22 tensor(0.0361, dtype=torch.float16)\n", - "13 tensor(0.0339, dtype=torch.float16)\n", - "24 tensor(0.0281, dtype=torch.float16)\n", - "11 tensor(0.0281, dtype=torch.float16)\n", - "21 tensor(0.0273, dtype=torch.float16)\n", - "23 tensor(0.0256, dtype=torch.float16)\n", - "9 tensor(0.0248, dtype=torch.float16)\n", - "8 tensor(0.0248, dtype=torch.float16)\n", - "30 tensor(0.0226, dtype=torch.float16)\n", - "7 tensor(0.0193, dtype=torch.float16)\n", - "\u001b[92m184: Guess: $16.75 Truth: $24.99 Error: $8.24 SLE: 0.15 Item: RICHMOND & FINCH Air...\u001b[0m\n", - "114 tensor(0.0076, dtype=torch.float16)\n", - "105 tensor(0.0076, dtype=torch.float16)\n", - "94 tensor(0.0074, dtype=torch.float16)\n", - "91 tensor(0.0074, dtype=torch.float16)\n", - "92 tensor(0.0074, dtype=torch.float16)\n", - "124 tensor(0.0072, dtype=torch.float16)\n", - "104 tensor(0.0072, dtype=torch.float16)\n", - "127 tensor(0.0072, dtype=torch.float16)\n", - "95 tensor(0.0072, dtype=torch.float16)\n", - "115 tensor(0.0072, dtype=torch.float16)\n", - "121 tensor(0.0069, dtype=torch.float16)\n", - "98 tensor(0.0069, dtype=torch.float16)\n", - "84 tensor(0.0069, dtype=torch.float16)\n", - "122 tensor(0.0069, dtype=torch.float16)\n", - "113 tensor(0.0069, dtype=torch.float16)\n", - "93 tensor(0.0069, dtype=torch.float16)\n", - "96 tensor(0.0067, dtype=torch.float16)\n", - "125 tensor(0.0067, dtype=torch.float16)\n", - "81 tensor(0.0067, dtype=torch.float16)\n", - "116 tensor(0.0067, dtype=torch.float16)\n", - "\u001b[93m185: Guess: $105.48 Truth: $41.04 Error: $64.44 SLE: 0.86 Item: LFA Industries - mm ...\u001b[0m\n", - "240 tensor(0.0241, dtype=torch.float16)\n", - "250 tensor(0.0212, dtype=torch.float16)\n", - "260 tensor(0.0188, dtype=torch.float16)\n", - "270 tensor(0.0171, dtype=torch.float16)\n", - "300 tensor(0.0165, dtype=torch.float16)\n", - "290 tensor(0.0129, dtype=torch.float16)\n", - "280 tensor(0.0129, dtype=torch.float16)\n", - "186 tensor(0.0110, dtype=torch.float16)\n", - "146 tensor(0.0107, dtype=torch.float16)\n", - "166 tensor(0.0100, dtype=torch.float16)\n", - "239 tensor(0.0097, dtype=torch.float16)\n", - "156 tensor(0.0089, dtype=torch.float16)\n", - "136 tensor(0.0089, dtype=torch.float16)\n", - "249 tensor(0.0089, dtype=torch.float16)\n", - "176 tensor(0.0089, dtype=torch.float16)\n", - "160 tensor(0.0089, dtype=torch.float16)\n", - "179 tensor(0.0089, dtype=torch.float16)\n", - "190 tensor(0.0086, dtype=torch.float16)\n", - "170 tensor(0.0086, dtype=torch.float16)\n", - "169 tensor(0.0083, dtype=torch.float16)\n", - "\u001b[93m186: Guess: $223.08 Truth: $327.90 Error: $104.82 SLE: 0.15 Item: SAUTVS LED Headlight...\u001b[0m\n", - "21 tensor(0.0346, dtype=torch.float16)\n", - "22 tensor(0.0346, dtype=torch.float16)\n", - "18 tensor(0.0325, dtype=torch.float16)\n", - "24 tensor(0.0315, dtype=torch.float16)\n", - "14 tensor(0.0305, dtype=torch.float16)\n", - "23 tensor(0.0305, dtype=torch.float16)\n", - "25 tensor(0.0296, dtype=torch.float16)\n", - "16 tensor(0.0296, dtype=torch.float16)\n", - "17 tensor(0.0287, dtype=torch.float16)\n", - "19 tensor(0.0287, dtype=torch.float16)\n", - "15 tensor(0.0278, dtype=torch.float16)\n", - "12 tensor(0.0269, dtype=torch.float16)\n", - "20 tensor(0.0261, dtype=torch.float16)\n", - "11 tensor(0.0253, dtype=torch.float16)\n", - "13 tensor(0.0253, dtype=torch.float16)\n", - "26 tensor(0.0253, dtype=torch.float16)\n", - "28 tensor(0.0238, dtype=torch.float16)\n", - "27 tensor(0.0230, dtype=torch.float16)\n", - "29 tensor(0.0203, dtype=torch.float16)\n", - "32 tensor(0.0203, dtype=torch.float16)\n", - "\u001b[92m187: Guess: $20.29 Truth: $10.99 Error: $9.30 SLE: 0.33 Item: 2 Pack Combo Womens ...\u001b[0m\n", - "15 tensor(0.5933, dtype=torch.float16)\n", - "14 tensor(0.1168, dtype=torch.float16)\n", - "16 tensor(0.0969, dtype=torch.float16)\n", - "17 tensor(0.0754, dtype=torch.float16)\n", - "13 tensor(0.0687, dtype=torch.float16)\n", - "12 tensor(0.0123, dtype=torch.float16)\n", - "18 tensor(0.0119, dtype=torch.float16)\n", - "19 tensor(0.0056, dtype=torch.float16)\n", - "10 tensor(0.0048, dtype=torch.float16)\n", - "20 tensor(0.0048, dtype=torch.float16)\n", - "11 tensor(0.0039, dtype=torch.float16)\n", - "21 tensor(0.0007, dtype=torch.float16)\n", - "22 tensor(0.0006, dtype=torch.float16)\n", - "25 tensor(0.0004, dtype=torch.float16)\n", - "5 tensor(0.0004, dtype=torch.float16)\n", - "23 tensor(0.0003, dtype=torch.float16)\n", - "9 tensor(0.0003, dtype=torch.float16)\n", - "8 tensor(0.0002, dtype=torch.float16)\n", - "4 tensor(0.0002, dtype=torch.float16)\n", - "7 tensor(0.0002, dtype=torch.float16)\n", - "\u001b[92m188: Guess: $15.00 Truth: $14.99 Error: $0.01 SLE: 0.00 Item: Arepa - Venezuelan c...\u001b[0m\n", - "41 tensor(0.0172, dtype=torch.float16)\n", - "42 tensor(0.0162, dtype=torch.float16)\n", - "31 tensor(0.0152, dtype=torch.float16)\n", - "51 tensor(0.0152, dtype=torch.float16)\n", - "34 tensor(0.0147, dtype=torch.float16)\n", - "38 tensor(0.0143, dtype=torch.float16)\n", - "44 tensor(0.0143, dtype=torch.float16)\n", - "43 tensor(0.0138, dtype=torch.float16)\n", - "32 tensor(0.0138, dtype=torch.float16)\n", - "47 tensor(0.0134, dtype=torch.float16)\n", - "61 tensor(0.0134, dtype=torch.float16)\n", - "52 tensor(0.0134, dtype=torch.float16)\n", - "48 tensor(0.0134, dtype=torch.float16)\n", - "54 tensor(0.0130, dtype=torch.float16)\n", - "39 tensor(0.0130, dtype=torch.float16)\n", - "37 tensor(0.0130, dtype=torch.float16)\n", - "53 tensor(0.0122, dtype=torch.float16)\n", - "33 tensor(0.0122, dtype=torch.float16)\n", - "49 tensor(0.0118, dtype=torch.float16)\n", - "36 tensor(0.0118, dtype=torch.float16)\n", - "\u001b[93m189: Guess: $43.11 Truth: $84.95 Error: $41.84 SLE: 0.44 Item: Schlage Lock Company...\u001b[0m\n", - "101 tensor(0.0139, dtype=torch.float16)\n", - "104 tensor(0.0135, dtype=torch.float16)\n", - "103 tensor(0.0131, dtype=torch.float16)\n", - "91 tensor(0.0131, dtype=torch.float16)\n", - "102 tensor(0.0119, dtype=torch.float16)\n", - "116 tensor(0.0119, dtype=torch.float16)\n", - "107 tensor(0.0119, dtype=torch.float16)\n", - "87 tensor(0.0115, dtype=torch.float16)\n", - "111 tensor(0.0115, dtype=torch.float16)\n", - "92 tensor(0.0115, dtype=torch.float16)\n", - "114 tensor(0.0112, dtype=torch.float16)\n", - "97 tensor(0.0112, dtype=torch.float16)\n", - "81 tensor(0.0112, dtype=torch.float16)\n", - "106 tensor(0.0112, dtype=torch.float16)\n", - "112 tensor(0.0112, dtype=torch.float16)\n", - "94 tensor(0.0108, dtype=torch.float16)\n", - "105 tensor(0.0108, dtype=torch.float16)\n", - "93 tensor(0.0105, dtype=torch.float16)\n", - "121 tensor(0.0102, dtype=torch.float16)\n", - "96 tensor(0.0102, dtype=torch.float16)\n", - "\u001b[92m190: Guess: $101.60 Truth: $111.00 Error: $9.40 SLE: 0.01 Item: Techni Mobili White ...\u001b[0m\n", - "157 tensor(0.0070, dtype=torch.float16)\n", - "156 tensor(0.0070, dtype=torch.float16)\n", - "144 tensor(0.0070, dtype=torch.float16)\n", - "166 tensor(0.0070, dtype=torch.float16)\n", - "164 tensor(0.0068, dtype=torch.float16)\n", - "184 tensor(0.0068, dtype=torch.float16)\n", - "178 tensor(0.0066, dtype=torch.float16)\n", - "158 tensor(0.0066, dtype=torch.float16)\n", - "148 tensor(0.0066, dtype=torch.float16)\n", - "162 tensor(0.0066, dtype=torch.float16)\n", - "171 tensor(0.0066, dtype=torch.float16)\n", - "168 tensor(0.0066, dtype=torch.float16)\n", - "161 tensor(0.0064, dtype=torch.float16)\n", - "186 tensor(0.0064, dtype=torch.float16)\n", - "163 tensor(0.0064, dtype=torch.float16)\n", - "250 tensor(0.0064, dtype=torch.float16)\n", - "174 tensor(0.0064, dtype=torch.float16)\n", - "176 tensor(0.0064, dtype=torch.float16)\n", - "153 tensor(0.0064, dtype=torch.float16)\n", - "172 tensor(0.0062, dtype=torch.float16)\n", - "\u001b[93m191: Guess: $169.26 Truth: $123.73 Error: $45.53 SLE: 0.10 Item: Special Lite Product...\u001b[0m\n", - "499 tensor(0.0291, dtype=torch.float16)\n", - "399 tensor(0.0257, dtype=torch.float16)\n", - "599 tensor(0.0257, dtype=torch.float16)\n", - "699 tensor(0.0166, dtype=torch.float16)\n", - "449 tensor(0.0161, dtype=torch.float16)\n", - "400 tensor(0.0151, dtype=torch.float16)\n", - "500 tensor(0.0146, dtype=torch.float16)\n", - "549 tensor(0.0146, dtype=torch.float16)\n", - "349 tensor(0.0117, dtype=torch.float16)\n", - "600 tensor(0.0117, dtype=torch.float16)\n", - "299 tensor(0.0110, dtype=torch.float16)\n", - "450 tensor(0.0107, dtype=torch.float16)\n", - "649 tensor(0.0100, dtype=torch.float16)\n", - "799 tensor(0.0089, dtype=torch.float16)\n", - "479 tensor(0.0089, dtype=torch.float16)\n", - "550 tensor(0.0083, dtype=torch.float16)\n", - "429 tensor(0.0081, dtype=torch.float16)\n", - "700 tensor(0.0078, dtype=torch.float16)\n", - "379 tensor(0.0074, dtype=torch.float16)\n", - "899 tensor(0.0074, dtype=torch.float16)\n", - "\u001b[92m192: Guess: $521.05 Truth: $557.38 Error: $36.33 SLE: 0.00 Item: Tascam Digital Porta...\u001b[0m\n", - "92 tensor(0.0095, dtype=torch.float16)\n", - "94 tensor(0.0089, dtype=torch.float16)\n", - "114 tensor(0.0089, dtype=torch.float16)\n", - "84 tensor(0.0089, dtype=torch.float16)\n", - "104 tensor(0.0089, dtype=torch.float16)\n", - "101 tensor(0.0089, dtype=torch.float16)\n", - "91 tensor(0.0086, dtype=torch.float16)\n", - "112 tensor(0.0086, dtype=torch.float16)\n", - "102 tensor(0.0083, dtype=torch.float16)\n", - "87 tensor(0.0083, dtype=torch.float16)\n", - "103 tensor(0.0083, dtype=torch.float16)\n", - "81 tensor(0.0083, dtype=torch.float16)\n", - "98 tensor(0.0081, dtype=torch.float16)\n", - "96 tensor(0.0081, dtype=torch.float16)\n", - "71 tensor(0.0081, dtype=torch.float16)\n", - "118 tensor(0.0078, dtype=torch.float16)\n", - "111 tensor(0.0078, dtype=torch.float16)\n", - "78 tensor(0.0078, dtype=torch.float16)\n", - "108 tensor(0.0078, dtype=torch.float16)\n", - "72 tensor(0.0076, dtype=torch.float16)\n", - "\u001b[92m193: Guess: $95.95 Truth: $95.55 Error: $0.40 SLE: 0.00 Item: Glow Lighting Vista ...\u001b[0m\n", - "139 tensor(0.0225, dtype=torch.float16)\n", - "159 tensor(0.0192, dtype=torch.float16)\n", - "149 tensor(0.0173, dtype=torch.float16)\n", - "169 tensor(0.0143, dtype=torch.float16)\n", - "129 tensor(0.0134, dtype=torch.float16)\n", - "144 tensor(0.0130, dtype=torch.float16)\n", - "119 tensor(0.0128, dtype=torch.float16)\n", - "174 tensor(0.0124, dtype=torch.float16)\n", - "179 tensor(0.0122, dtype=torch.float16)\n", - "175 tensor(0.0122, dtype=torch.float16)\n", - "154 tensor(0.0111, dtype=torch.float16)\n", - "145 tensor(0.0105, dtype=torch.float16)\n", - "157 tensor(0.0105, dtype=torch.float16)\n", - "155 tensor(0.0101, dtype=torch.float16)\n", - "147 tensor(0.0097, dtype=torch.float16)\n", - "164 tensor(0.0092, dtype=torch.float16)\n", - "135 tensor(0.0091, dtype=torch.float16)\n", - "165 tensor(0.0091, dtype=torch.float16)\n", - "189 tensor(0.0089, dtype=torch.float16)\n", - "124 tensor(0.0089, dtype=torch.float16)\n", - "\u001b[92m194: Guess: $152.89 Truth: $154.00 Error: $1.11 SLE: 0.00 Item: Z3 Wind Deflector, S...\u001b[0m\n", - "300 tensor(0.0359, dtype=torch.float16)\n", - "250 tensor(0.0298, dtype=torch.float16)\n", - "150 tensor(0.0218, dtype=torch.float16)\n", - "200 tensor(0.0198, dtype=torch.float16)\n", - "400 tensor(0.0181, dtype=torch.float16)\n", - "180 tensor(0.0159, dtype=torch.float16)\n", - "130 tensor(0.0136, dtype=torch.float16)\n", - "100 tensor(0.0136, dtype=torch.float16)\n", - "350 tensor(0.0132, dtype=torch.float16)\n", - "140 tensor(0.0124, dtype=torch.float16)\n", - "120 tensor(0.0120, dtype=torch.float16)\n", - "160 tensor(0.0120, dtype=torch.float16)\n", - "199 tensor(0.0113, dtype=torch.float16)\n", - "99 tensor(0.0110, dtype=torch.float16)\n", - "149 tensor(0.0106, dtype=torch.float16)\n", - "240 tensor(0.0106, dtype=torch.float16)\n", - "299 tensor(0.0097, dtype=torch.float16)\n", - "280 tensor(0.0097, dtype=torch.float16)\n", - "175 tensor(0.0094, dtype=torch.float16)\n", - "125 tensor(0.0094, dtype=torch.float16)\n", - "\u001b[92m195: Guess: $214.41 Truth: $198.99 Error: $15.42 SLE: 0.01 Item: Olympus E-20 5MP Dig...\u001b[0m\n", - "300 tensor(0.0265, dtype=torch.float16)\n", - "250 tensor(0.0265, dtype=torch.float16)\n", - "240 tensor(0.0194, dtype=torch.float16)\n", - "280 tensor(0.0161, dtype=torch.float16)\n", - "270 tensor(0.0156, dtype=torch.float16)\n", - "400 tensor(0.0146, dtype=torch.float16)\n", - "260 tensor(0.0142, dtype=torch.float16)\n", - "220 tensor(0.0129, dtype=torch.float16)\n", - "180 tensor(0.0125, dtype=torch.float16)\n", - "200 tensor(0.0125, dtype=torch.float16)\n", - "190 tensor(0.0125, dtype=torch.float16)\n", - "290 tensor(0.0121, dtype=torch.float16)\n", - "350 tensor(0.0121, dtype=torch.float16)\n", - "170 tensor(0.0117, dtype=torch.float16)\n", - "160 tensor(0.0114, dtype=torch.float16)\n", - "230 tensor(0.0114, dtype=torch.float16)\n", - "210 tensor(0.0110, dtype=torch.float16)\n", - "150 tensor(0.0089, dtype=torch.float16)\n", - "330 tensor(0.0086, dtype=torch.float16)\n", - "140 tensor(0.0086, dtype=torch.float16)\n", - "\u001b[91m196: Guess: $248.34 Truth: $430.44 Error: $182.10 SLE: 0.30 Item: PHYNEDI 1 1000 World...\u001b[0m\n", - "18 tensor(0.0338, dtype=torch.float16)\n", - "22 tensor(0.0328, dtype=torch.float16)\n", - "17 tensor(0.0318, dtype=torch.float16)\n", - "19 tensor(0.0318, dtype=torch.float16)\n", - "24 tensor(0.0308, dtype=torch.float16)\n", - "21 tensor(0.0298, dtype=torch.float16)\n", - "23 tensor(0.0298, dtype=torch.float16)\n", - "26 tensor(0.0289, dtype=torch.float16)\n", - "16 tensor(0.0280, dtype=torch.float16)\n", - "25 tensor(0.0272, dtype=torch.float16)\n", - "29 tensor(0.0263, dtype=torch.float16)\n", - "20 tensor(0.0263, dtype=torch.float16)\n", - "14 tensor(0.0255, dtype=torch.float16)\n", - "28 tensor(0.0247, dtype=torch.float16)\n", - "27 tensor(0.0247, dtype=torch.float16)\n", - "15 tensor(0.0232, dtype=torch.float16)\n", - "13 tensor(0.0218, dtype=torch.float16)\n", - "12 tensor(0.0205, dtype=torch.float16)\n", - "30 tensor(0.0205, dtype=torch.float16)\n", - "34 tensor(0.0199, dtype=torch.float16)\n", - "\u001b[92m197: Guess: $21.52 Truth: $45.67 Error: $24.15 SLE: 0.53 Item: YANGHUAN Unstable Un...\u001b[0m\n", - "300 tensor(0.0175, dtype=torch.float16)\n", - "250 tensor(0.0154, dtype=torch.float16)\n", - "400 tensor(0.0116, dtype=torch.float16)\n", - "240 tensor(0.0096, dtype=torch.float16)\n", - "350 tensor(0.0093, dtype=torch.float16)\n", - "270 tensor(0.0075, dtype=torch.float16)\n", - "280 tensor(0.0075, dtype=torch.float16)\n", - "260 tensor(0.0071, dtype=torch.float16)\n", - "200 tensor(0.0066, dtype=torch.float16)\n", - "225 tensor(0.0064, dtype=torch.float16)\n", - "299 tensor(0.0064, dtype=torch.float16)\n", - "230 tensor(0.0060, dtype=torch.float16)\n", - "220 tensor(0.0058, dtype=torch.float16)\n", - "249 tensor(0.0057, dtype=torch.float16)\n", - "290 tensor(0.0057, dtype=torch.float16)\n", - "210 tensor(0.0053, dtype=torch.float16)\n", - "500 tensor(0.0053, dtype=torch.float16)\n", - "320 tensor(0.0053, dtype=torch.float16)\n", - "330 tensor(0.0053, dtype=torch.float16)\n", - "215 tensor(0.0052, dtype=torch.float16)\n", - "\u001b[92m198: Guess: $284.81 Truth: $249.00 Error: $35.81 SLE: 0.02 Item: Interlogix NetworX T...\u001b[0m\n", - "21 tensor(0.0219, dtype=torch.float16)\n", - "23 tensor(0.0206, dtype=torch.float16)\n", - "24 tensor(0.0194, dtype=torch.float16)\n", - "31 tensor(0.0194, dtype=torch.float16)\n", - "22 tensor(0.0194, dtype=torch.float16)\n", - "26 tensor(0.0194, dtype=torch.float16)\n", - "18 tensor(0.0188, dtype=torch.float16)\n", - "28 tensor(0.0188, dtype=torch.float16)\n", - "34 tensor(0.0182, dtype=torch.float16)\n", - "14 tensor(0.0176, dtype=torch.float16)\n", - "27 tensor(0.0176, dtype=torch.float16)\n", - "17 tensor(0.0176, dtype=torch.float16)\n", - "16 tensor(0.0171, dtype=torch.float16)\n", - "29 tensor(0.0171, dtype=torch.float16)\n", - "32 tensor(0.0166, dtype=torch.float16)\n", - "41 tensor(0.0166, dtype=torch.float16)\n", - "19 tensor(0.0166, dtype=torch.float16)\n", - "13 tensor(0.0166, dtype=torch.float16)\n", - "11 tensor(0.0156, dtype=torch.float16)\n", - "12 tensor(0.0156, dtype=torch.float16)\n", - "\u001b[92m199: Guess: $23.04 Truth: $42.99 Error: $19.95 SLE: 0.36 Item: Steering Damper,Univ...\u001b[0m\n", - "122 tensor(0.0054, dtype=torch.float16)\n", - "132 tensor(0.0054, dtype=torch.float16)\n", - "124 tensor(0.0054, dtype=torch.float16)\n", - "144 tensor(0.0054, dtype=torch.float16)\n", - "131 tensor(0.0054, dtype=torch.float16)\n", - "142 tensor(0.0052, dtype=torch.float16)\n", - "123 tensor(0.0050, dtype=torch.float16)\n", - "137 tensor(0.0050, dtype=torch.float16)\n", - "138 tensor(0.0050, dtype=torch.float16)\n", - "121 tensor(0.0050, dtype=torch.float16)\n", - "141 tensor(0.0050, dtype=torch.float16)\n", - "127 tensor(0.0049, dtype=torch.float16)\n", - "153 tensor(0.0049, dtype=torch.float16)\n", - "134 tensor(0.0049, dtype=torch.float16)\n", - "152 tensor(0.0049, dtype=torch.float16)\n", - "148 tensor(0.0049, dtype=torch.float16)\n", - "113 tensor(0.0049, dtype=torch.float16)\n", - "133 tensor(0.0047, dtype=torch.float16)\n", - "114 tensor(0.0047, dtype=torch.float16)\n", - "136 tensor(0.0047, dtype=torch.float16)\n", - "\u001b[93m200: Guess: $133.23 Truth: $181.33 Error: $48.10 SLE: 0.09 Item: Amprobe TIC 410A Hot...\u001b[0m\n", - "3 tensor(0.1571, dtype=torch.float16)\n", - "4 tensor(0.1431, dtype=torch.float16)\n", - "2 tensor(0.1302, dtype=torch.float16)\n", - "5 tensor(0.1149, dtype=torch.float16)\n", - "6 tensor(0.0953, dtype=torch.float16)\n", - "7 tensor(0.0765, dtype=torch.float16)\n", - "1 tensor(0.0596, dtype=torch.float16)\n", - "8 tensor(0.0578, dtype=torch.float16)\n", - "9 tensor(0.0410, dtype=torch.float16)\n", - "10 tensor(0.0226, dtype=torch.float16)\n", - "11 tensor(0.0213, dtype=torch.float16)\n", - "12 tensor(0.0142, dtype=torch.float16)\n", - "13 tensor(0.0100, dtype=torch.float16)\n", - "14 tensor(0.0078, dtype=torch.float16)\n", - "15 tensor(0.0052, dtype=torch.float16)\n", - "16 tensor(0.0047, dtype=torch.float16)\n", - "17 tensor(0.0035, dtype=torch.float16)\n", - "18 tensor(0.0032, dtype=torch.float16)\n", - "19 tensor(0.0026, dtype=torch.float16)\n", - "21 tensor(0.0022, dtype=torch.float16)\n", - "\u001b[92m201: Guess: $5.25 Truth: $6.03 Error: $0.78 SLE: 0.01 Item: MyCableMart 3.5mm Pl...\u001b[0m\n", - "45 tensor(0.0603, dtype=torch.float16)\n", - "40 tensor(0.0566, dtype=torch.float16)\n", - "50 tensor(0.0532, dtype=torch.float16)\n", - "35 tensor(0.0455, dtype=torch.float16)\n", - "42 tensor(0.0333, dtype=torch.float16)\n", - "48 tensor(0.0285, dtype=torch.float16)\n", - "44 tensor(0.0285, dtype=torch.float16)\n", - "30 tensor(0.0267, dtype=torch.float16)\n", - "55 tensor(0.0259, dtype=torch.float16)\n", - "43 tensor(0.0251, dtype=torch.float16)\n", - "38 tensor(0.0236, dtype=torch.float16)\n", - "41 tensor(0.0229, dtype=torch.float16)\n", - "34 tensor(0.0229, dtype=torch.float16)\n", - "47 tensor(0.0215, dtype=torch.float16)\n", - "46 tensor(0.0202, dtype=torch.float16)\n", - "49 tensor(0.0184, dtype=torch.float16)\n", - "60 tensor(0.0184, dtype=torch.float16)\n", - "37 tensor(0.0178, dtype=torch.float16)\n", - "39 tensor(0.0162, dtype=torch.float16)\n", - "36 tensor(0.0157, dtype=torch.float16)\n", - "\u001b[92m202: Guess: $42.96 Truth: $29.99 Error: $12.97 SLE: 0.12 Item: OtterBox + Pop Symme...\u001b[0m\n", - "999 tensor(0.0520, dtype=torch.float16)\n", - "699 tensor(0.0352, dtype=torch.float16)\n", - "599 tensor(0.0305, dtype=torch.float16)\n", - "799 tensor(0.0305, dtype=torch.float16)\n", - "800 tensor(0.0301, dtype=torch.float16)\n", - "499 tensor(0.0257, dtype=torch.float16)\n", - "899 tensor(0.0257, dtype=torch.float16)\n", - "700 tensor(0.0234, dtype=torch.float16)\n", - "900 tensor(0.0224, dtype=torch.float16)\n", - "500 tensor(0.0220, dtype=torch.float16)\n", - "600 tensor(0.0203, dtype=torch.float16)\n", - "400 tensor(0.0142, dtype=torch.float16)\n", - "749 tensor(0.0120, dtype=torch.float16)\n", - "399 tensor(0.0118, dtype=torch.float16)\n", - "750 tensor(0.0114, dtype=torch.float16)\n", - "650 tensor(0.0101, dtype=torch.float16)\n", - "649 tensor(0.0101, dtype=torch.float16)\n", - "850 tensor(0.0084, dtype=torch.float16)\n", - "450 tensor(0.0084, dtype=torch.float16)\n", - "549 tensor(0.0082, dtype=torch.float16)\n", - "\u001b[93m203: Guess: $716.23 Truth: $899.00 Error: $182.77 SLE: 0.05 Item: Dell XPS Desktop ( I...\u001b[0m\n", - "400 tensor(0.1459, dtype=torch.float16)\n", - "500 tensor(0.1210, dtype=torch.float16)\n", - "600 tensor(0.1154, dtype=torch.float16)\n", - "700 tensor(0.1003, dtype=torch.float16)\n", - "800 tensor(0.0858, dtype=torch.float16)\n", - "300 tensor(0.0474, dtype=torch.float16)\n", - "900 tensor(0.0459, dtype=torch.float16)\n", - "450 tensor(0.0266, dtype=torch.float16)\n", - "650 tensor(0.0238, dtype=torch.float16)\n", - "350 tensor(0.0188, dtype=torch.float16)\n", - "550 tensor(0.0183, dtype=torch.float16)\n", - "750 tensor(0.0164, dtype=torch.float16)\n", - "850 tensor(0.0092, dtype=torch.float16)\n", - "430 tensor(0.0067, dtype=torch.float16)\n", - "950 tensor(0.0059, dtype=torch.float16)\n", - "580 tensor(0.0057, dtype=torch.float16)\n", - "480 tensor(0.0056, dtype=torch.float16)\n", - "250 tensor(0.0054, dtype=torch.float16)\n", - "380 tensor(0.0051, dtype=torch.float16)\n", - "599 tensor(0.0049, dtype=torch.float16)\n", - "\u001b[91m204: Guess: $574.14 Truth: $399.99 Error: $174.15 SLE: 0.13 Item: Franklin Iron Works ...\u001b[0m\n", - "4 tensor(0.0354, dtype=torch.float16)\n", - "7 tensor(0.0322, dtype=torch.float16)\n", - "11 tensor(0.0322, dtype=torch.float16)\n", - "3 tensor(0.0322, dtype=torch.float16)\n", - "6 tensor(0.0322, dtype=torch.float16)\n", - "9 tensor(0.0312, dtype=torch.float16)\n", - "8 tensor(0.0312, dtype=torch.float16)\n", - "5 tensor(0.0312, dtype=torch.float16)\n", - "14 tensor(0.0303, dtype=torch.float16)\n", - "13 tensor(0.0284, dtype=torch.float16)\n", - "12 tensor(0.0276, dtype=torch.float16)\n", - "2 tensor(0.0267, dtype=torch.float16)\n", - "21 tensor(0.0243, dtype=torch.float16)\n", - "18 tensor(0.0236, dtype=torch.float16)\n", - "16 tensor(0.0229, dtype=torch.float16)\n", - "17 tensor(0.0229, dtype=torch.float16)\n", - "10 tensor(0.0229, dtype=torch.float16)\n", - "23 tensor(0.0215, dtype=torch.float16)\n", - "19 tensor(0.0215, dtype=torch.float16)\n", - "22 tensor(0.0215, dtype=torch.float16)\n", - "\u001b[92m205: Guess: $11.18 Truth: $4.66 Error: $6.52 SLE: 0.59 Item: Avery Legal Dividers...\u001b[0m\n", - "184 tensor(0.0057, dtype=torch.float16)\n", - "144 tensor(0.0057, dtype=torch.float16)\n", - "166 tensor(0.0055, dtype=torch.float16)\n", - "142 tensor(0.0055, dtype=torch.float16)\n", - "141 tensor(0.0055, dtype=torch.float16)\n", - "250 tensor(0.0053, dtype=torch.float16)\n", - "158 tensor(0.0053, dtype=torch.float16)\n", - "168 tensor(0.0053, dtype=torch.float16)\n", - "172 tensor(0.0053, dtype=torch.float16)\n", - "157 tensor(0.0053, dtype=torch.float16)\n", - "164 tensor(0.0053, dtype=torch.float16)\n", - "152 tensor(0.0053, dtype=torch.float16)\n", - "186 tensor(0.0053, dtype=torch.float16)\n", - "173 tensor(0.0051, dtype=torch.float16)\n", - "171 tensor(0.0051, dtype=torch.float16)\n", - "176 tensor(0.0051, dtype=torch.float16)\n", - "148 tensor(0.0051, dtype=torch.float16)\n", - "153 tensor(0.0051, dtype=torch.float16)\n", - "192 tensor(0.0050, dtype=torch.float16)\n", - "161 tensor(0.0050, dtype=torch.float16)\n", - "\u001b[93m206: Guess: $167.76 Truth: $261.41 Error: $93.65 SLE: 0.19 Item: Moen 8346 Commercial...\u001b[0m\n", - "132 tensor(0.0184, dtype=torch.float16)\n", - "141 tensor(0.0184, dtype=torch.float16)\n", - "121 tensor(0.0173, dtype=torch.float16)\n", - "122 tensor(0.0162, dtype=torch.float16)\n", - "142 tensor(0.0157, dtype=torch.float16)\n", - "131 tensor(0.0157, dtype=torch.float16)\n", - "123 tensor(0.0153, dtype=torch.float16)\n", - "147 tensor(0.0153, dtype=torch.float16)\n", - "127 tensor(0.0153, dtype=torch.float16)\n", - "154 tensor(0.0143, dtype=torch.float16)\n", - "134 tensor(0.0135, dtype=torch.float16)\n", - "144 tensor(0.0135, dtype=torch.float16)\n", - "157 tensor(0.0135, dtype=torch.float16)\n", - "152 tensor(0.0130, dtype=torch.float16)\n", - "124 tensor(0.0123, dtype=torch.float16)\n", - "137 tensor(0.0123, dtype=torch.float16)\n", - "111 tensor(0.0123, dtype=torch.float16)\n", - "153 tensor(0.0119, dtype=torch.float16)\n", - "148 tensor(0.0119, dtype=torch.float16)\n", - "151 tensor(0.0119, dtype=torch.float16)\n", - "\u001b[92m207: Guess: $137.01 Truth: $136.97 Error: $0.04 SLE: 0.00 Item: Carlisle Versa Trail...\u001b[0m\n", - "149 tensor(0.0303, dtype=torch.float16)\n", - "169 tensor(0.0285, dtype=torch.float16)\n", - "129 tensor(0.0251, dtype=torch.float16)\n", - "199 tensor(0.0251, dtype=torch.float16)\n", - "159 tensor(0.0236, dtype=torch.float16)\n", - "179 tensor(0.0229, dtype=torch.float16)\n", - "139 tensor(0.0215, dtype=torch.float16)\n", - "99 tensor(0.0215, dtype=torch.float16)\n", - "249 tensor(0.0208, dtype=torch.float16)\n", - "119 tensor(0.0184, dtype=torch.float16)\n", - "189 tensor(0.0168, dtype=torch.float16)\n", - "229 tensor(0.0157, dtype=torch.float16)\n", - "299 tensor(0.0148, dtype=torch.float16)\n", - "219 tensor(0.0148, dtype=torch.float16)\n", - "109 tensor(0.0130, dtype=torch.float16)\n", - "250 tensor(0.0115, dtype=torch.float16)\n", - "239 tensor(0.0105, dtype=torch.float16)\n", - "168 tensor(0.0105, dtype=torch.float16)\n", - "148 tensor(0.0098, dtype=torch.float16)\n", - "128 tensor(0.0095, dtype=torch.float16)\n", - "\u001b[91m208: Guess: $174.33 Truth: $79.00 Error: $95.33 SLE: 0.62 Item: SUNWAYFOTO 44mm Trip...\u001b[0m\n", - "300 tensor(0.0126, dtype=torch.float16)\n", - "400 tensor(0.0122, dtype=torch.float16)\n", - "250 tensor(0.0089, dtype=torch.float16)\n", - "240 tensor(0.0084, dtype=torch.float16)\n", - "500 tensor(0.0081, dtype=torch.float16)\n", - "350 tensor(0.0079, dtype=torch.float16)\n", - "450 tensor(0.0063, dtype=torch.float16)\n", - "280 tensor(0.0063, dtype=torch.float16)\n", - "600 tensor(0.0060, dtype=torch.float16)\n", - "360 tensor(0.0060, dtype=torch.float16)\n", - "299 tensor(0.0058, dtype=torch.float16)\n", - "270 tensor(0.0058, dtype=torch.float16)\n", - "330 tensor(0.0056, dtype=torch.float16)\n", - "320 tensor(0.0054, dtype=torch.float16)\n", - "260 tensor(0.0054, dtype=torch.float16)\n", - "290 tensor(0.0051, dtype=torch.float16)\n", - "340 tensor(0.0049, dtype=torch.float16)\n", - "399 tensor(0.0048, dtype=torch.float16)\n", - "480 tensor(0.0045, dtype=torch.float16)\n", - "249 tensor(0.0044, dtype=torch.float16)\n", - "\u001b[93m209: Guess: $347.23 Truth: $444.99 Error: $97.76 SLE: 0.06 Item: NanoBeam AC 4 Units ...\u001b[0m\n", - "500 tensor(0.0072, dtype=torch.float16)\n", - "400 tensor(0.0057, dtype=torch.float16)\n", - "600 tensor(0.0056, dtype=torch.float16)\n", - "450 tensor(0.0052, dtype=torch.float16)\n", - "550 tensor(0.0047, dtype=torch.float16)\n", - "650 tensor(0.0044, dtype=torch.float16)\n", - "520 tensor(0.0044, dtype=torch.float16)\n", - "480 tensor(0.0042, dtype=torch.float16)\n", - "535 tensor(0.0042, dtype=torch.float16)\n", - "599 tensor(0.0041, dtype=torch.float16)\n", - "420 tensor(0.0041, dtype=torch.float16)\n", - "530 tensor(0.0041, dtype=torch.float16)\n", - "560 tensor(0.0041, dtype=torch.float16)\n", - "525 tensor(0.0041, dtype=torch.float16)\n", - "510 tensor(0.0041, dtype=torch.float16)\n", - "499 tensor(0.0041, dtype=torch.float16)\n", - "580 tensor(0.0039, dtype=torch.float16)\n", - "700 tensor(0.0039, dtype=torch.float16)\n", - "545 tensor(0.0039, dtype=torch.float16)\n", - "440 tensor(0.0037, dtype=torch.float16)\n", - "\u001b[93m210: Guess: $526.83 Truth: $411.94 Error: $114.89 SLE: 0.06 Item: WULF 4 Front 2 Rear ...\u001b[0m\n", - "121 tensor(0.0139, dtype=torch.float16)\n", - "122 tensor(0.0127, dtype=torch.float16)\n", - "124 tensor(0.0127, dtype=torch.float16)\n", - "112 tensor(0.0123, dtype=torch.float16)\n", - "104 tensor(0.0119, dtype=torch.float16)\n", - "111 tensor(0.0119, dtype=torch.float16)\n", - "123 tensor(0.0119, dtype=torch.float16)\n", - "114 tensor(0.0115, dtype=torch.float16)\n", - "132 tensor(0.0115, dtype=torch.float16)\n", - "144 tensor(0.0112, dtype=torch.float16)\n", - "116 tensor(0.0112, dtype=torch.float16)\n", - "118 tensor(0.0112, dtype=torch.float16)\n", - "142 tensor(0.0112, dtype=torch.float16)\n", - "136 tensor(0.0112, dtype=torch.float16)\n", - "103 tensor(0.0112, dtype=torch.float16)\n", - "107 tensor(0.0108, dtype=torch.float16)\n", - "102 tensor(0.0105, dtype=torch.float16)\n", - "101 tensor(0.0105, dtype=torch.float16)\n", - "134 tensor(0.0102, dtype=torch.float16)\n", - "117 tensor(0.0102, dtype=torch.float16)\n", - "\u001b[92m211: Guess: $119.21 Truth: $148.40 Error: $29.19 SLE: 0.05 Item: Alera ALEVABFMC Vale...\u001b[0m\n", - "250 tensor(0.0099, dtype=torch.float16)\n", - "300 tensor(0.0096, dtype=torch.float16)\n", - "144 tensor(0.0075, dtype=torch.float16)\n", - "240 tensor(0.0073, dtype=torch.float16)\n", - "150 tensor(0.0070, dtype=torch.float16)\n", - "120 tensor(0.0068, dtype=torch.float16)\n", - "135 tensor(0.0066, dtype=torch.float16)\n", - "115 tensor(0.0066, dtype=torch.float16)\n", - "148 tensor(0.0064, dtype=torch.float16)\n", - "200 tensor(0.0064, dtype=torch.float16)\n", - "98 tensor(0.0062, dtype=torch.float16)\n", - "145 tensor(0.0062, dtype=torch.float16)\n", - "180 tensor(0.0062, dtype=torch.float16)\n", - "165 tensor(0.0062, dtype=torch.float16)\n", - "175 tensor(0.0060, dtype=torch.float16)\n", - "140 tensor(0.0060, dtype=torch.float16)\n", - "104 tensor(0.0060, dtype=torch.float16)\n", - "160 tensor(0.0060, dtype=torch.float16)\n", - "108 tensor(0.0060, dtype=torch.float16)\n", - "128 tensor(0.0060, dtype=torch.float16)\n", - "\u001b[93m212: Guess: $166.50 Truth: $244.99 Error: $78.49 SLE: 0.15 Item: YU-GI-OH! Ignition A...\u001b[0m\n", - "115 tensor(0.0113, dtype=torch.float16)\n", - "135 tensor(0.0106, dtype=torch.float16)\n", - "130 tensor(0.0100, dtype=torch.float16)\n", - "165 tensor(0.0100, dtype=torch.float16)\n", - "145 tensor(0.0100, dtype=torch.float16)\n", - "140 tensor(0.0097, dtype=torch.float16)\n", - "125 tensor(0.0094, dtype=torch.float16)\n", - "136 tensor(0.0094, dtype=torch.float16)\n", - "120 tensor(0.0091, dtype=torch.float16)\n", - "150 tensor(0.0091, dtype=torch.float16)\n", - "116 tensor(0.0088, dtype=torch.float16)\n", - "170 tensor(0.0088, dtype=torch.float16)\n", - "124 tensor(0.0088, dtype=torch.float16)\n", - "155 tensor(0.0088, dtype=torch.float16)\n", - "160 tensor(0.0085, dtype=torch.float16)\n", - "110 tensor(0.0085, dtype=torch.float16)\n", - "144 tensor(0.0085, dtype=torch.float16)\n", - "128 tensor(0.0083, dtype=torch.float16)\n", - "175 tensor(0.0083, dtype=torch.float16)\n", - "105 tensor(0.0080, dtype=torch.float16)\n", - "\u001b[93m213: Guess: $137.27 Truth: $86.50 Error: $50.77 SLE: 0.21 Item: 48 x 36 Extra-Large ...\u001b[0m\n", - "139 tensor(0.0119, dtype=torch.float16)\n", - "124 tensor(0.0115, dtype=torch.float16)\n", - "125 tensor(0.0112, dtype=torch.float16)\n", - "129 tensor(0.0112, dtype=torch.float16)\n", - "135 tensor(0.0105, dtype=torch.float16)\n", - "145 tensor(0.0105, dtype=torch.float16)\n", - "250 tensor(0.0102, dtype=torch.float16)\n", - "115 tensor(0.0102, dtype=torch.float16)\n", - "144 tensor(0.0098, dtype=torch.float16)\n", - "114 tensor(0.0098, dtype=torch.float16)\n", - "149 tensor(0.0098, dtype=torch.float16)\n", - "150 tensor(0.0093, dtype=torch.float16)\n", - "128 tensor(0.0090, dtype=torch.float16)\n", - "138 tensor(0.0090, dtype=torch.float16)\n", - "127 tensor(0.0087, dtype=torch.float16)\n", - "123 tensor(0.0087, dtype=torch.float16)\n", - "136 tensor(0.0087, dtype=torch.float16)\n", - "132 tensor(0.0087, dtype=torch.float16)\n", - "109 tensor(0.0084, dtype=torch.float16)\n", - "175 tensor(0.0084, dtype=torch.float16)\n", - "\u001b[91m214: Guess: $139.41 Truth: $297.95 Error: $158.54 SLE: 0.57 Item: Dell Latitude D620 R...\u001b[0m\n", - "399 tensor(0.0421, dtype=torch.float16)\n", - "400 tensor(0.0401, dtype=torch.float16)\n", - "499 tensor(0.0389, dtype=torch.float16)\n", - "449 tensor(0.0354, dtype=torch.float16)\n", - "450 tensor(0.0276, dtype=torch.float16)\n", - "500 tensor(0.0267, dtype=torch.float16)\n", - "439 tensor(0.0240, dtype=torch.float16)\n", - "429 tensor(0.0229, dtype=torch.float16)\n", - "479 tensor(0.0225, dtype=torch.float16)\n", - "430 tensor(0.0211, dtype=torch.float16)\n", - "419 tensor(0.0199, dtype=torch.float16)\n", - "469 tensor(0.0196, dtype=torch.float16)\n", - "459 tensor(0.0173, dtype=torch.float16)\n", - "440 tensor(0.0157, dtype=torch.float16)\n", - "489 tensor(0.0148, dtype=torch.float16)\n", - "480 tensor(0.0141, dtype=torch.float16)\n", - "470 tensor(0.0130, dtype=torch.float16)\n", - "389 tensor(0.0128, dtype=torch.float16)\n", - "420 tensor(0.0126, dtype=torch.float16)\n", - "460 tensor(0.0122, dtype=torch.float16)\n", - "\u001b[92m215: Guess: $446.73 Truth: $399.99 Error: $46.74 SLE: 0.01 Item: acer Aspire 5 Laptop...\u001b[0m\n", - "300 tensor(0.0058, dtype=torch.float16)\n", - "250 tensor(0.0051, dtype=torch.float16)\n", - "240 tensor(0.0049, dtype=torch.float16)\n", - "400 tensor(0.0046, dtype=torch.float16)\n", - "270 tensor(0.0042, dtype=torch.float16)\n", - "280 tensor(0.0040, dtype=torch.float16)\n", - "350 tensor(0.0038, dtype=torch.float16)\n", - "260 tensor(0.0038, dtype=torch.float16)\n", - "290 tensor(0.0035, dtype=torch.float16)\n", - "320 tensor(0.0034, dtype=torch.float16)\n", - "330 tensor(0.0033, dtype=torch.float16)\n", - "310 tensor(0.0032, dtype=torch.float16)\n", - "225 tensor(0.0032, dtype=torch.float16)\n", - "238 tensor(0.0032, dtype=torch.float16)\n", - "360 tensor(0.0032, dtype=torch.float16)\n", - "299 tensor(0.0032, dtype=torch.float16)\n", - "216 tensor(0.0031, dtype=torch.float16)\n", - "218 tensor(0.0031, dtype=torch.float16)\n", - "198 tensor(0.0031, dtype=torch.float16)\n", - "229 tensor(0.0031, dtype=torch.float16)\n", - "\u001b[91m216: Guess: $281.38 Truth: $599.00 Error: $317.62 SLE: 0.57 Item: Elk 30 by 6-Inch Viv...\u001b[0m\n", - "300 tensor(0.0232, dtype=torch.float16)\n", - "250 tensor(0.0211, dtype=torch.float16)\n", - "100 tensor(0.0204, dtype=torch.float16)\n", - "150 tensor(0.0150, dtype=torch.float16)\n", - "80 tensor(0.0141, dtype=torch.float16)\n", - "75 tensor(0.0141, dtype=torch.float16)\n", - "90 tensor(0.0136, dtype=torch.float16)\n", - "200 tensor(0.0132, dtype=torch.float16)\n", - "95 tensor(0.0120, dtype=torch.float16)\n", - "85 tensor(0.0120, dtype=torch.float16)\n", - "400 tensor(0.0117, dtype=torch.float16)\n", - "70 tensor(0.0106, dtype=torch.float16)\n", - "120 tensor(0.0106, dtype=torch.float16)\n", - "125 tensor(0.0106, dtype=torch.float16)\n", - "65 tensor(0.0106, dtype=torch.float16)\n", - "60 tensor(0.0100, dtype=torch.float16)\n", - "98 tensor(0.0094, dtype=torch.float16)\n", - "135 tensor(0.0091, dtype=torch.float16)\n", - "115 tensor(0.0091, dtype=torch.float16)\n", - "140 tensor(0.0091, dtype=torch.float16)\n", - "\u001b[93m217: Guess: $148.36 Truth: $105.99 Error: $42.37 SLE: 0.11 Item: Barbie Top Model Dol...\u001b[0m\n", - "500 tensor(0.0177, dtype=torch.float16)\n", - "600 tensor(0.0134, dtype=torch.float16)\n", - "400 tensor(0.0118, dtype=torch.float16)\n", - "700 tensor(0.0099, dtype=torch.float16)\n", - "450 tensor(0.0095, dtype=torch.float16)\n", - "650 tensor(0.0079, dtype=torch.float16)\n", - "550 tensor(0.0079, dtype=torch.float16)\n", - "800 tensor(0.0073, dtype=torch.float16)\n", - "499 tensor(0.0073, dtype=torch.float16)\n", - "599 tensor(0.0063, dtype=torch.float16)\n", - "580 tensor(0.0055, dtype=torch.float16)\n", - "480 tensor(0.0054, dtype=torch.float16)\n", - "699 tensor(0.0051, dtype=torch.float16)\n", - "649 tensor(0.0050, dtype=torch.float16)\n", - "440 tensor(0.0048, dtype=torch.float16)\n", - "449 tensor(0.0047, dtype=torch.float16)\n", - "750 tensor(0.0047, dtype=torch.float16)\n", - "549 tensor(0.0046, dtype=torch.float16)\n", - "399 tensor(0.0046, dtype=torch.float16)\n", - "530 tensor(0.0046, dtype=torch.float16)\n", - "\u001b[92m218: Guess: $558.66 Truth: $689.00 Error: $130.34 SLE: 0.04 Item: Danby Designer 20-In...\u001b[0m\n", - "500 tensor(0.0047, dtype=torch.float16)\n", - "400 tensor(0.0044, dtype=torch.float16)\n", - "600 tensor(0.0038, dtype=torch.float16)\n", - "450 tensor(0.0035, dtype=torch.float16)\n", - "499 tensor(0.0034, dtype=torch.float16)\n", - "700 tensor(0.0034, dtype=torch.float16)\n", - "800 tensor(0.0032, dtype=torch.float16)\n", - "599 tensor(0.0032, dtype=torch.float16)\n", - "650 tensor(0.0031, dtype=torch.float16)\n", - "495 tensor(0.0030, dtype=torch.float16)\n", - "350 tensor(0.0029, dtype=torch.float16)\n", - "399 tensor(0.0029, dtype=torch.float16)\n", - "440 tensor(0.0029, dtype=torch.float16)\n", - "429 tensor(0.0028, dtype=torch.float16)\n", - "580 tensor(0.0027, dtype=torch.float16)\n", - "420 tensor(0.0027, dtype=torch.float16)\n", - "550 tensor(0.0027, dtype=torch.float16)\n", - "480 tensor(0.0027, dtype=torch.float16)\n", - "649 tensor(0.0027, dtype=torch.float16)\n", - "430 tensor(0.0027, dtype=torch.float16)\n", - "\u001b[93m219: Guess: $521.73 Truth: $404.99 Error: $116.74 SLE: 0.06 Item: FixtureDisplays® Met...\u001b[0m\n", - "192 tensor(0.0091, dtype=torch.float16)\n", - "193 tensor(0.0088, dtype=torch.float16)\n", - "171 tensor(0.0085, dtype=torch.float16)\n", - "206 tensor(0.0085, dtype=torch.float16)\n", - "172 tensor(0.0082, dtype=torch.float16)\n", - "186 tensor(0.0080, dtype=torch.float16)\n", - "173 tensor(0.0077, dtype=torch.float16)\n", - "236 tensor(0.0075, dtype=torch.float16)\n", - "202 tensor(0.0075, dtype=torch.float16)\n", - "174 tensor(0.0075, dtype=torch.float16)\n", - "176 tensor(0.0075, dtype=torch.float16)\n", - "163 tensor(0.0075, dtype=torch.float16)\n", - "203 tensor(0.0075, dtype=torch.float16)\n", - "178 tensor(0.0073, dtype=torch.float16)\n", - "196 tensor(0.0073, dtype=torch.float16)\n", - "201 tensor(0.0073, dtype=torch.float16)\n", - "233 tensor(0.0073, dtype=torch.float16)\n", - "184 tensor(0.0071, dtype=torch.float16)\n", - "191 tensor(0.0071, dtype=torch.float16)\n", - "197 tensor(0.0071, dtype=torch.float16)\n", - "\u001b[92m220: Guess: $191.14 Truth: $207.76 Error: $16.62 SLE: 0.01 Item: ACDelco GM Original ...\u001b[0m\n", - "141 tensor(0.0141, dtype=torch.float16)\n", - "172 tensor(0.0128, dtype=torch.float16)\n", - "152 tensor(0.0124, dtype=torch.float16)\n", - "142 tensor(0.0124, dtype=torch.float16)\n", - "147 tensor(0.0124, dtype=torch.float16)\n", - "151 tensor(0.0121, dtype=torch.float16)\n", - "154 tensor(0.0121, dtype=torch.float16)\n", - "157 tensor(0.0121, dtype=torch.float16)\n", - "162 tensor(0.0117, dtype=torch.float16)\n", - "163 tensor(0.0117, dtype=torch.float16)\n", - "171 tensor(0.0117, dtype=torch.float16)\n", - "153 tensor(0.0113, dtype=torch.float16)\n", - "164 tensor(0.0113, dtype=torch.float16)\n", - "178 tensor(0.0110, dtype=torch.float16)\n", - "161 tensor(0.0110, dtype=torch.float16)\n", - "166 tensor(0.0110, dtype=torch.float16)\n", - "173 tensor(0.0110, dtype=torch.float16)\n", - "131 tensor(0.0106, dtype=torch.float16)\n", - "174 tensor(0.0106, dtype=torch.float16)\n", - "167 tensor(0.0106, dtype=torch.float16)\n", - "\u001b[92m221: Guess: $158.58 Truth: $171.82 Error: $13.24 SLE: 0.01 Item: EBC Premium Street B...\u001b[0m\n", - "300 tensor(0.0344, dtype=torch.float16)\n", - "250 tensor(0.0236, dtype=torch.float16)\n", - "350 tensor(0.0209, dtype=torch.float16)\n", - "280 tensor(0.0196, dtype=torch.float16)\n", - "400 tensor(0.0196, dtype=torch.float16)\n", - "330 tensor(0.0190, dtype=torch.float16)\n", - "270 tensor(0.0184, dtype=torch.float16)\n", - "260 tensor(0.0168, dtype=torch.float16)\n", - "240 tensor(0.0163, dtype=torch.float16)\n", - "320 tensor(0.0157, dtype=torch.float16)\n", - "290 tensor(0.0139, dtype=torch.float16)\n", - "360 tensor(0.0135, dtype=torch.float16)\n", - "310 tensor(0.0126, dtype=torch.float16)\n", - "340 tensor(0.0126, dtype=torch.float16)\n", - "380 tensor(0.0108, dtype=torch.float16)\n", - "370 tensor(0.0102, dtype=torch.float16)\n", - "450 tensor(0.0082, dtype=torch.float16)\n", - "275 tensor(0.0074, dtype=torch.float16)\n", - "390 tensor(0.0072, dtype=torch.float16)\n", - "315 tensor(0.0072, dtype=torch.float16)\n", - "\u001b[92m222: Guess: $315.56 Truth: $293.24 Error: $22.32 SLE: 0.01 Item: FXR Men's Boost FX J...\u001b[0m\n", - "400 tensor(0.0875, dtype=torch.float16)\n", - "450 tensor(0.0530, dtype=torch.float16)\n", - "500 tensor(0.0461, dtype=torch.float16)\n", - "350 tensor(0.0293, dtype=torch.float16)\n", - "425 tensor(0.0258, dtype=torch.float16)\n", - "430 tensor(0.0247, dtype=torch.float16)\n", - "375 tensor(0.0239, dtype=torch.float16)\n", - "460 tensor(0.0228, dtype=torch.float16)\n", - "410 tensor(0.0225, dtype=torch.float16)\n", - "420 tensor(0.0208, dtype=torch.float16)\n", - "380 tensor(0.0204, dtype=torch.float16)\n", - "360 tensor(0.0204, dtype=torch.float16)\n", - "390 tensor(0.0204, dtype=torch.float16)\n", - "370 tensor(0.0201, dtype=torch.float16)\n", - "440 tensor(0.0198, dtype=torch.float16)\n", - "480 tensor(0.0181, dtype=torch.float16)\n", - "300 tensor(0.0172, dtype=torch.float16)\n", - "435 tensor(0.0157, dtype=torch.float16)\n", - "470 tensor(0.0152, dtype=torch.float16)\n", - "340 tensor(0.0143, dtype=torch.float16)\n", - "\u001b[92m223: Guess: $414.55 Truth: $374.95 Error: $39.60 SLE: 0.01 Item: SuperATV Scratch Res...\u001b[0m\n", - "84 tensor(0.0776, dtype=torch.float16)\n", - "114 tensor(0.0729, dtype=torch.float16)\n", - "115 tensor(0.0685, dtype=torch.float16)\n", - "102 tensor(0.0551, dtype=torch.float16)\n", - "104 tensor(0.0367, dtype=torch.float16)\n", - "105 tensor(0.0334, dtype=torch.float16)\n", - "112 tensor(0.0314, dtype=torch.float16)\n", - "85 tensor(0.0314, dtype=torch.float16)\n", - "95 tensor(0.0295, dtype=torch.float16)\n", - "94 tensor(0.0268, dtype=torch.float16)\n", - "92 tensor(0.0237, dtype=torch.float16)\n", - "107 tensor(0.0216, dtype=torch.float16)\n", - "89 tensor(0.0216, dtype=torch.float16)\n", - "119 tensor(0.0203, dtype=torch.float16)\n", - "88 tensor(0.0203, dtype=torch.float16)\n", - "79 tensor(0.0190, dtype=torch.float16)\n", - "75 tensor(0.0173, dtype=torch.float16)\n", - "109 tensor(0.0158, dtype=torch.float16)\n", - "103 tensor(0.0153, dtype=torch.float16)\n", - "96 tensor(0.0153, dtype=torch.float16)\n", - "\u001b[92m224: Guess: $99.98 Truth: $111.99 Error: $12.01 SLE: 0.01 Item: SBU 3 Layer All Weat...\u001b[0m\n", - "43 tensor(0.0212, dtype=torch.float16)\n", - "36 tensor(0.0212, dtype=torch.float16)\n", - "41 tensor(0.0193, dtype=torch.float16)\n", - "46 tensor(0.0193, dtype=torch.float16)\n", - "33 tensor(0.0193, dtype=torch.float16)\n", - "37 tensor(0.0193, dtype=torch.float16)\n", - "34 tensor(0.0187, dtype=torch.float16)\n", - "39 tensor(0.0181, dtype=torch.float16)\n", - "38 tensor(0.0181, dtype=torch.float16)\n", - "31 tensor(0.0175, dtype=torch.float16)\n", - "44 tensor(0.0175, dtype=torch.float16)\n", - "47 tensor(0.0170, dtype=torch.float16)\n", - "53 tensor(0.0165, dtype=torch.float16)\n", - "42 tensor(0.0165, dtype=torch.float16)\n", - "29 tensor(0.0165, dtype=torch.float16)\n", - "32 tensor(0.0160, dtype=torch.float16)\n", - "56 tensor(0.0155, dtype=torch.float16)\n", - "28 tensor(0.0150, dtype=torch.float16)\n", - "49 tensor(0.0150, dtype=torch.float16)\n", - "48 tensor(0.0150, dtype=torch.float16)\n", - "\u001b[92m225: Guess: $40.13 Truth: $42.99 Error: $2.86 SLE: 0.00 Item: 2 Pack Outdoor Broch...\u001b[0m\n", - "141 tensor(0.0325, dtype=torch.float16)\n", - "131 tensor(0.0253, dtype=torch.float16)\n", - "147 tensor(0.0245, dtype=torch.float16)\n", - "123 tensor(0.0245, dtype=torch.float16)\n", - "122 tensor(0.0238, dtype=torch.float16)\n", - "121 tensor(0.0230, dtype=torch.float16)\n", - "142 tensor(0.0223, dtype=torch.float16)\n", - "132 tensor(0.0223, dtype=torch.float16)\n", - "151 tensor(0.0191, dtype=torch.float16)\n", - "127 tensor(0.0185, dtype=torch.float16)\n", - "152 tensor(0.0179, dtype=torch.float16)\n", - "154 tensor(0.0174, dtype=torch.float16)\n", - "134 tensor(0.0168, dtype=torch.float16)\n", - "144 tensor(0.0168, dtype=torch.float16)\n", - "157 tensor(0.0163, dtype=torch.float16)\n", - "153 tensor(0.0163, dtype=torch.float16)\n", - "124 tensor(0.0158, dtype=torch.float16)\n", - "137 tensor(0.0153, dtype=torch.float16)\n", - "148 tensor(0.0153, dtype=torch.float16)\n", - "143 tensor(0.0144, dtype=torch.float16)\n", - "\u001b[92m226: Guess: $138.27 Truth: $116.71 Error: $21.56 SLE: 0.03 Item: Monroe Shocks & Stru...\u001b[0m\n", - "144 tensor(0.0080, dtype=torch.float16)\n", - "184 tensor(0.0078, dtype=torch.float16)\n", - "164 tensor(0.0078, dtype=torch.float16)\n", - "166 tensor(0.0075, dtype=torch.float16)\n", - "168 tensor(0.0073, dtype=torch.float16)\n", - "178 tensor(0.0073, dtype=torch.float16)\n", - "158 tensor(0.0073, dtype=torch.float16)\n", - "156 tensor(0.0071, dtype=torch.float16)\n", - "152 tensor(0.0071, dtype=torch.float16)\n", - "148 tensor(0.0071, dtype=torch.float16)\n", - "142 tensor(0.0071, dtype=torch.float16)\n", - "154 tensor(0.0069, dtype=torch.float16)\n", - "165 tensor(0.0069, dtype=torch.float16)\n", - "198 tensor(0.0069, dtype=torch.float16)\n", - "175 tensor(0.0069, dtype=torch.float16)\n", - "141 tensor(0.0069, dtype=torch.float16)\n", - "153 tensor(0.0069, dtype=torch.float16)\n", - "171 tensor(0.0069, dtype=torch.float16)\n", - "176 tensor(0.0069, dtype=torch.float16)\n", - "157 tensor(0.0069, dtype=torch.float16)\n", - "\u001b[93m227: Guess: $162.49 Truth: $118.61 Error: $43.88 SLE: 0.10 Item: Elements of Design M...\u001b[0m\n", - "123 tensor(0.0146, dtype=torch.float16)\n", - "141 tensor(0.0146, dtype=torch.float16)\n", - "121 tensor(0.0141, dtype=torch.float16)\n", - "131 tensor(0.0137, dtype=torch.float16)\n", - "122 tensor(0.0133, dtype=torch.float16)\n", - "101 tensor(0.0133, dtype=torch.float16)\n", - "142 tensor(0.0129, dtype=torch.float16)\n", - "111 tensor(0.0117, dtype=torch.float16)\n", - "152 tensor(0.0114, dtype=torch.float16)\n", - "132 tensor(0.0114, dtype=torch.float16)\n", - "151 tensor(0.0114, dtype=torch.float16)\n", - "147 tensor(0.0114, dtype=torch.float16)\n", - "127 tensor(0.0114, dtype=torch.float16)\n", - "102 tensor(0.0110, dtype=torch.float16)\n", - "103 tensor(0.0110, dtype=torch.float16)\n", - "157 tensor(0.0103, dtype=torch.float16)\n", - "107 tensor(0.0103, dtype=torch.float16)\n", - "91 tensor(0.0100, dtype=torch.float16)\n", - "153 tensor(0.0097, dtype=torch.float16)\n", - "114 tensor(0.0097, dtype=torch.float16)\n", - "\u001b[92m228: Guess: $126.49 Truth: $147.12 Error: $20.63 SLE: 0.02 Item: GM Genuine Parts Air...\u001b[0m\n", - "130 tensor(0.0445, dtype=torch.float16)\n", - "140 tensor(0.0392, dtype=torch.float16)\n", - "170 tensor(0.0357, dtype=torch.float16)\n", - "160 tensor(0.0357, dtype=torch.float16)\n", - "120 tensor(0.0346, dtype=torch.float16)\n", - "150 tensor(0.0325, dtype=torch.float16)\n", - "110 tensor(0.0325, dtype=torch.float16)\n", - "180 tensor(0.0278, dtype=torch.float16)\n", - "100 tensor(0.0270, dtype=torch.float16)\n", - "190 tensor(0.0217, dtype=torch.float16)\n", - "250 tensor(0.0180, dtype=torch.float16)\n", - "200 tensor(0.0180, dtype=torch.float16)\n", - "90 tensor(0.0169, dtype=torch.float16)\n", - "220 tensor(0.0164, dtype=torch.float16)\n", - "240 tensor(0.0140, dtype=torch.float16)\n", - "210 tensor(0.0131, dtype=torch.float16)\n", - "230 tensor(0.0127, dtype=torch.float16)\n", - "80 tensor(0.0112, dtype=torch.float16)\n", - "300 tensor(0.0090, dtype=torch.float16)\n", - "136 tensor(0.0088, dtype=torch.float16)\n", - "\u001b[92m229: Guess: $158.83 Truth: $119.99 Error: $38.84 SLE: 0.08 Item: Baseus USB C Docking...\u001b[0m\n", - "300 tensor(0.0104, dtype=torch.float16)\n", - "400 tensor(0.0101, dtype=torch.float16)\n", - "350 tensor(0.0096, dtype=torch.float16)\n", - "299 tensor(0.0090, dtype=torch.float16)\n", - "330 tensor(0.0081, dtype=torch.float16)\n", - "399 tensor(0.0077, dtype=torch.float16)\n", - "360 tensor(0.0077, dtype=torch.float16)\n", - "329 tensor(0.0072, dtype=torch.float16)\n", - "349 tensor(0.0068, dtype=torch.float16)\n", - "320 tensor(0.0067, dtype=torch.float16)\n", - "250 tensor(0.0066, dtype=torch.float16)\n", - "290 tensor(0.0066, dtype=torch.float16)\n", - "310 tensor(0.0065, dtype=torch.float16)\n", - "340 tensor(0.0064, dtype=torch.float16)\n", - "369 tensor(0.0063, dtype=torch.float16)\n", - "270 tensor(0.0062, dtype=torch.float16)\n", - "319 tensor(0.0062, dtype=torch.float16)\n", - "289 tensor(0.0062, dtype=torch.float16)\n", - "370 tensor(0.0061, dtype=torch.float16)\n", - "280 tensor(0.0060, dtype=torch.float16)\n", - "\u001b[92m230: Guess: $328.37 Truth: $369.98 Error: $41.61 SLE: 0.01 Item: Whitehall™ Personali...\u001b[0m\n", - "250 tensor(0.0134, dtype=torch.float16)\n", - "240 tensor(0.0130, dtype=torch.float16)\n", - "270 tensor(0.0104, dtype=torch.float16)\n", - "300 tensor(0.0095, dtype=torch.float16)\n", - "260 tensor(0.0095, dtype=torch.float16)\n", - "215 tensor(0.0089, dtype=torch.float16)\n", - "238 tensor(0.0084, dtype=torch.float16)\n", - "280 tensor(0.0079, dtype=torch.float16)\n", - "237 tensor(0.0076, dtype=torch.float16)\n", - "206 tensor(0.0074, dtype=torch.float16)\n", - "216 tensor(0.0074, dtype=torch.float16)\n", - "290 tensor(0.0074, dtype=torch.float16)\n", - "225 tensor(0.0074, dtype=torch.float16)\n", - "255 tensor(0.0072, dtype=torch.float16)\n", - "198 tensor(0.0072, dtype=torch.float16)\n", - "235 tensor(0.0072, dtype=torch.float16)\n", - "193 tensor(0.0069, dtype=torch.float16)\n", - "208 tensor(0.0069, dtype=torch.float16)\n", - "207 tensor(0.0067, dtype=torch.float16)\n", - "205 tensor(0.0067, dtype=torch.float16)\n", - "\u001b[93m231: Guess: $239.12 Truth: $315.55 Error: $76.43 SLE: 0.08 Item: Pro Circuit Works Pi...\u001b[0m\n", - "300 tensor(0.0552, dtype=torch.float16)\n", - "250 tensor(0.0472, dtype=torch.float16)\n", - "240 tensor(0.0335, dtype=torch.float16)\n", - "280 tensor(0.0314, dtype=torch.float16)\n", - "260 tensor(0.0314, dtype=torch.float16)\n", - "270 tensor(0.0295, dtype=torch.float16)\n", - "220 tensor(0.0269, dtype=torch.float16)\n", - "230 tensor(0.0253, dtype=torch.float16)\n", - "400 tensor(0.0253, dtype=torch.float16)\n", - "200 tensor(0.0223, dtype=torch.float16)\n", - "180 tensor(0.0216, dtype=torch.float16)\n", - "190 tensor(0.0210, dtype=torch.float16)\n", - "350 tensor(0.0210, dtype=torch.float16)\n", - "290 tensor(0.0203, dtype=torch.float16)\n", - "170 tensor(0.0197, dtype=torch.float16)\n", - "330 tensor(0.0197, dtype=torch.float16)\n", - "210 tensor(0.0191, dtype=torch.float16)\n", - "160 tensor(0.0158, dtype=torch.float16)\n", - "320 tensor(0.0144, dtype=torch.float16)\n", - "370 tensor(0.0112, dtype=torch.float16)\n", - "\u001b[93m232: Guess: $261.61 Truth: $190.99 Error: $70.62 SLE: 0.10 Item: HYANKA 15 1200W Prof...\u001b[0m\n", - "299 tensor(0.0249, dtype=torch.float16)\n", - "249 tensor(0.0177, dtype=torch.float16)\n", - "199 tensor(0.0177, dtype=torch.float16)\n", - "250 tensor(0.0151, dtype=torch.float16)\n", - "149 tensor(0.0146, dtype=torch.float16)\n", - "300 tensor(0.0138, dtype=torch.float16)\n", - "399 tensor(0.0133, dtype=torch.float16)\n", - "179 tensor(0.0129, dtype=torch.float16)\n", - "169 tensor(0.0129, dtype=torch.float16)\n", - "189 tensor(0.0125, dtype=torch.float16)\n", - "229 tensor(0.0118, dtype=torch.float16)\n", - "129 tensor(0.0118, dtype=torch.float16)\n", - "159 tensor(0.0107, dtype=torch.float16)\n", - "259 tensor(0.0101, dtype=torch.float16)\n", - "349 tensor(0.0098, dtype=torch.float16)\n", - "219 tensor(0.0095, dtype=torch.float16)\n", - "239 tensor(0.0092, dtype=torch.float16)\n", - "139 tensor(0.0089, dtype=torch.float16)\n", - "289 tensor(0.0086, dtype=torch.float16)\n", - "400 tensor(0.0086, dtype=torch.float16)\n", - "\u001b[91m233: Guess: $239.87 Truth: $155.00 Error: $84.87 SLE: 0.19 Item: Bluetooth X6BT Card ...\u001b[0m\n", - "300 tensor(0.3494, dtype=torch.float16)\n", - "350 tensor(0.2119, dtype=torch.float16)\n", - "400 tensor(0.1099, dtype=torch.float16)\n", - "250 tensor(0.0346, dtype=torch.float16)\n", - "450 tensor(0.0135, dtype=torch.float16)\n", - "330 tensor(0.0096, dtype=torch.float16)\n", - "338 tensor(0.0075, dtype=torch.float16)\n", - "340 tensor(0.0062, dtype=torch.float16)\n", - "299 tensor(0.0060, dtype=torch.float16)\n", - "337 tensor(0.0050, dtype=torch.float16)\n", - "280 tensor(0.0045, dtype=torch.float16)\n", - "500 tensor(0.0043, dtype=torch.float16)\n", - "349 tensor(0.0041, dtype=torch.float16)\n", - "325 tensor(0.0038, dtype=torch.float16)\n", - "290 tensor(0.0038, dtype=torch.float16)\n", - "331 tensor(0.0036, dtype=torch.float16)\n", - "360 tensor(0.0036, dtype=torch.float16)\n", - "298 tensor(0.0035, dtype=torch.float16)\n", - "320 tensor(0.0034, dtype=torch.float16)\n", - "297 tensor(0.0034, dtype=torch.float16)\n", - "\u001b[92m234: Guess: $330.68 Truth: $349.99 Error: $19.31 SLE: 0.00 Item: AIRAID Cold Air Inta...\u001b[0m\n", - "250 tensor(0.0381, dtype=torch.float16)\n", - "240 tensor(0.0359, dtype=torch.float16)\n", - "190 tensor(0.0307, dtype=torch.float16)\n", - "260 tensor(0.0288, dtype=torch.float16)\n", - "300 tensor(0.0288, dtype=torch.float16)\n", - "220 tensor(0.0279, dtype=torch.float16)\n", - "170 tensor(0.0279, dtype=torch.float16)\n", - "180 tensor(0.0246, dtype=torch.float16)\n", - "210 tensor(0.0246, dtype=torch.float16)\n", - "270 tensor(0.0224, dtype=torch.float16)\n", - "230 tensor(0.0224, dtype=torch.float16)\n", - "280 tensor(0.0224, dtype=torch.float16)\n", - "160 tensor(0.0217, dtype=torch.float16)\n", - "200 tensor(0.0204, dtype=torch.float16)\n", - "290 tensor(0.0154, dtype=torch.float16)\n", - "150 tensor(0.0120, dtype=torch.float16)\n", - "140 tensor(0.0113, dtype=torch.float16)\n", - "186 tensor(0.0097, dtype=torch.float16)\n", - "130 tensor(0.0094, dtype=torch.float16)\n", - "169 tensor(0.0094, dtype=torch.float16)\n", - "\u001b[92m235: Guess: $220.99 Truth: $249.99 Error: $29.00 SLE: 0.02 Item: Bostingner Shower Fa...\u001b[0m\n", - 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"20 tensor(0.0884, dtype=torch.float16)\n", - "18 tensor(0.0608, dtype=torch.float16)\n", - "15 tensor(0.0553, dtype=torch.float16)\n", - "22 tensor(0.0553, dtype=torch.float16)\n", - "19 tensor(0.0488, dtype=torch.float16)\n", - "21 tensor(0.0488, dtype=torch.float16)\n", - "17 tensor(0.0473, dtype=torch.float16)\n", - "25 tensor(0.0473, dtype=torch.float16)\n", - "16 tensor(0.0431, dtype=torch.float16)\n", - "23 tensor(0.0418, dtype=torch.float16)\n", - "24 tensor(0.0405, dtype=torch.float16)\n", - "14 tensor(0.0357, dtype=torch.float16)\n", - "13 tensor(0.0270, dtype=torch.float16)\n", - "30 tensor(0.0261, dtype=torch.float16)\n", - "12 tensor(0.0261, dtype=torch.float16)\n", - "26 tensor(0.0253, dtype=torch.float16)\n", - "27 tensor(0.0231, dtype=torch.float16)\n", - "28 tensor(0.0217, dtype=torch.float16)\n", - "10 tensor(0.0210, dtype=torch.float16)\n", - "29 tensor(0.0185, dtype=torch.float16)\n", - "\u001b[92m237: Guess: $20.02 Truth: $17.99 Error: $2.03 SLE: 0.01 Item: Caseology Bumpy Comp...\u001b[0m\n", - "400 tensor(0.0098, dtype=torch.float16)\n", - "350 tensor(0.0081, dtype=torch.float16)\n", - "300 tensor(0.0073, dtype=torch.float16)\n", - "450 tensor(0.0070, dtype=torch.float16)\n", - "500 tensor(0.0069, dtype=torch.float16)\n", - "399 tensor(0.0054, dtype=torch.float16)\n", - "360 tensor(0.0051, dtype=torch.float16)\n", - "600 tensor(0.0050, dtype=torch.float16)\n", - "390 tensor(0.0048, dtype=torch.float16)\n", - "380 tensor(0.0044, dtype=torch.float16)\n", - "480 tensor(0.0042, dtype=torch.float16)\n", - "410 tensor(0.0042, dtype=torch.float16)\n", - "370 tensor(0.0041, dtype=torch.float16)\n", - "330 tensor(0.0041, dtype=torch.float16)\n", - "430 tensor(0.0041, dtype=torch.float16)\n", - "420 tensor(0.0040, dtype=torch.float16)\n", - "299 tensor(0.0040, dtype=torch.float16)\n", - "550 tensor(0.0040, dtype=torch.float16)\n", - "250 tensor(0.0040, dtype=torch.float16)\n", - "440 tensor(0.0039, dtype=torch.float16)\n", - "\u001b[92m238: Guess: $404.65 Truth: $425.00 Error: $20.35 SLE: 0.00 Item: Fleck 2510 Timer Mec...\u001b[0m\n", - "250 tensor(0.0602, dtype=torch.float16)\n", - "240 tensor(0.0565, dtype=torch.float16)\n", - "300 tensor(0.0531, dtype=torch.float16)\n", - "260 tensor(0.0484, dtype=torch.float16)\n", - "270 tensor(0.0469, dtype=torch.float16)\n", - "280 tensor(0.0414, dtype=torch.float16)\n", - "220 tensor(0.0322, dtype=torch.float16)\n", - "230 tensor(0.0312, dtype=torch.float16)\n", - "210 tensor(0.0267, dtype=torch.float16)\n", - "190 tensor(0.0267, dtype=torch.float16)\n", - "290 tensor(0.0267, dtype=torch.float16)\n", - "200 tensor(0.0243, dtype=torch.float16)\n", - "170 tensor(0.0243, dtype=torch.float16)\n", - "180 tensor(0.0208, dtype=torch.float16)\n", - "160 tensor(0.0178, dtype=torch.float16)\n", - "330 tensor(0.0172, dtype=torch.float16)\n", - "350 tensor(0.0134, dtype=torch.float16)\n", - "320 tensor(0.0126, dtype=torch.float16)\n", - "400 tensor(0.0126, dtype=torch.float16)\n", - "310 tensor(0.0105, dtype=torch.float16)\n", - "\u001b[92m239: Guess: $252.43 Truth: $249.99 Error: $2.44 SLE: 0.00 Item: Haloview MC7108 Wire...\u001b[0m\n", - "61 tensor(0.0151, dtype=torch.float16)\n", - "63 tensor(0.0151, dtype=torch.float16)\n", - "64 tensor(0.0146, dtype=torch.float16)\n", - "54 tensor(0.0142, dtype=torch.float16)\n", - "58 tensor(0.0142, dtype=torch.float16)\n", - "62 tensor(0.0137, dtype=torch.float16)\n", - "52 tensor(0.0137, dtype=torch.float16)\n", - "72 tensor(0.0133, dtype=torch.float16)\n", - "71 tensor(0.0133, dtype=torch.float16)\n", - "51 tensor(0.0133, dtype=torch.float16)\n", - "57 tensor(0.0129, dtype=torch.float16)\n", - "73 tensor(0.0129, dtype=torch.float16)\n", - "59 tensor(0.0129, dtype=torch.float16)\n", - "68 tensor(0.0125, dtype=torch.float16)\n", - "53 tensor(0.0121, dtype=torch.float16)\n", - "48 tensor(0.0121, dtype=torch.float16)\n", - "66 tensor(0.0121, dtype=torch.float16)\n", - "67 tensor(0.0118, dtype=torch.float16)\n", - "49 tensor(0.0118, dtype=torch.float16)\n", - "44 tensor(0.0118, dtype=torch.float16)\n", - "\u001b[93m240: Guess: $59.75 Truth: $138.23 Error: $78.48 SLE: 0.69 Item: Schmidt Spiele - Man...\u001b[0m\n", - "300 tensor(0.0157, dtype=torch.float16)\n", - "250 tensor(0.0118, dtype=torch.float16)\n", - "400 tensor(0.0118, dtype=torch.float16)\n", - "240 tensor(0.0109, dtype=torch.float16)\n", - "270 tensor(0.0094, dtype=torch.float16)\n", - "280 tensor(0.0087, dtype=torch.float16)\n", - "350 tensor(0.0083, dtype=torch.float16)\n", - "260 tensor(0.0079, dtype=torch.float16)\n", - "320 tensor(0.0079, dtype=torch.float16)\n", - "330 tensor(0.0079, dtype=torch.float16)\n", - "290 tensor(0.0076, dtype=torch.float16)\n", - "360 tensor(0.0073, dtype=torch.float16)\n", - "310 tensor(0.0070, dtype=torch.float16)\n", - "450 tensor(0.0070, dtype=torch.float16)\n", - "380 tensor(0.0061, dtype=torch.float16)\n", - "340 tensor(0.0061, dtype=torch.float16)\n", - "500 tensor(0.0058, dtype=torch.float16)\n", - "225 tensor(0.0055, dtype=torch.float16)\n", - "265 tensor(0.0053, dtype=torch.float16)\n", - "245 tensor(0.0052, dtype=torch.float16)\n", - "\u001b[93m241: Guess: $315.04 Truth: $414.99 Error: $99.95 SLE: 0.08 Item: Corsa 14333 Tip Kit ...\u001b[0m\n", - "157 tensor(0.0066, dtype=torch.float16)\n", - "172 tensor(0.0064, dtype=torch.float16)\n", - "148 tensor(0.0062, dtype=torch.float16)\n", - "153 tensor(0.0062, dtype=torch.float16)\n", - "173 tensor(0.0062, dtype=torch.float16)\n", - "142 tensor(0.0062, dtype=torch.float16)\n", - "161 tensor(0.0062, dtype=torch.float16)\n", - "144 tensor(0.0060, dtype=torch.float16)\n", - "166 tensor(0.0060, dtype=torch.float16)\n", - "163 tensor(0.0060, dtype=torch.float16)\n", - "162 tensor(0.0060, dtype=torch.float16)\n", - "152 tensor(0.0060, dtype=torch.float16)\n", - "164 tensor(0.0060, dtype=torch.float16)\n", - "178 tensor(0.0060, dtype=torch.float16)\n", - "158 tensor(0.0060, dtype=torch.float16)\n", - "184 tensor(0.0060, dtype=torch.float16)\n", - "171 tensor(0.0060, dtype=torch.float16)\n", - "154 tensor(0.0058, dtype=torch.float16)\n", - "156 tensor(0.0058, dtype=torch.float16)\n", - "141 tensor(0.0058, dtype=torch.float16)\n", - "\u001b[92m242: Guess: $159.98 Truth: $168.28 Error: $8.30 SLE: 0.00 Item: Hoshizaki FM116A Fan...\u001b[0m\n", - "299 tensor(0.0168, dtype=torch.float16)\n", - "300 tensor(0.0158, dtype=torch.float16)\n", - "250 tensor(0.0144, dtype=torch.float16)\n", - "249 tensor(0.0127, dtype=torch.float16)\n", - "240 tensor(0.0123, dtype=torch.float16)\n", - "169 tensor(0.0123, dtype=torch.float16)\n", - "199 tensor(0.0123, dtype=torch.float16)\n", - "179 tensor(0.0123, dtype=torch.float16)\n", - "159 tensor(0.0119, dtype=torch.float16)\n", - "189 tensor(0.0115, dtype=torch.float16)\n", - "129 tensor(0.0105, dtype=torch.float16)\n", - "149 tensor(0.0105, dtype=torch.float16)\n", - "139 tensor(0.0102, dtype=torch.float16)\n", - "219 tensor(0.0099, dtype=torch.float16)\n", - "239 tensor(0.0099, dtype=torch.float16)\n", - "260 tensor(0.0096, dtype=torch.float16)\n", - "280 tensor(0.0096, dtype=torch.float16)\n", - "270 tensor(0.0096, dtype=torch.float16)\n", - "229 tensor(0.0090, dtype=torch.float16)\n", - "259 tensor(0.0087, dtype=torch.float16)\n", - "\u001b[92m243: Guess: $222.90 Truth: $199.99 Error: $22.91 SLE: 0.01 Item: BAINUO Antler Chande...\u001b[0m\n", - "131 tensor(0.0165, dtype=torch.float16)\n", - "121 tensor(0.0160, dtype=torch.float16)\n", - "132 tensor(0.0155, dtype=torch.float16)\n", - "122 tensor(0.0141, dtype=torch.float16)\n", - "123 tensor(0.0141, dtype=torch.float16)\n", - "141 tensor(0.0141, dtype=torch.float16)\n", - "111 tensor(0.0133, dtype=torch.float16)\n", - "127 tensor(0.0133, dtype=torch.float16)\n", - "151 tensor(0.0133, dtype=torch.float16)\n", - "102 tensor(0.0125, dtype=torch.float16)\n", - "147 tensor(0.0125, dtype=torch.float16)\n", - "142 tensor(0.0125, dtype=torch.float16)\n", - "114 tensor(0.0117, dtype=torch.float16)\n", - "101 tensor(0.0117, dtype=torch.float16)\n", - "152 tensor(0.0117, dtype=torch.float16)\n", - "128 tensor(0.0117, dtype=torch.float16)\n", - "103 tensor(0.0113, dtype=torch.float16)\n", - "137 tensor(0.0113, dtype=torch.float16)\n", - "153 tensor(0.0113, dtype=torch.float16)\n", - "124 tensor(0.0113, dtype=torch.float16)\n", - "\u001b[92m244: Guess: $128.15 Truth: $126.70 Error: $1.45 SLE: 0.00 Item: DNA MOTORING Smoke L...\u001b[0m\n", - "4 tensor(0.1163, dtype=torch.float16)\n", - "5 tensor(0.1026, dtype=torch.float16)\n", - "6 tensor(0.0994, dtype=torch.float16)\n", - "3 tensor(0.0964, dtype=torch.float16)\n", - "7 tensor(0.0850, dtype=torch.float16)\n", - "8 tensor(0.0662, dtype=torch.float16)\n", - "2 tensor(0.0549, dtype=torch.float16)\n", - "9 tensor(0.0549, dtype=torch.float16)\n", - "11 tensor(0.0402, dtype=torch.float16)\n", - "10 tensor(0.0333, dtype=torch.float16)\n", - "12 tensor(0.0285, dtype=torch.float16)\n", - "13 tensor(0.0229, dtype=torch.float16)\n", - "14 tensor(0.0215, dtype=torch.float16)\n", - "16 tensor(0.0130, dtype=torch.float16)\n", - "15 tensor(0.0130, dtype=torch.float16)\n", - "17 tensor(0.0112, dtype=torch.float16)\n", - "18 tensor(0.0108, dtype=torch.float16)\n", - "1 tensor(0.0102, dtype=torch.float16)\n", - "21 tensor(0.0087, dtype=torch.float16)\n", - "19 tensor(0.0087, dtype=torch.float16)\n", - "\u001b[92m245: Guess: $7.19 Truth: $5.91 Error: $1.28 SLE: 0.03 Item: Wera Stainless 3840/...\u001b[0m\n", - "250 tensor(0.0598, dtype=torch.float16)\n", - "300 tensor(0.0411, dtype=torch.float16)\n", - "240 tensor(0.0363, dtype=torch.float16)\n", - "270 tensor(0.0301, dtype=torch.float16)\n", - "260 tensor(0.0265, dtype=torch.float16)\n", - "280 tensor(0.0257, dtype=torch.float16)\n", - "230 tensor(0.0257, dtype=torch.float16)\n", - "220 tensor(0.0220, dtype=torch.float16)\n", - "249 tensor(0.0166, dtype=torch.float16)\n", - "290 tensor(0.0161, dtype=torch.float16)\n", - "330 tensor(0.0156, dtype=torch.float16)\n", - "210 tensor(0.0156, dtype=torch.float16)\n", - "350 tensor(0.0147, dtype=torch.float16)\n", - "320 tensor(0.0129, dtype=torch.float16)\n", - "229 tensor(0.0118, dtype=torch.float16)\n", - "190 tensor(0.0114, dtype=torch.float16)\n", - "200 tensor(0.0107, dtype=torch.float16)\n", - "259 tensor(0.0095, dtype=torch.float16)\n", - "299 tensor(0.0095, dtype=torch.float16)\n", - "310 tensor(0.0092, dtype=torch.float16)\n", - "\u001b[93m246: Guess: $262.86 Truth: $193.06 Error: $69.80 SLE: 0.09 Item: Celestron - PowerSee...\u001b[0m\n", - "250 tensor(0.0389, dtype=torch.float16)\n", - "240 tensor(0.0343, dtype=torch.float16)\n", - "300 tensor(0.0303, dtype=torch.float16)\n", - "270 tensor(0.0303, dtype=torch.float16)\n", - "260 tensor(0.0293, dtype=torch.float16)\n", - "280 tensor(0.0243, dtype=torch.float16)\n", - "190 tensor(0.0222, dtype=torch.float16)\n", - "220 tensor(0.0208, dtype=torch.float16)\n", - "299 tensor(0.0202, dtype=torch.float16)\n", - "290 tensor(0.0202, dtype=torch.float16)\n", - "230 tensor(0.0202, dtype=torch.float16)\n", - "249 tensor(0.0195, dtype=torch.float16)\n", - "179 tensor(0.0190, dtype=torch.float16)\n", - "210 tensor(0.0162, dtype=torch.float16)\n", - "229 tensor(0.0157, dtype=torch.float16)\n", - "269 tensor(0.0157, dtype=torch.float16)\n", - "170 tensor(0.0157, dtype=torch.float16)\n", - "180 tensor(0.0152, dtype=torch.float16)\n", - "259 tensor(0.0152, dtype=torch.float16)\n", - "219 tensor(0.0152, dtype=torch.float16)\n", - "\u001b[92m247: Guess: $244.38 Truth: $249.99 Error: $5.61 SLE: 0.00 Item: NHOPEEW Android Car ...\u001b[0m\n", - "92 tensor(0.0091, dtype=torch.float16)\n", - "104 tensor(0.0088, dtype=torch.float16)\n", - "91 tensor(0.0085, dtype=torch.float16)\n", - "114 tensor(0.0083, dtype=torch.float16)\n", - "93 tensor(0.0083, dtype=torch.float16)\n", - "94 tensor(0.0083, dtype=torch.float16)\n", - "105 tensor(0.0080, dtype=torch.float16)\n", - "112 tensor(0.0080, dtype=torch.float16)\n", - "108 tensor(0.0080, dtype=torch.float16)\n", - "117 tensor(0.0078, dtype=torch.float16)\n", - "107 tensor(0.0078, dtype=torch.float16)\n", - "103 tensor(0.0078, dtype=torch.float16)\n", - "102 tensor(0.0078, dtype=torch.float16)\n", - "118 tensor(0.0078, dtype=torch.float16)\n", - "122 tensor(0.0078, dtype=torch.float16)\n", - "113 tensor(0.0078, dtype=torch.float16)\n", - "87 tensor(0.0075, dtype=torch.float16)\n", - "121 tensor(0.0075, dtype=torch.float16)\n", - "96 tensor(0.0075, dtype=torch.float16)\n", - "98 tensor(0.0075, dtype=torch.float16)\n", - "\u001b[93m248: Guess: $104.67 Truth: $64.12 Error: $40.55 SLE: 0.23 Item: Other Harmonica A)\n", - "F...\u001b[0m\n", - "250 tensor(0.0182, dtype=torch.float16)\n", - "260 tensor(0.0182, dtype=torch.float16)\n", - "240 tensor(0.0176, dtype=torch.float16)\n", - "270 tensor(0.0166, dtype=torch.float16)\n", - "290 tensor(0.0156, dtype=torch.float16)\n", - "300 tensor(0.0156, dtype=torch.float16)\n", - "239 tensor(0.0146, dtype=torch.float16)\n", - "289 tensor(0.0146, dtype=torch.float16)\n", - "259 tensor(0.0137, dtype=torch.float16)\n", - "249 tensor(0.0125, dtype=torch.float16)\n", - "299 tensor(0.0125, dtype=torch.float16)\n", - "269 tensor(0.0125, dtype=torch.float16)\n", - "280 tensor(0.0121, dtype=torch.float16)\n", - "209 tensor(0.0110, dtype=torch.float16)\n", - "279 tensor(0.0097, dtype=torch.float16)\n", - "186 tensor(0.0097, dtype=torch.float16)\n", - "330 tensor(0.0081, dtype=torch.float16)\n", - "229 tensor(0.0081, dtype=torch.float16)\n", - "219 tensor(0.0081, dtype=torch.float16)\n", - "216 tensor(0.0076, dtype=torch.float16)\n", - "\u001b[91m249: Guess: $260.32 Truth: $114.99 Error: $145.33 SLE: 0.66 Item: Harley Air Filter Ve...\u001b[0m\n", - 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "test[80]" - ], - "metadata": { - "id": "M4NSMcKl3Bhw", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "02c34fb4-858a-4f8b-bfbb-42c79bb3f57e" - }, - "execution_count": 97, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "{'text': \"How much does this cost to the nearest dollar?\\n\\nLongacre Aluminum Turn Plates\\nLongacre is an established brand name in the racing industry and is recognized for dedication to quality, innovation and customer satisfaction. Check out our comprehensive line of race scales, alignment tools, racing gauges and other products. Whether you are into stock, modified, drag, go kart, off-road, sprint or RC car racing, we'll provide you with the quality racing parts you deserve. The free floating in 2 directions eliminates bind It reads to 1/2° - Degrees can be zeroed with the car on The low profile design means that its only 1 tall Can also be used on top of scale pads Has a weight capacity of 1,500 lbs. per scale Manufacturer Longacre, Brand Longacre, Model Longacre Racing Products, Weight 31\\n\\nPrice is $\",\n", - " 'price': 940.33}" - ] - }, - "metadata": {}, - "execution_count": 97 - } - ] - }, - { - "cell_type": "code", - "source": [ - "# --- (Your helper functions and model variables) ---\n", - "# Ensure the following are defined in your script:\n", - "# set_seed, tokenizer, fine_tuned_model, make_prompt, test (data)\n", - "# MODEL_ARTIFACT_NAME, REVISION_TAG, COLOR_MAP, RESET\n", - "# ---------------------------------------------------\n", - "\n", - "def calculate_weighted_price(prices, probabilities):\n", - " \"\"\"\n", - " Calculates a normalized weighted average price.\n", - " (This function is unchanged)\n", - "\n", - " Args:\n", - " prices (list or np.array): A list of prices.\n", - " probabilities (list or np.array): A list of corresponding probabilities (or weights).\n", - "\n", - " Returns:\n", - " float: The normalized weighted average price.\n", - " \"\"\"\n", - " prices_array = np.array(prices)\n", - " probs_array = np.array(probabilities)\n", - "\n", - " total_prob = np.sum(probs_array)\n", - "\n", - " if total_prob == 0:\n", - " # Handle case with no/zero probabilities\n", - " if len(prices_array) > 0:\n", - " return np.mean(prices_array)\n", - " else:\n", - " return 0.0 # No prices and no probabilities\n", - "\n", - " # Use np.average for a robust and clean weighted average\n", - " weighted_price = np.average(prices_array, weights=probs_array)\n", - "\n", - " return weighted_price\n", - "\n", - "# Set the maximum number of probabilities to fetch\n", - "TOP_K = 900\n", - "\n", - "def get_top_k_predictions(prompt, device=\"cuda\"):\n", - " \"\"\"\n", - " MODIFIED: Gets the top K price/probability pairs from the model.\n", - "\n", - " Returns:\n", - " (list, list): A tuple containing (prices, probabilities)\n", - " \"\"\"\n", - " set_seed(42)\n", - " inputs = tokenizer.encode(prompt, return_tensors=\"pt\").to(device)\n", - " attention_mask = torch.ones(inputs.shape, device=device)\n", - "\n", - " with torch.no_grad():\n", - " outputs = fine_tuned_model(inputs, attention_mask=attention_mask)\n", - " next_token_logits = outputs.logits[:, -1, :].to('cpu')\n", - "\n", - " next_token_probs = F.softmax(next_token_logits, dim=-1)\n", - " top_prob, top_token_id = next_token_probs.topk(TOP_K)\n", - "\n", - " prices = []\n", - " probabilities = []\n", - "\n", - " for i in range(TOP_K):\n", - " predicted_token = tokenizer.decode(top_token_id[0][i])\n", - " probability_tensor = top_prob[0][i]\n", - "\n", - " try:\n", - " price = float(predicted_token)\n", - " except ValueError as e:\n", - " price = 0.0\n", - "\n", - " # Only include valid, positive prices\n", - " if price > 0:\n", - " prices.append(price)\n", - " # Store the probability as a simple float\n", - " probabilities.append(probability_tensor.item())\n", - "\n", - " if not prices:\n", - " return [], []\n", - "\n", - " # Return the raw lists for analysis\n", - " return prices, probabilities\n", - "\n", - "\n", - "class Tester:\n", - " \"\"\"\n", - " MODIFIED: This class now tests for the optimal 'k' value.\n", - " \"\"\"\n", - " def __init__(self, predictor, data, title=None, size=250):\n", - " self.predictor = predictor # This will be get_top_k_predictions\n", - " self.data = data\n", - " self.title = title or predictor.__name__.replace(\"_\", \" \").title()\n", - " self.size = size\n", - " self.truths = []\n", - "\n", - " # This will store the errors for each k for every inference\n", - " # Shape: (self.size, TOP_K)\n", - " # self.all_k_errors[i][k-1] = error for inference i at k\n", - " self.all_k_errors = []\n", - " self.max_k = TOP_K\n", - "\n", - " def run_datapoint(self, i):\n", - " datapoint = self.data[i]\n", - " base_prompt = datapoint[\"text\"]\n", - " prompt = make_prompt(base_prompt)\n", - " truth = datapoint[\"price\"]\n", - " self.truths.append(truth)\n", - "\n", - " # 1. Get the raw lists of prices and probabilities\n", - " prices, probabilities = self.predictor(prompt)\n", - "\n", - " errors_for_this_datapoint = []\n", - "\n", - " if not prices:\n", - " # Handle cases where the model returned no valid prices\n", - " print(f\"{i+1}: No valid prices found. Truth: ${truth:,.2f}.\")\n", - " # Assign the error (abs(0 - truth)) for all k values\n", - " error = np.abs(0 - truth)\n", - " errors_for_this_datapoint = [error] * self.max_k\n", - " self.all_k_errors.append(errors_for_this_datapoint)\n", - " return\n", - "\n", - " # 2. Iterate from k=1 up to max_k\n", - " for k in range(1, self.max_k + 1):\n", - " # Get the top k prices/probs\n", - " # Python slicing handles k > len(prices) gracefully\n", - " k_prices = prices[:k]\n", - " k_probabilities = probabilities[:k]\n", - "\n", - " # 3. Calculate the weighted price just for this k\n", - " guess = calculate_weighted_price(k_prices, k_probabilities)\n", - "\n", - " # 4. Calculate and store the error for this k\n", - " error = np.abs(guess - truth)\n", - " errors_for_this_datapoint.append(error)\n", - "\n", - " # Store the list of errors (for k=1 to max_k) for this inference\n", - " self.all_k_errors.append(errors_for_this_datapoint)\n", - "\n", - " # Print a summary for this datapoint\n", - " title = datapoint[\"text\"].split(\"\\n\\n\")[1][:20] + \"...\"\n", - " # Using [0], [19], [-1] for k=1, k=20, k=max_k (0-indexed)\n", - " k_1_err = errors_for_this_datapoint[0]\n", - " k_20_err = errors_for_this_datapoint[19]\n", - " k_max_err = errors_for_this_datapoint[-1]\n", - "\n", - " # --- FIX IS ON THIS LINE ---\n", - " # Assuming COLOR_MAP is a dict and RESET is a string\n", - " print(f\"{COLOR_MAP.get('orange', '')}{i+1}: Truth: ${truth:,.2f}. \"\n", - " f\"Errors (k=1, k=20, k={self.max_k}): \"\n", - " f\"(${k_1_err:,.2f}, ${k_20_err:,.2f}, ${k_max_err:,.2f}) \"\n", - " f\"Item: {title}{RESET}\") # Removed .get('', '') from RESET\n", - "\n", - " def plot_k_vs_error(self, k_values, avg_errors_by_k, best_k, min_error):\n", - " \"\"\"\n", - " NEW: Plots the Average Error vs. k\n", - " \"\"\"\n", - " plt.figure(figsize=(12, 8))\n", - " plt.plot(k_values, avg_errors_by_k, label='Average Error vs. k')\n", - "\n", - " # Highlight the best k\n", - " plt.axvline(x=best_k, color='red', linestyle='--',\n", - " label=f'Best k = {best_k} (Avg Error: ${min_error:,.2f})')\n", - "\n", - " plt.xlabel('Number of Top Probabilities/Prices (k)')\n", - " plt.ylabel('Average Absolute Error ($)')\n", - " plt.title(f'Optimal k Analysis for {self.title}')\n", - " plt.legend()\n", - " plt.grid(True, which='both', linestyle='--', linewidth=0.5)\n", - " # Set x-axis to start at 1\n", - " plt.xlim(left=1)\n", - " plt.show()\n", - "\n", - " def report(self):\n", - " \"\"\"\n", - " MODIFIED: Performs the final analysis and plots the k-vs-error graph.\n", - " \"\"\"\n", - " # 1. Convert list of lists to a 2D numpy array (inferences x k_values)\n", - " errors_array = np.array(self.all_k_errors)\n", - "\n", - " # 2. Calculate the average error for each k (column-wise mean)\n", - " # axis=0 means calculate mean *down* the columns\n", - " avg_errors_by_k = np.mean(errors_array, axis=0)\n", - "\n", - " # 3. Find the best k (index of the minimum average error)\n", - " best_k_index = np.argmin(avg_errors_by_k)\n", - " min_error = avg_errors_by_k[best_k_index]\n", - "\n", - " # k is 1-based, but our index is 0-based\n", - " best_k = best_k_index + 1\n", - "\n", - " print(\"\\n--- Optimal k Analysis Report ---\")\n", - " print(f\"Model: {self.title}\")\n", - " print(f\"Inferences Run: {self.size}\")\n", - " print(f\"Analyzed k from 1 to {self.max_k}\")\n", - " print(f\"===================================\")\n", - " print(f\"==> Best k: {best_k}\")\n", - " print(f\"==> Minimum Average Error: ${min_error:,.2f}\")\n", - " print(f\"===================================\")\n", - "\n", - " # 4. Plot the graph\n", - " k_values = np.arange(1, self.max_k + 1)\n", - " self.plot_k_vs_error(k_values, avg_errors_by_k, best_k, min_error)\n", - "\n", - " def run(self):\n", - " for i in range(self.size):\n", - " self.run_datapoint(i)\n", - " # The report now does all the analysis and plotting\n", - " self.report()\n", - "\n", - " @classmethod\n", - " def test(cls, function, data):\n", - " cls(function, data).run()\n", - "\n", - "# --- MODIFIED EXECUTION ---\n", - "# Pass the new function 'get_top_k_predictions' to the Tester\n", - "tester = Tester(get_top_k_predictions, test, title=f\"{MODEL_ARTIFACT_NAME}:{REVISION_TAG}\")\n", - "tester.run()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "Z41oRz96rAu5", - "outputId": "4a4ad321-b110-47f2-8df3-3e65ca6d414b" - }, - "execution_count": 58, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[93m1: Truth: $374.41. Errors (k=1, k=20, k=5000): ($81.41, $72.98, $51.95) Item: OEM AC Compressor w/...\u001b[0m\n", - "\u001b[93m2: Truth: $225.11. Errors (k=1, k=20, k=5000): ($84.11, $79.28, $59.00) Item: Motorcraft YB3125 Fa...\u001b[0m\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.12/dist-packages/numpy/lib/_function_base_impl.py:573: RuntimeWarning: invalid value encountered in multiply\n", - " avg = avg_as_array = np.multiply(a, wgt,\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[93m3: Truth: $61.68. Errors (k=1, k=20, k=5000): ($20.68, $15.16, $nan) Item: Dorman Front Washer ...\u001b[0m\n", - "\u001b[93m4: Truth: $599.99. Errors (k=1, k=20, k=5000): ($99.99, $102.15, $79.59) Item: HP Premium HD Plus T...\u001b[0m\n", - "\u001b[93m5: Truth: $16.99. Errors (k=1, k=20, k=5000): ($7.99, $5.32, $0.34) Item: Super Switch Pickup ...\u001b[0m\n", - "\u001b[93m6: Truth: $31.99. Errors (k=1, k=20, k=5000): ($19.99, $17.14, $11.46) Item: Horror Bookmarks, Re...\u001b[0m\n", - "\u001b[93m7: Truth: $101.79. Errors (k=1, k=20, k=5000): ($60.79, $57.23, $nan) Item: SK6241 - Stinger 4 G...\u001b[0m\n", - "\u001b[93m8: Truth: $289.00. Errors (k=1, k=20, k=5000): ($10.00, $22.44, $7.98) Item: Godox ML60Bi LED Lig...\u001b[0m\n", - "\u001b[93m9: Truth: $635.86. Errors (k=1, k=20, k=5000): ($135.86, $32.93, $37.37) Item: Randall G3 Plus Comb...\u001b[0m\n", - "\u001b[93m10: Truth: $65.99. Errors (k=1, k=20, k=5000): ($44.01, $53.50, $nan) Item: HOLDWILL 6 Pack LED ...\u001b[0m\n", - "\u001b[93m11: Truth: $254.21. Errors (k=1, k=20, k=5000): ($45.79, $49.20, $62.56) Item: Viking Horns 3 Gallo...\u001b[0m\n", - "\u001b[93m12: Truth: $412.99. Errors (k=1, k=20, k=5000): ($12.99, $18.22, $19.90) Item: CURT 70110 Custom To...\u001b[0m\n", - "\u001b[93m13: Truth: $205.50. Errors (k=1, k=20, k=5000): ($34.50, $58.44, $nan) Item: Solar HAMMERED BRONZ...\u001b[0m\n", - "\u001b[93m14: Truth: $248.23. Errors (k=1, k=20, k=5000): ($51.77, $21.44, $27.85) Item: COSTWAY Electric Tum...\u001b[0m\n", - "\u001b[93m15: Truth: $399.00. Errors (k=1, k=20, k=5000): ($99.00, $51.34, $8.45) Item: FREE SIGNAL TV Trans...\u001b[0m\n", - "\u001b[93m16: Truth: $373.94. Errors (k=1, k=20, k=5000): ($35.94, $28.00, $20.61) Item: Bilstein 5100 Monotu...\u001b[0m\n", - "\u001b[93m17: Truth: $92.89. Errors (k=1, k=20, k=5000): ($5.11, $4.40, $nan) Item: Sangean K-200 Multi-...\u001b[0m\n", - "\u001b[93m18: Truth: $51.99. Errors (k=1, k=20, k=5000): ($52.01, $67.21, $nan) Item: Charles Leonard Magn...\u001b[0m\n", - "\u001b[93m19: Truth: $179.00. Errors (k=1, k=20, k=5000): ($20.00, $65.59, $79.32) Item: Gigabyte AMD Radeon ...\u001b[0m\n", - "\u001b[93m20: Truth: $19.42. Errors (k=1, k=20, k=5000): ($0.42, $2.47, $1.97) Item: 3dRose LLC 8 x 8 x 0...\u001b[0m\n", - "\u001b[93m21: Truth: $539.95. Errors (k=1, k=20, k=5000): ($40.95, $17.93, $10.77) Item: ROKINON 85mm F1.4 Au...\u001b[0m\n", - "\u001b[93m22: Truth: $147.67. Errors (k=1, k=20, k=5000): ($40.67, $43.95, $30.75) Item: Headlight Assembly C...\u001b[0m\n", - "\u001b[93m23: Truth: $24.99. Errors (k=1, k=20, k=5000): ($24.01, $18.60, $nan) Item: ASI NAUTICAL 2.5 Inc...\u001b[0m\n", - "\u001b[93m24: Truth: $149.00. Errors (k=1, k=20, k=5000): ($80.00, $68.07, $65.00) Item: Behringer TUBE OVERD...\u001b[0m\n", - "\u001b[93m25: Truth: $16.99. Errors (k=1, k=20, k=5000): ($4.99, $4.12, $1.53) Item: Fun Express Insect F...\u001b[0m\n", - "\u001b[93m26: Truth: $7.99. Errors (k=1, k=20, k=5000): ($2.01, $2.80, $8.93) Item: WAFJAMF Roller Stamp...\u001b[0m\n", - "\u001b[93m27: Truth: $199.99. Errors (k=1, k=20, k=5000): ($13.99, $15.86, $0.92) Item: Capulina Tiffany Flo...\u001b[0m\n", - "\u001b[93m28: Truth: $251.45. Errors (k=1, k=20, k=5000): ($1.45, $6.34, $6.58) Item: Apple Watch Series 6...\u001b[0m\n", - "\u001b[93m29: Truth: $231.62. Errors (k=1, k=20, k=5000): ($60.62, $69.76, $28.40) Item: ICON 01725 Tandem Ax...\u001b[0m\n", - "\u001b[93m30: Truth: $135.00. Errors (k=1, k=20, k=5000): ($35.00, $52.41, $nan) Item: SanDisk 128GB Ultra ...\u001b[0m\n", - "\u001b[93m31: Truth: $356.62. Errors (k=1, k=20, k=5000): ($163.62, $134.83, $69.13) Item: Velvac - 715427\n", - "2020...\u001b[0m\n", - "\u001b[93m32: Truth: $257.99. Errors (k=1, k=20, k=5000): ($7.99, $40.21, $41.27) Item: TCMT Passenger Backr...\u001b[0m\n", - "\u001b[93m33: Truth: $27.99. Errors (k=1, k=20, k=5000): ($11.99, $10.64, $1.79) Item: Alnicov 63.5MM Brass...\u001b[0m\n", - "\u001b[93m34: Truth: $171.20. Errors (k=1, k=20, k=5000): ($80.20, $55.81, $nan) Item: Subaru Forester Outb...\u001b[0m\n", - "\u001b[93m35: Truth: $225.00. Errors (k=1, k=20, k=5000): ($24.00, $38.28, $60.32) Item: Richmond Auto Uphols...\u001b[0m\n", - "\u001b[93m36: Truth: $105.00. Errors (k=1, k=20, k=5000): ($54.00, $69.01, $nan) Item: AP-39 Automotive Pai...\u001b[0m\n", - "\u001b[93m37: Truth: $299.99. Errors (k=1, k=20, k=5000): ($0.99, $31.50, $56.83) Item: Road Top Wireless Ca...\u001b[0m\n", - "\u001b[93m38: Truth: $535.09. Errors (k=1, k=20, k=5000): ($9.09, $33.06, $inf) Item: Gibson Performance E...\u001b[0m\n", - "\u001b[93m39: Truth: $12.33. Errors (k=1, k=20, k=5000): ($0.33, $3.27, $8.74) Item: Bella Tunno Happy Li...\u001b[0m\n", - "\u001b[93m40: Truth: $84.99. Errors (k=1, k=20, k=5000): ($4.99, $1.12, $nan) Item: CANMORE H300 Handhel...\u001b[0m\n", - "\u001b[93m41: Truth: $15.99. Errors (k=1, k=20, k=5000): ($2.99, $0.54, $2.70) Item: DCPOWER AC Adapter C...\u001b[0m\n", - "\u001b[93m42: Truth: $62.44. Errors (k=1, k=20, k=5000): ($17.44, $16.50, $nan) Item: Sharp, Commercial De...\u001b[0m\n", - "\u001b[93m43: Truth: $82.99. Errors (k=1, k=20, k=5000): ($17.99, $19.31, $nan) Item: Melissa & Doug Lifel...\u001b[0m\n", - "\u001b[93m44: Truth: $599.95. Errors (k=1, k=20, k=5000): ($201.95, $214.48, $185.63) Item: Sony SSCS8 2-Way Cen...\u001b[0m\n", - "\u001b[93m45: Truth: $194.99. Errors (k=1, k=20, k=5000): ($54.01, $45.87, $47.28) Item: ASUS Chromebook CX1,...\u001b[0m\n", - "\u001b[93m46: Truth: $344.95. Errors (k=1, k=20, k=5000): ($55.05, $47.99, $56.73) Item: FiiO X7 32GB Hi-Res ...\u001b[0m\n", - "\u001b[93m47: Truth: $37.99. Errors (k=1, k=20, k=5000): ($2.01, $2.14, $8.21) Item: TORRO Leather Case C...\u001b[0m\n", - "\u001b[93m48: Truth: $224.35. Errors (k=1, k=20, k=5000): ($19.35, $16.25, $11.97) Item: Universal Air Condit...\u001b[0m\n", - "\u001b[93m49: Truth: $814.00. Errors (k=1, k=20, k=5000): ($14.00, $42.04, $inf) Item: Street Series Stainl...\u001b[0m\n", - "\u001b[93m50: Truth: $439.88. Errors (k=1, k=20, k=5000): ($40.88, $66.90, $56.06) Item: Lenovo IdeaPad 3 Lap...\u001b[0m\n", - "\u001b[93m51: Truth: $341.43. Errors (k=1, k=20, k=5000): ($92.43, $72.99, $70.49) Item: Access Bed Covers To...\u001b[0m\n", - "\u001b[93m52: Truth: $46.78. Errors (k=1, k=20, k=5000): ($1.78, $12.93, $nan) Item: G.I. JOE Hasbro 3 3/...\u001b[0m\n", - "\u001b[93m53: Truth: $171.44. Errors (k=1, k=20, k=5000): ($12.56, $6.30, $72.96) Item: T&S Brass Double Pan...\u001b[0m\n", - "\u001b[93m54: Truth: $458.00. Errors (k=1, k=20, k=5000): ($158.00, $108.54, $19.78) Item: ZTUOAUMA Fuel Inject...\u001b[0m\n", - "\u001b[93m55: Truth: $130.75. Errors (k=1, k=20, k=5000): ($119.25, $52.77, $55.84) Item: Hp Prime Graphing Ca...\u001b[0m\n", - "\u001b[93m56: Truth: $83.81. Errors (k=1, k=20, k=5000): ($42.81, $51.58, $nan) Item: Lowrance Nmea 2000 2...\u001b[0m\n", - "\u001b[93m57: Truth: $386.39. Errors (k=1, k=20, k=5000): ($245.39, $232.53, $nan) Item: Jeep Genuine Accesso...\u001b[0m\n", - "\u001b[93m58: Truth: $169.00. Errors (k=1, k=20, k=5000): ($130.00, $47.50, $nan) Item: GODOX CB-06 Hard Car...\u001b[0m\n", - "\u001b[93m59: Truth: $17.95. Errors (k=1, k=20, k=5000): ($2.95, $1.67, $2.19) Item: Au-Tomotive Gold, IN...\u001b[0m\n", - "\u001b[93m60: Truth: $269.00. Errors (k=1, k=20, k=5000): ($20.00, $40.42, $39.88) Item: Snailfly Black Roof ...\u001b[0m\n", - "\u001b[93m61: Truth: $77.77. Errors (k=1, k=20, k=5000): ($8.77, $22.60, $nan) Item: KING SHA Anti Glare ...\u001b[0m\n", - "\u001b[93m62: Truth: $88.99. Errors (k=1, k=20, k=5000): ($7.99, $4.97, $nan) Item: APS Compatible with ...\u001b[0m\n", - "\u001b[93m63: Truth: $364.41. Errors (k=1, k=20, k=5000): ($65.41, $100.45, $31.63) Item: Wilwood Engineering ...\u001b[0m\n", - "\u001b[93m64: Truth: $127.03. Errors (k=1, k=20, k=5000): ($13.97, $19.73, $37.87) Item: ACDelco Gold Starter...\u001b[0m\n", - "\u001b[93m65: Truth: $778.95. Errors (k=1, k=20, k=5000): ($242.95, $210.19, $inf) Item: UWS Matte Black Heav...\u001b[0m\n", - "\u001b[93m66: Truth: $206.66. Errors (k=1, k=20, k=5000): ($43.34, $5.79, $23.25) Item: Dell Latitude E5440 ...\u001b[0m\n", - "\u001b[93m67: Truth: $35.94. Errors (k=1, k=20, k=5000): ($10.06, $6.05, $nan) Item: (Plug and Play) Spar...\u001b[0m\n", - "\u001b[93m68: Truth: $149.00. Errors (k=1, k=20, k=5000): ($101.00, $13.75, $nan) Item: The Ultimate Roadsid...\u001b[0m\n", - "\u001b[93m69: Truth: $251.98. Errors (k=1, k=20, k=5000): ($42.98, $32.30, $8.12) Item: Brand New 18 x 8.5 R...\u001b[0m\n", - "\u001b[93m70: Truth: $160.00. Errors (k=1, k=20, k=5000): ($90.00, $78.47, $101.44) Item: Headlight Headlamp L...\u001b[0m\n", - "\u001b[93m71: Truth: $39.99. Errors (k=1, k=20, k=5000): ($4.99, $7.22, $0.06) Item: Lilo And Stitch Delu...\u001b[0m\n", - "\u001b[93m72: Truth: $362.41. Errors (k=1, k=20, k=5000): ($112.41, $109.19, $100.10) Item: AC Compressor & A/C ...\u001b[0m\n", - "\u001b[93m73: Truth: $344.00. Errors (k=1, k=20, k=5000): ($44.00, $27.91, $31.63) Item: House Of Troy Pinnac...\u001b[0m\n", - "\u001b[93m74: Truth: $25.09. Errors (k=1, k=20, k=5000): ($25.91, $33.61, $nan) Item: Juno T29 WH Floating...\u001b[0m\n", - "\u001b[93m75: Truth: $175.95. Errors (k=1, k=20, k=5000): ($104.95, $102.92, $nan) Item: Sherman GO-PARTS - f...\u001b[0m\n", - "\u001b[93m76: Truth: $132.64. Errors (k=1, k=20, k=5000): ($167.36, $175.31, $184.75) Item: Roland RPU-3 Electro...\u001b[0m\n", - "\u001b[93m77: Truth: $422.99. Errors (k=1, k=20, k=5000): ($122.99, $82.91, $52.37) Item: Rockland VMI14 12,00...\u001b[0m\n", - "\u001b[93m78: Truth: $146.48. Errors (k=1, k=20, k=5000): ($0.52, $5.95, $18.37) Item: Max Advanced Brakes ...\u001b[0m\n", - "\u001b[93m79: Truth: $156.83. Errors (k=1, k=20, k=5000): ($2.83, $6.12, $5.69) Item: Quality-Built 11030 ...\u001b[0m\n", - "\u001b[93m80: Truth: $251.99. Errors (k=1, k=20, k=5000): ($101.99, $90.47, $nan) Item: Lucida LG-510 Studen...\u001b[0m\n", - "\u001b[93m81: Truth: $940.33. Errors (k=1, k=20, k=5000): ($799.33, $794.27, $nan) Item: Longacre Aluminum Tu...\u001b[0m\n", - "\u001b[93m82: Truth: $52.99. Errors (k=1, k=20, k=5000): ($8.01, $14.94, $nan) Item: Motion Pro Adjustabl...\u001b[0m\n", - "\u001b[93m83: Truth: $219.95. Errors (k=1, k=20, k=5000): ($30.05, $63.75, $28.16) Item: Glyph Thunderbolt 3 ...\u001b[0m\n", - "\u001b[93m84: Truth: $441.03. Errors (k=1, k=20, k=5000): ($141.03, $138.31, $105.38) Item: TOYO Open Country MT...\u001b[0m\n", - "\u001b[93m85: Truth: $168.98. Errors (k=1, k=20, k=5000): ($18.98, $28.33, $20.80) Item: Razer Seiren X USB S...\u001b[0m\n", - "\u001b[93m86: Truth: $2.49. Errors (k=1, k=20, k=5000): ($1.51, $1.95, $3.29) Item: Happy Birthday to Da...\u001b[0m\n", - "\u001b[93m87: Truth: $98.62. Errors (k=1, k=20, k=5000): ($1.38, $5.55, $nan) Item: Little Tikes My Real...\u001b[0m\n", - "\u001b[93m88: Truth: $256.95. Errors (k=1, k=20, k=5000): ($6.95, $24.48, $38.05) Item: Studio M Peace and H...\u001b[0m\n", - "\u001b[93m89: Truth: $30.99. Errors (k=1, k=20, k=5000): ($10.99, $9.62, $5.89) Item: MyVolts 12V Power Su...\u001b[0m\n", - "\u001b[93m90: Truth: $569.84. Errors (k=1, k=20, k=5000): ($69.84, $22.40, $10.57) Item: Dell Latitude 7212 R...\u001b[0m\n", - "\u001b[93m91: Truth: $177.99. Errors (k=1, k=20, k=5000): ($16.99, $15.89, $17.61) Item: Covermates Contour F...\u001b[0m\n", - "\u001b[93m92: Truth: $997.99. Errors (k=1, k=20, k=5000): ($0.01, $1.90, $3.97) Item: Westin Black HDX Gri...\u001b[0m\n", - "\u001b[93m93: Truth: $219.00. Errors (k=1, k=20, k=5000): ($31.00, $26.50, $nan) Item: Fieldpiece JL2 Job L...\u001b[0m\n", - "\u001b[93m94: Truth: $225.55. Errors (k=1, k=20, k=5000): ($74.45, $63.37, $113.40) Item: hansgrohe Talis S Mo...\u001b[0m\n", - "\u001b[93m95: Truth: $495.95. Errors (k=1, k=20, k=5000): ($503.05, $207.91, $169.48) Item: G-Technology G-SPEED...\u001b[0m\n", - "\u001b[93m96: Truth: $942.37. Errors (k=1, k=20, k=5000): ($42.37, $108.81, $147.79) Item: DreamLine Shower Doo...\u001b[0m\n", - "\u001b[93m97: Truth: $1.94. Errors (k=1, k=20, k=5000): ($59.06, $63.59, $nan) Item: Sanctuary Square Bac...\u001b[0m\n", - "\u001b[93m98: Truth: $284.34. Errors (k=1, k=20, k=5000): ($15.66, $0.19, $25.22) Item: Pelican Protector 17...\u001b[0m\n", - "\u001b[93m99: Truth: $171.90. Errors (k=1, k=20, k=5000): ($30.90, $32.72, $nan) Item: Brock Replacement Dr...\u001b[0m\n", - "\u001b[93m100: Truth: $144.99. Errors (k=1, k=20, k=5000): ($24.01, $13.93, $45.54) Item: Carlinkit Ai Box Min...\u001b[0m\n", - "\u001b[93m101: Truth: $470.47. Errors (k=1, k=20, k=5000): ($70.47, $23.99, $23.80) Item: StarDot YouTube Live...\u001b[0m\n", - "\u001b[93m102: Truth: $66.95. Errors (k=1, k=20, k=5000): ($5.95, $4.84, $6.50) Item: Atomic Compatible ME...\u001b[0m\n", - "\u001b[93m103: Truth: $117.00. Errors (k=1, k=20, k=5000): ($25.00, $10.61, $nan) Item: Bandai Awakening of ...\u001b[0m\n", - "\u001b[93m104: Truth: $172.14. Errors (k=1, k=20, k=5000): ($1.14, $8.79, $45.41) Item: Fit System 62135G Pa...\u001b[0m\n", - "\u001b[93m105: Truth: $392.74. Errors (k=1, k=20, k=5000): ($8.74, $13.62, $4.43) Item: Black Horse Black Al...\u001b[0m\n", - "\u001b[93m106: Truth: $16.99. Errors (k=1, k=20, k=5000): ($2.99, $1.77, $6.57) Item: Dearsun Twinkle Star...\u001b[0m\n", - "\u001b[93m107: Truth: $1.34. Errors (k=1, k=20, k=5000): ($0.34, $0.91, $2.65) Item: Pokemon - Gallade Sp...\u001b[0m\n", - "\u001b[93m108: Truth: $349.98. Errors (k=1, k=20, k=5000): ($99.98, $119.63, $120.29) Item: Ibanez GIO Series Cl...\u001b[0m\n", - "\u001b[93m109: Truth: $370.71. Errors (k=1, k=20, k=5000): ($130.71, $84.50, $70.55) Item: Set 2 Heavy Duty 12 ...\u001b[0m\n", - "\u001b[93m110: Truth: $65.88. Errors (k=1, k=20, k=5000): ($12.88, $16.16, $nan) Item: Hairpin Table Legs 2...\u001b[0m\n", - "\u001b[93m111: Truth: $229.99. Errors (k=1, k=20, k=5000): ($10.01, $37.54, $20.24) Item: Marada Racing Seat w...\u001b[0m\n", - "\u001b[93m112: Truth: $9.14. Errors (k=1, k=20, k=5000): ($5.14, $2.90, $1.14) Item: Remington Industries...\u001b[0m\n", - "\u001b[93m113: Truth: $199.00. Errors (k=1, k=20, k=5000): ($201.00, $310.61, $294.75) Item: Acer Ultrabook, Inte...\u001b[0m\n", - "\u001b[93m114: Truth: $109.99. Errors (k=1, k=20, k=5000): ($140.01, $145.60, $133.29) Item: ICBEAMER 7 RGB LED H...\u001b[0m\n", - "\u001b[93m115: Truth: $570.42. Errors (k=1, k=20, k=5000): ($194.42, $215.73, $190.63) Item: R1 Concepts Front Re...\u001b[0m\n", - "\u001b[93m116: Truth: $279.99. Errors (k=1, k=20, k=5000): ($20.01, $18.13, $8.97) Item: Camplux 2.64 GPM Tan...\u001b[0m\n", - "\u001b[93m117: Truth: $30.99. Errors (k=1, k=20, k=5000): ($6.01, $4.87, $11.35) Item: KNOKLOCK 10 Pack 3.7...\u001b[0m\n", - "\u001b[93m118: Truth: $31.99. Errors (k=1, k=20, k=5000): ($13.01, $12.76, $nan) Item: Valley Enterprises Y...\u001b[0m\n", - "\u001b[93m119: Truth: $15.90. Errors (k=1, k=20, k=5000): ($13.10, $12.01, $nan) Item: G9 LED Light 100W re...\u001b[0m\n", - "\u001b[93m120: Truth: $45.99. Errors (k=1, k=20, k=5000): ($24.01, $41.82, $52.45) Item: ZCHAOZ 4 Lights Anti...\u001b[0m\n", - "\u001b[93m121: Truth: $113.52. Errors (k=1, k=20, k=5000): ($136.48, $80.11, $91.31) Item: Honeywell Honeywell ...\u001b[0m\n", - "\u001b[93m122: Truth: $516.99. Errors (k=1, k=20, k=5000): ($216.99, $179.92, $150.81) Item: Patriot Exhaust 1-7/...\u001b[0m\n", - "\u001b[93m123: Truth: $196.99. Errors (k=1, k=20, k=5000): ($105.99, $102.06, $nan) Item: Fitrite Autopart New...\u001b[0m\n", - "\u001b[93m124: Truth: $46.55. Errors (k=1, k=20, k=5000): ($5.55, $4.82, $nan) Item: Technical Precision ...\u001b[0m\n", - "\u001b[93m125: Truth: $356.99. Errors (k=1, k=20, k=5000): ($63.99, $19.09, $23.43) Item: Covercraft Carhartt ...\u001b[0m\n", - "\u001b[93m126: Truth: $319.95. Errors (k=1, k=20, k=5000): ($20.95, $22.51, $23.82) Item: Sennheiser SD Pro 2 ...\u001b[0m\n", - "\u001b[93m127: Truth: $96.06. Errors (k=1, k=20, k=5000): ($4.94, $18.64, $nan) Item: Hitachi Mass Air Flo...\u001b[0m\n", - "\u001b[93m128: Truth: $190.99. Errors (k=1, k=20, k=5000): ($59.01, $0.10, $45.82) Item: AmScope LED Cordless...\u001b[0m\n", - "\u001b[93m129: Truth: $257.95. Errors (k=1, k=20, k=5000): ($196.95, $194.13, $nan) Item: Front Left Driver Si...\u001b[0m\n", - "\u001b[93m130: Truth: $62.95. Errors (k=1, k=20, k=5000): ($51.05, $55.18, $nan) Item: Premium Replica Hubc...\u001b[0m\n", - "\u001b[93m131: Truth: $47.66. Errors (k=1, k=20, k=5000): ($6.34, $8.36, $nan) Item: Excellerations Phoni...\u001b[0m\n", - "\u001b[93m132: Truth: $226.99. Errors (k=1, k=20, k=5000): ($23.01, $72.83, $68.60) Item: RC4WD BigDog Dual Ax...\u001b[0m\n", - "\u001b[93m133: Truth: $359.95. Errors (k=1, k=20, k=5000): ($109.95, $70.13, $70.32) Item: Unknown Stage 2 Clut...\u001b[0m\n", - "\u001b[93m134: Truth: $78.40. Errors (k=1, k=20, k=5000): ($37.40, $12.13, $nan) Item: Dodge Ram 1500 Mopar...\u001b[0m\n", - "\u001b[93m135: Truth: $172.77. Errors (k=1, k=20, k=5000): ($18.77, $12.86, $6.44) Item: Pro Comp Alloys Seri...\u001b[0m\n", - "\u001b[93m136: Truth: $316.45. Errors (k=1, k=20, k=5000): ($13.55, $8.57, $24.70) Item: Detroit Axle - Front...\u001b[0m\n", - "\u001b[93m137: Truth: $87.99. Errors (k=1, k=20, k=5000): ($3.01, $3.44, $nan) Item: ECCPP Rear Wheel Axl...\u001b[0m\n", - "\u001b[93m138: Truth: $226.63. Errors (k=1, k=20, k=5000): ($23.37, $5.47, $24.09) Item: Dell Latitude E6520 ...\u001b[0m\n", - "\u001b[93m139: Truth: $31.49. Errors (k=1, k=20, k=5000): ($10.49, $4.79, $6.17) Item: F FIERCE CYCLE 251pc...\u001b[0m\n", - "\u001b[93m140: Truth: $196.00. Errors (k=1, k=20, k=5000): ($44.00, $0.44, $36.64) Item: Flash Furniture 4 Pk...\u001b[0m\n", - "\u001b[93m141: Truth: $78.40. Errors (k=1, k=20, k=5000): ($2.60, $24.09, $nan) Item: B&M 30287 Throttle V...\u001b[0m\n", - "\u001b[93m142: Truth: $116.25. Errors (k=1, k=20, k=5000): ($24.75, $29.03, $nan) Item: Gates TCK226 PowerGr...\u001b[0m\n", - "\u001b[93m143: Truth: $112.78. Errors (k=1, k=20, k=5000): ($28.22, $26.80, $29.58) Item: Monroe Shocks & Stru...\u001b[0m\n", - "\u001b[93m144: Truth: $27.32. Errors (k=1, k=20, k=5000): ($13.68, $25.82, $nan) Item: Feit Electric 35W EQ...\u001b[0m\n", - "\u001b[93m145: Truth: $145.91. Errors (k=1, k=20, k=5000): ($41.91, $37.00, $nan) Item: Yellow Jacket 2806 C...\u001b[0m\n", - "\u001b[93m146: Truth: $171.09. Errors (k=1, k=20, k=5000): ($30.09, $23.32, $nan) Item: Garage-Pro Tailgate ...\u001b[0m\n", - "\u001b[93m147: Truth: $167.95. Errors (k=1, k=20, k=5000): ($23.95, $28.77, $nan) Item: 3M Perfect It Buffin...\u001b[0m\n", - "\u001b[93m148: Truth: $28.49. Errors (k=1, k=20, k=5000): ($17.51, $14.67, $nan) Item: Chinese Style Dollho...\u001b[0m\n", - "\u001b[93m149: Truth: $122.23. Errors (k=1, k=20, k=5000): ($51.23, $56.28, $nan) Item: Generic NRG Innovati...\u001b[0m\n", - "\u001b[93m150: Truth: $32.99. Errors (k=1, k=20, k=5000): ($7.01, $8.25, $20.10) Item: Learning Resources C...\u001b[0m\n", - "\u001b[93m151: Truth: $71.20. Errors (k=1, k=20, k=5000): ($29.80, $35.02, $nan) Item: Bosch Automotive 154...\u001b[0m\n", - "\u001b[93m152: Truth: $112.75. Errors (k=1, k=20, k=5000): ($51.75, $48.46, $nan) Item: Case of 24-2 Inch Bl...\u001b[0m\n", - "\u001b[93m153: Truth: $142.43. Errors (k=1, k=20, k=5000): ($39.43, $34.56, $nan) Item: MOCA Engine Water Pu...\u001b[0m\n", - "\u001b[93m154: Truth: $398.99. Errors (k=1, k=20, k=5000): ($99.99, $89.42, $80.00) Item: SAREMAS Foot Step Ba...\u001b[0m\n", - "\u001b[93m155: Truth: $449.00. Errors (k=1, k=20, k=5000): ($151.00, $151.79, $141.05) Item: Gretsch G9210 Square...\u001b[0m\n", - "\u001b[93m156: Truth: $189.00. Errors (k=1, k=20, k=5000): ($61.00, $2.60, $nan) Item: NikoMaku Mirror Dash...\u001b[0m\n", - "\u001b[93m157: Truth: $120.91. Errors (k=1, k=20, k=5000): ($9.09, $23.28, $nan) Item: Fenix HP25R v2.0 USB...\u001b[0m\n", - "\u001b[93m158: Truth: $203.53. Errors (k=1, k=20, k=5000): ($31.53, $33.44, $30.67) Item: R&L Racing Heavy Dut...\u001b[0m\n", - "\u001b[93m159: Truth: $349.99. Errors (k=1, k=20, k=5000): ($99.99, $75.63, $79.88) Item: Garmin GPSMAP 64sx, ...\u001b[0m\n", - "\u001b[93m160: Truth: $34.35. Errors (k=1, k=20, k=5000): ($23.35, $22.26, $16.74) Item: Brown 5-7/8 X 8-1/2 ...\u001b[0m\n", - "\u001b[93m161: Truth: $384.99. Errors (k=1, k=20, k=5000): ($85.99, $79.46, $52.71) Item: GAOMON PD2200 Pen Di...\u001b[0m\n", - "\u001b[93m162: Truth: $211.00. Errors (k=1, k=20, k=5000): ($25.00, $27.41, $17.52) Item: VXMOTOR for 97-03 Fo...\u001b[0m\n", - "\u001b[93m163: Truth: $129.00. Errors (k=1, k=20, k=5000): ($121.00, $41.15, $69.57) Item: HP EliteBook 2540p I...\u001b[0m\n", - "\u001b[93m164: Truth: $111.45. Errors (k=1, k=20, k=5000): ($87.45, $82.08, $nan) Item: Green EPX Mixing Noz...\u001b[0m\n", - "\u001b[93m165: Truth: $81.12. Errors (k=1, k=20, k=5000): ($50.12, $46.87, $nan) Item: Box Partners 6 1/4 x...\u001b[0m\n", - "\u001b[93m166: Truth: $457.08. Errors (k=1, k=20, k=5000): ($57.08, $81.73, $inf) Item: Vixen Air 1/2 NPT Ai...\u001b[0m\n", - "\u001b[93m167: Truth: $49.49. Errors (k=1, k=20, k=5000): ($40.51, $41.52, $nan) Item: Smart Floor Lamp, Mu...\u001b[0m\n", - "\u001b[93m168: Truth: $80.56. Errors (k=1, k=20, k=5000): ($49.56, $47.97, $nan) Item: SOZG 324mm Wheelbase...\u001b[0m\n", - "\u001b[93m169: Truth: $278.39. Errors (k=1, k=20, k=5000): ($10.61, $8.78, $31.60) Item: Mickey Thompson ET S...\u001b[0m\n", - "\u001b[93m170: Truth: $364.50. Errors (k=1, k=20, k=5000): ($109.50, $97.54, $69.42) Item: Pirelli 106W XL RFT ...\u001b[0m\n", - "\u001b[93m171: Truth: $378.99. Errors (k=1, k=20, k=5000): ($78.99, $93.39, $48.28) Item: Torklift C3212 Rear ...\u001b[0m\n", - "\u001b[93m172: Truth: $165.28. Errors (k=1, k=20, k=5000): ($27.72, $19.36, $74.65) Item: Cardone Remanufactur...\u001b[0m\n", - "\u001b[93m173: Truth: $56.74. Errors (k=1, k=20, k=5000): ($15.74, $3.36, $nan) Item: Kidde AccessPoint 00...\u001b[0m\n", - "\u001b[93m174: Truth: $307.95. Errors (k=1, k=20, k=5000): ($7.95, $3.05, $57.75) Item: 3M Protecta Self Ret...\u001b[0m\n", - "\u001b[93m175: Truth: $38.00. Errors (k=1, k=20, k=5000): ($11.00, $18.24, $nan) Item: Plantronics Wired He...\u001b[0m\n", - "\u001b[93m176: Truth: $53.00. Errors (k=1, k=20, k=5000): ($47.00, $65.60, $nan) Item: Logitech K750 Wirele...\u001b[0m\n", - "\u001b[93m177: Truth: $498.00. Errors (k=1, k=20, k=5000): ($98.00, $26.16, $21.81) Item: Olympus PEN E-PL9 Bo...\u001b[0m\n", - "\u001b[93m178: Truth: $53.99. Errors (k=1, k=20, k=5000): ($87.01, $88.58, $nan) Item: Beck/Arnley Hub & Be...\u001b[0m\n", - "\u001b[93m179: Truth: $350.00. Errors (k=1, k=20, k=5000): ($0.00, $4.38, $10.28) Item: Eibach Pro-Kit Perfo...\u001b[0m\n", - "\u001b[93m180: Truth: $299.95. Errors (k=1, k=20, k=5000): ($100.05, $44.21, $65.77) Item: LEGO DC Batman 1989 ...\u001b[0m\n", - "\u001b[93m181: Truth: $94.93. Errors (k=1, k=20, k=5000): ($3.93, $8.68, $nan) Item: Kingston Brass Resto...\u001b[0m\n", - "\u001b[93m182: Truth: $379.00. Errors (k=1, k=20, k=5000): ($80.00, $46.76, $18.19) Item: Polk Vanishing Serie...\u001b[0m\n", - "\u001b[93m183: Truth: $299.95. Errors (k=1, k=20, k=5000): ($49.95, $23.89, $18.86) Item: Spec-D Tuning LED Pr...\u001b[0m\n", - "\u001b[93m184: Truth: $24.99. Errors (k=1, k=20, k=5000): ($9.99, $8.24, $5.58) Item: RICHMOND & FINCH Air...\u001b[0m\n", - "\u001b[93m185: Truth: $41.04. Errors (k=1, k=20, k=5000): ($63.96, $64.91, $nan) Item: LFA Industries - mm ...\u001b[0m\n", - "\u001b[93m186: Truth: $327.90. Errors (k=1, k=20, k=5000): ($87.90, $104.82, $106.55) Item: SAUTVS LED Headlight...\u001b[0m\n", - "\u001b[93m187: Truth: $10.99. Errors (k=1, k=20, k=5000): ($10.01, $9.26, $19.71) Item: 2 Pack Combo Womens ...\u001b[0m\n", - "\u001b[93m188: Truth: $14.99. Errors (k=1, k=20, k=5000): ($0.01, $0.01, $0.18) Item: Arepa - Venezuelan c...\u001b[0m\n", - "\u001b[93m189: Truth: $84.95. Errors (k=1, k=20, k=5000): ($43.95, $41.84, $nan) Item: Schlage Lock Company...\u001b[0m\n", - "\u001b[93m190: Truth: $111.00. Errors (k=1, k=20, k=5000): ($10.00, $9.44, $nan) Item: Techni Mobili White ...\u001b[0m\n", - "\u001b[93m191: Truth: $123.73. Errors (k=1, k=20, k=5000): ($32.27, $44.59, $84.12) Item: Special Lite Product...\u001b[0m\n", - "\u001b[93m192: Truth: $557.38. Errors (k=1, k=20, k=5000): ($58.38, $36.33, $27.73) Item: Tascam Digital Porta...\u001b[0m\n", - "\u001b[93m193: Truth: $95.55. Errors (k=1, k=20, k=5000): ($3.55, $1.53, $nan) Item: Glow Lighting Vista ...\u001b[0m\n", - "\u001b[93m194: Truth: $154.00. Errors (k=1, k=20, k=5000): ($15.00, $2.99, $17.68) Item: Z3 Wind Deflector, S...\u001b[0m\n", - "\u001b[93m195: Truth: $198.99. Errors (k=1, k=20, k=5000): ($101.01, $15.42, $nan) Item: Olympus E-20 5MP Dig...\u001b[0m\n", - "\u001b[93m196: Truth: $430.44. Errors (k=1, k=20, k=5000): ($180.44, $182.10, $nan) Item: PHYNEDI 1 1000 World...\u001b[0m\n", - "\u001b[93m197: Truth: $45.67. Errors (k=1, k=20, k=5000): ($27.67, $24.15, $8.72) Item: YANGHUAN Unstable Un...\u001b[0m\n", - "\u001b[93m198: Truth: $249.00. Errors (k=1, k=20, k=5000): ($51.00, $35.81, $46.25) Item: Interlogix NetworX T...\u001b[0m\n", - "\u001b[93m199: Truth: $42.99. Errors (k=1, k=20, k=5000): ($21.99, $18.78, $nan) Item: Steering Damper,Univ...\u001b[0m\n", - "\u001b[93m200: Truth: $181.33. Errors (k=1, k=20, k=5000): ($37.33, $47.77, $44.44) Item: Amprobe TIC 410A Hot...\u001b[0m\n", - "\u001b[93m201: Truth: $6.03. Errors (k=1, k=20, k=5000): ($3.03, $0.78, $0.72) Item: MyCableMart 3.5mm Pl...\u001b[0m\n", - "\u001b[93m202: Truth: $29.99. Errors (k=1, k=20, k=5000): ($15.01, $12.97, $19.43) Item: OtterBox + Pop Symme...\u001b[0m\n", - "\u001b[93m203: Truth: $899.00. Errors (k=1, k=20, k=5000): ($100.00, $182.77, $225.08) Item: Dell XPS Desktop ( I...\u001b[0m\n", - "\u001b[93m204: Truth: $399.99. Errors (k=1, k=20, k=5000): ($0.01, $174.15, $172.38) Item: Franklin Iron Works ...\u001b[0m\n", - "\u001b[93m205: Truth: $4.66. Errors (k=1, k=20, k=5000): ($0.66, $6.52, $24.80) Item: Avery Legal Dividers...\u001b[0m\n", - "\u001b[93m206: Truth: $261.41. Errors (k=1, k=20, k=5000): ($77.41, $95.39, $24.61) Item: Moen 8346 Commercial...\u001b[0m\n", - "\u001b[93m207: Truth: $136.97. Errors (k=1, k=20, k=5000): ($4.97, $1.36, $11.91) Item: Carlisle Versa Trail...\u001b[0m\n", - "\u001b[93m208: Truth: $79.00. Errors (k=1, k=20, k=5000): ($70.00, $95.33, $nan) Item: SUNWAYFOTO 44mm Trip...\u001b[0m\n", - "\u001b[93m209: Truth: $444.99. Errors (k=1, k=20, k=5000): ($144.99, $97.76, $60.56) Item: NanoBeam AC 4 Units ...\u001b[0m\n", - "\u001b[93m210: Truth: $411.94. Errors (k=1, k=20, k=5000): ($88.06, $114.89, $inf) Item: WULF 4 Front 2 Rear ...\u001b[0m\n", - "\u001b[93m211: Truth: $148.40. Errors (k=1, k=20, k=5000): ($27.40, $29.50, $nan) Item: Alera ALEVABFMC Vale...\u001b[0m\n", - "\u001b[93m212: Truth: $244.99. Errors (k=1, k=20, k=5000): ($5.01, $78.49, $nan) Item: YU-GI-OH! Ignition A...\u001b[0m\n", - "\u001b[93m213: Truth: $86.50. Errors (k=1, k=20, k=5000): ($28.50, $54.04, $nan) Item: 48 x 36 Extra-Large ...\u001b[0m\n", - "\u001b[93m214: Truth: $297.95. Errors (k=1, k=20, k=5000): ($158.95, $156.86, $115.57) Item: Dell Latitude D620 R...\u001b[0m\n", - "\u001b[93m215: Truth: $399.99. Errors (k=1, k=20, k=5000): ($0.99, $46.74, $51.32) Item: acer Aspire 5 Laptop...\u001b[0m\n", - "\u001b[93m216: Truth: $599.00. Errors (k=1, k=20, k=5000): ($299.00, $317.34, $244.65) Item: Elk 30 by 6-Inch Viv...\u001b[0m\n", - "\u001b[93m217: Truth: $105.99. Errors (k=1, k=20, k=5000): ($194.01, $42.37, $nan) Item: Barbie Top Model Dol...\u001b[0m\n", - "\u001b[93m218: Truth: $689.00. Errors (k=1, k=20, k=5000): ($189.00, $130.34, $110.61) Item: Danby Designer 20-In...\u001b[0m\n", - "\u001b[93m219: Truth: $404.99. Errors (k=1, k=20, k=5000): ($95.01, $116.74, $inf) Item: FixtureDisplays® Met...\u001b[0m\n", - "\u001b[93m220: Truth: $207.76. Errors (k=1, k=20, k=5000): ($15.76, $16.58, $28.16) Item: ACDelco GM Original ...\u001b[0m\n", - "\u001b[93m221: Truth: $171.82. Errors (k=1, k=20, k=5000): ($30.82, $15.15, $nan) Item: EBC Premium Street B...\u001b[0m\n", - "\u001b[93m222: Truth: $293.24. Errors (k=1, k=20, k=5000): ($6.76, $22.32, $27.57) Item: FXR Men's Boost FX J...\u001b[0m\n", - "\u001b[93m223: Truth: $374.95. Errors (k=1, k=20, k=5000): ($25.05, $39.60, $62.80) Item: SuperATV Scratch Res...\u001b[0m\n", - "\u001b[93m224: Truth: $111.99. Errors (k=1, k=20, k=5000): ($27.99, $12.01, $12.87) Item: SBU 3 Layer All Weat...\u001b[0m\n", - "\u001b[93m225: Truth: $42.99. Errors (k=1, k=20, k=5000): ($0.01, $3.20, $nan) Item: 2 Pack Outdoor Broch...\u001b[0m\n", - "\u001b[93m226: Truth: $116.71. Errors (k=1, k=20, k=5000): ($24.29, $21.02, $22.60) Item: Monroe Shocks & Stru...\u001b[0m\n", - "\u001b[93m227: Truth: $118.61. Errors (k=1, k=20, k=5000): ($25.39, $43.88, $78.15) Item: Elements of Design M...\u001b[0m\n", - "\u001b[93m228: Truth: $147.12. Errors (k=1, k=20, k=5000): ($6.12, $20.59, $nan) Item: GM Genuine Parts Air...\u001b[0m\n", - "\u001b[93m229: Truth: $119.99. Errors (k=1, k=20, k=5000): ($10.01, $38.84, $34.94) Item: Baseus USB C Docking...\u001b[0m\n", - "\u001b[93m230: Truth: $369.98. Errors (k=1, k=20, k=5000): ($69.98, $41.61, $6.95) Item: Whitehall™ Personali...\u001b[0m\n", - "\u001b[93m231: Truth: $315.55. Errors (k=1, k=20, k=5000): ($65.55, $76.95, $69.43) Item: Pro Circuit Works Pi...\u001b[0m\n", - "\u001b[93m232: Truth: $190.99. Errors (k=1, k=20, k=5000): ($109.01, $70.62, $85.72) Item: HYANKA 15 1200W Prof...\u001b[0m\n", - "\u001b[93m233: Truth: $155.00. Errors (k=1, k=20, k=5000): ($144.00, $86.94, $110.85) Item: Bluetooth X6BT Card ...\u001b[0m\n", - "\u001b[93m234: Truth: $349.99. Errors (k=1, k=20, k=5000): ($49.99, $19.31, $19.40) Item: AIRAID Cold Air Inta...\u001b[0m\n", - "\u001b[93m235: Truth: $249.99. Errors (k=1, k=20, k=5000): ($0.01, $28.79, $30.67) Item: Bostingner Shower Fa...\u001b[0m\n", - "\u001b[93m236: Truth: $42.99. Errors (k=1, k=20, k=5000): ($3.01, $2.81, $nan) Item: PIT66 Front Bumper T...\u001b[0m\n", - "\u001b[93m237: Truth: $17.99. Errors (k=1, k=20, k=5000): ($2.01, $2.03, $4.08) Item: Caseology Bumpy Comp...\u001b[0m\n", - "\u001b[93m238: Truth: $425.00. Errors (k=1, k=20, k=5000): ($25.00, $20.35, $inf) Item: Fleck 2510 Timer Mec...\u001b[0m\n", - "\u001b[93m239: Truth: $249.99. Errors (k=1, k=20, k=5000): ($0.01, $2.44, $3.71) Item: Haloview MC7108 Wire...\u001b[0m\n", - "\u001b[93m240: Truth: $138.23. Errors (k=1, k=20, k=5000): ($75.23, $78.48, $nan) Item: Schmidt Spiele - Man...\u001b[0m\n", - "\u001b[93m241: Truth: $414.99. Errors (k=1, k=20, k=5000): ($114.99, $99.95, $63.86) Item: Corsa 14333 Tip Kit ...\u001b[0m\n", - "\u001b[93m242: Truth: $168.28. Errors (k=1, k=20, k=5000): ($11.28, $7.82, $50.46) Item: Hoshizaki FM116A Fan...\u001b[0m\n", - "\u001b[93m243: Truth: $199.99. Errors (k=1, k=20, k=5000): ($99.01, $22.91, $nan) Item: BAINUO Antler Chande...\u001b[0m\n", - "\u001b[93m244: Truth: $126.70. Errors (k=1, k=20, k=5000): ($4.30, $0.14, $nan) Item: DNA MOTORING Smoke L...\u001b[0m\n", - "\u001b[93m245: Truth: $5.91. Errors (k=1, k=20, k=5000): ($1.91, $1.28, $6.22) Item: Wera Stainless 3840/...\u001b[0m\n", - "\u001b[93m246: Truth: $193.06. Errors (k=1, k=20, k=5000): ($56.94, $69.80, $74.65) Item: Celestron - PowerSee...\u001b[0m\n", - "\u001b[93m247: Truth: $249.99. Errors (k=1, k=20, k=5000): ($0.01, $5.61, $0.13) Item: NHOPEEW Android Car ...\u001b[0m\n", - "\u001b[93m248: Truth: $64.12. Errors (k=1, k=20, k=5000): ($27.88, $41.54, $nan) Item: Other Harmonica A)\n", - "F...\u001b[0m\n", - "\u001b[93m249: Truth: $114.99. Errors (k=1, k=20, k=5000): ($145.01, $145.33, $149.01) Item: Harley Air Filter Ve...\u001b[0m\n", - "\u001b[93m250: Truth: $926.00. Errors (k=1, k=20, k=5000): ($526.00, $557.60, $461.43) Item: Elite Screens Edge F...\u001b[0m\n", - "\n", - "--- Optimal k Analysis Report ---\n", - "Model: model-2025-10-23_23.41.24:v22\n", - "Inferences Run: 250\n", - "Analyzed k from 1 to 5000\n", - "===================================\n", - "==> Best k: 1108\n", - "==> Minimum Average Error: $nan\n", - "===================================\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import math\n", - "import torch\n", - "import torch.nn.functional as F\n", - "\n", - "# ... (All your other functions like calculate_weighted_price,\n", - "# get_top_k_predictions, set_seed, etc. remain the same) ...\n", - "\n", - "class Tester:\n", - " \"\"\"\n", - " MODIFIED: This class now also analyzes and plots probability spread.\n", - " \"\"\"\n", - " def __init__(self, predictor, data, title=None, size=250):\n", - " self.predictor = predictor\n", - " self.data = data\n", - " self.title = title or predictor.__name__.replace(\"_\", \" \").title()\n", - " self.size = size\n", - " self.truths = []\n", - "\n", - " # From previous step\n", - " self.all_k_errors = []\n", - " self.max_k = TOP_K\n", - "\n", - " # --- NEW FOR SPREAD ANALYSIS ---\n", - " # Store the list of probabilities for each inference\n", - " self.all_prob_lists = []\n", - " # Store the standard deviation of probs for each inference\n", - " self.prob_std_devs = []\n", - " # -------------------------------\n", - "\n", - " def run_datapoint(self, i):\n", - " datapoint = self.data[i]\n", - " base_prompt = datapoint[\"text\"]\n", - " prompt = make_prompt(base_prompt)\n", - " truth = datapoint[\"price\"]\n", - " self.truths.append(truth)\n", - "\n", - " # 1. Get the raw lists of prices and probabilities\n", - " prices, probabilities = self.predictor(prompt)\n", - "\n", - " # --- NEW FOR SPREAD ANALYSIS ---\n", - " # Store the full list of probabilities\n", - " self.all_prob_lists.append(probabilities)\n", - "\n", - " if probabilities:\n", - " # Calculate and store the spread (std dev) of this prob list\n", - " self.prob_std_devs.append(np.std(probabilities))\n", - " else:\n", - " # No probabilities, append 0 for spread\n", - " self.prob_std_devs.append(0.0)\n", - " # -------------------------------\n", - "\n", - " errors_for_this_datapoint = []\n", - "\n", - " if not prices:\n", - " print(f\"{i+1}: No valid prices found. Truth: ${truth:,.2f}.\")\n", - " error = np.abs(0 - truth)\n", - " errors_for_this_datapoint = [error] * self.max_k\n", - " self.all_k_errors.append(errors_for_this_datapoint)\n", - " return\n", - "\n", - " # 2. Iterate from k=1 up to max_k\n", - " for k in range(1, self.max_k + 1):\n", - " k_prices = prices[:k]\n", - " k_probabilities = probabilities[:k]\n", - " guess = calculate_weighted_price(k_prices, k_probabilities)\n", - " error = np.abs(guess - truth)\n", - " errors_for_this_datapoint.append(error)\n", - "\n", - " self.all_k_errors.append(errors_for_this_datapoint)\n", - "\n", - " # (The rest of this method's print logic is unchanged)\n", - " title = datapoint[\"text\"].split(\"\\n\\n\")[1][:20] + \"...\"\n", - " k_1_err = errors_for_this_datapoint[0]\n", - " k_20_err = errors_for_this_datapoint[19]\n", - " k_max_err = errors_for_this_datapoint[-1]\n", - "\n", - " print(f\"{COLOR_MAP.get('orange', '')}{i+1}: Truth: ${truth:,.2f}. \"\n", - " f\"Errors (k=1, k=20, k={self.max_k}): \"\n", - " f\"(${k_1_err:,.2f}, ${k_20_err:,.2f}, ${k_max_err:,.2f}) \"\n", - " f\"Item: {title}{RESET}\")\n", - "\n", - " def plot_k_vs_error(self, k_values, avg_errors_by_k, best_k, min_error):\n", - " # (This function is unchanged from before)\n", - " plt.figure(figsize=(12, 8))\n", - " plt.plot(k_values, avg_errors_by_k, label='Average Error vs. k')\n", - " plt.axvline(x=best_k, color='red', linestyle='--',\n", - " label=f'Best k = {best_k} (Avg Error: ${min_error:,.2f})')\n", - " plt.xlabel('Number of Top Probabilities/Prices (k)')\n", - " plt.ylabel('Average Absolute Error ($)')\n", - " plt.title(f'Optimal k Analysis for {self.title}')\n", - " plt.legend()\n", - " plt.grid(True, which='both', linestyle='--', linewidth=0.5)\n", - " plt.xlim(left=1)\n", - " plt.show()\n", - "\n", - " def plot_probability_spread(self, idx_min_std, idx_med_std, idx_max_std):\n", - " \"\"\"\n", - " NEW: Plots the probability distributions for the inferences\n", - " with minimum, median, and maximum spread.\n", - " \"\"\"\n", - " # Get the lists of probabilities\n", - " probs_min = self.all_prob_lists[idx_min_std]\n", - " probs_med = self.all_prob_lists[idx_med_std]\n", - " probs_max = self.all_prob_lists[idx_max_std]\n", - "\n", - " # Get the std values\n", - " std_min = self.prob_std_devs[idx_min_std]\n", - " std_med = self.prob_std_devs[idx_med_std]\n", - " std_max = self.prob_std_devs[idx_max_std]\n", - "\n", - " # Create the figure with 3 subplots\n", - " fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(18, 7), sharey=True)\n", - " fig.suptitle('Probability Distribution Spread Analysis', fontsize=16)\n", - "\n", - " # Helper function for plotting a 1D strip plot\n", - " def plot_strip(ax, probs, title):\n", - " if not probs:\n", - " ax.set_title(f\"{title}\\n(No probabilities found)\")\n", - " return\n", - "\n", - " # Create an array of zeros for the x-axis to stack points vertically\n", - " # Add a small amount of \"jitter\" (random noise) for better visibility\n", - " jitter = np.random.normal(0, 0.01, size=len(probs))\n", - " ax.scatter(jitter, probs, alpha=0.5)\n", - " ax.set_title(title)\n", - " ax.set_xlabel(\"Jitter\")\n", - " ax.get_xaxis().set_ticks([]) # Hide x-axis ticks\n", - "\n", - " # Plot 1: Minimum Spread\n", - " plot_strip(ax1, probs_min,\n", - " f'Inference {idx_min_std} (Lowest Spread)\\nStd Dev: {std_min:.6f}')\n", - " ax1.set_ylabel('Probability')\n", - "\n", - " # Plot 2: Median Spread\n", - " plot_strip(ax2, probs_med,\n", - " f'Inference {idx_med_std} (Median Spread)\\nStd Dev: {std_med:.6f}')\n", - "\n", - " # Plot 3: Maximum Spread\n", - " plot_strip(ax3, probs_max,\n", - " f'Inference {idx_max_std} (Highest Spread)\\nStd Dev: {std_max:.6f}')\n", - "\n", - " plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n", - " plt.show()\n", - "\n", - " def report(self):\n", - " \"\"\"\n", - " MODIFIED: Now also triggers the spread analysis plot.\n", - " \"\"\"\n", - " # --- 1. Optimal k Analysis (Same as before) ---\n", - " errors_array = np.array(self.all_k_errors)\n", - " avg_errors_by_k = np.mean(errors_array, axis=0)\n", - " best_k_index = np.argmin(avg_errors_by_k)\n", - " min_error = avg_errors_by_k[best_k_index]\n", - " best_k = best_k_index + 1\n", - "\n", - " print(\"\\n--- Optimal k Analysis Report ---\")\n", - " print(f\"Model: {self.title}\")\n", - " print(f\"Inferences Run: {self.size}\")\n", - " print(f\"Analyzed k from 1 to {self.max_k}\")\n", - " print(f\"===================================\")\n", - " print(f\"==> Best k: {best_k}\")\n", - " print(f\"==> Minimum Average Error: ${min_error:,.2f}\")\n", - " print(f\"===================================\")\n", - "\n", - " k_values = np.arange(1, self.max_k + 1)\n", - " self.plot_k_vs_error(k_values, avg_errors_by_k, best_k, min_error)\n", - "\n", - " # --- 2. Probability Spread Analysis (NEW) ---\n", - " if not self.prob_std_devs:\n", - " print(\"\\nNo probability spreads recorded, skipping spread plot.\")\n", - " return\n", - "\n", - " # Find the indices for min, median, and max spread\n", - " std_sorted_indices = np.argsort(self.prob_std_devs)\n", - "\n", - " idx_min_std = std_sorted_indices[0]\n", - " idx_med_std = std_sorted_indices[len(std_sorted_indices) // 2]\n", - " idx_max_std = std_sorted_indices[-1]\n", - "\n", - " print(\"\\n--- Probability Spread Analysis ---\")\n", - " print(f\"Lowest spread (std): {self.prob_std_devs[idx_min_std]:.6f} (Inference {idx_min_std})\")\n", - " print(f\"Median spread (std): {self.prob_std_devs[idx_med_std]:.6f} (Inference {idx_med_std})\")\n", - " print(f\"Highest spread (std): {self.prob_std_devs[idx_max_std]:.6f} (Inference {idx_max_std})\")\n", - "\n", - " # Call the new plotting function\n", - " self.plot_probability_spread(idx_min_std, idx_med_std, idx_max_std)\n", - "\n", - "\n", - " def run(self):\n", - " # (This function is unchanged)\n", - " for i in range(self.size):\n", - " self.run_datapoint(i)\n", - " self.report()\n", - "\n", - " @classmethod\n", - " def test(cls, function, data):\n", - " # (This function is unchanged)\n", - " cls(function, data).run()\n", - "\n", - "# --- EXECUTION (Unchanged) ---\n", - "tester = Tester(get_top_k_predictions, test, title=f\"{MODEL_ARTIFACT_NAME}:{REVISION_TAG}\")\n", - "tester.run()" - ], - "metadata": { - "id": "BSPGZmsi65z8", - "outputId": "7c4b9a43-c34e-4b15-fb3d-4f38826a7ca0", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - } - }, - "execution_count": 55, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[93m1: Truth: $374.41. Errors (k=1, k=20, k=100): ($81.41, $73.24, $67.97) Item: OEM AC Compressor w/...\u001b[0m\n", - "\u001b[93m2: Truth: $225.11. Errors (k=1, k=20, k=100): ($84.11, $80.03, $82.84) Item: Motorcraft YB3125 Fa...\u001b[0m\n", - "\u001b[93m3: Truth: $61.68. Errors (k=1, k=20, k=100): ($20.68, $15.16, $3.86) Item: Dorman Front Washer ...\u001b[0m\n", - "\u001b[93m4: Truth: $599.99. Errors (k=1, k=20, k=100): ($99.99, $102.15, $89.32) Item: HP Premium HD Plus T...\u001b[0m\n", - "\u001b[93m5: Truth: $16.99. Errors (k=1, k=20, k=100): ($7.99, $5.32, $1.49) Item: Super Switch Pickup ...\u001b[0m\n", - "\u001b[93m6: Truth: $31.99. Errors (k=1, k=20, k=100): ($19.99, $17.74, $13.03) Item: Horror Bookmarks, Re...\u001b[0m\n", - "\u001b[93m7: Truth: $101.79. Errors (k=1, k=20, k=100): ($60.79, $57.40, $45.58) Item: SK6241 - Stinger 4 G...\u001b[0m\n", - "\u001b[93m8: Truth: $289.00. Errors (k=1, k=20, k=100): ($10.00, $22.44, $12.82) Item: Godox ML60Bi LED Lig...\u001b[0m\n", - "\u001b[93m9: Truth: $635.86. Errors (k=1, k=20, k=100): ($135.86, $32.93, $33.48) Item: Randall G3 Plus Comb...\u001b[0m\n", - "\u001b[93m10: Truth: $65.99. Errors (k=1, k=20, k=100): ($44.01, $53.82, $52.82) Item: HOLDWILL 6 Pack LED ...\u001b[0m\n", - "\u001b[93m11: Truth: $254.21. Errors (k=1, k=20, k=100): ($45.79, $49.20, $45.24) Item: Viking Horns 3 Gallo...\u001b[0m\n", - "\u001b[93m12: Truth: $412.99. Errors (k=1, k=20, k=100): ($12.99, $18.22, $11.09) Item: CURT 70110 Custom To...\u001b[0m\n", - "\u001b[93m13: Truth: $205.50. Errors (k=1, k=20, k=100): ($34.50, $58.06, $42.22) Item: Solar HAMMERED BRONZ...\u001b[0m\n", - "\u001b[93m14: Truth: $248.23. Errors (k=1, k=20, k=100): ($51.77, $21.44, $24.60) Item: COSTWAY Electric Tum...\u001b[0m\n", - "\u001b[93m15: Truth: $399.00. Errors (k=1, k=20, k=100): ($99.00, $51.34, $28.05) Item: FREE SIGNAL TV Trans...\u001b[0m\n", - "\u001b[93m16: Truth: $373.94. Errors (k=1, k=20, k=100): ($35.94, $28.00, $26.70) Item: Bilstein 5100 Monotu...\u001b[0m\n", - "\u001b[93m17: Truth: $92.89. Errors (k=1, k=20, k=100): ($2.11, $4.40, $1.19) Item: Sangean K-200 Multi-...\u001b[0m\n", - "\u001b[93m18: Truth: $51.99. Errors (k=1, k=20, k=100): ($52.01, $67.21, $72.74) Item: Charles Leonard Magn...\u001b[0m\n", - "\u001b[93m19: Truth: $179.00. Errors (k=1, k=20, k=100): ($20.00, $65.59, $64.52) Item: Gigabyte AMD Radeon ...\u001b[0m\n", - "\u001b[93m20: Truth: $19.42. Errors (k=1, k=20, k=100): ($0.42, $2.47, $2.24) Item: 3dRose LLC 8 x 8 x 0...\u001b[0m\n", - "\u001b[93m21: Truth: $539.95. Errors (k=1, k=20, k=100): ($40.95, $17.93, $10.77) Item: ROKINON 85mm F1.4 Au...\u001b[0m\n", - "\u001b[93m22: Truth: $147.67. Errors (k=1, k=20, k=100): ($40.67, $43.67, $37.13) Item: Headlight Assembly C...\u001b[0m\n", - "\u001b[93m23: Truth: $24.99. Errors (k=1, k=20, k=100): ($24.01, $18.60, $27.46) Item: ASI NAUTICAL 2.5 Inc...\u001b[0m\n", - "\u001b[93m24: Truth: $149.00. Errors (k=1, k=20, k=100): ($80.00, $68.07, $66.76) Item: Behringer TUBE OVERD...\u001b[0m\n", - "\u001b[93m25: Truth: $16.99. Errors (k=1, k=20, k=100): ($4.99, $4.12, $2.07) Item: Fun Express Insect F...\u001b[0m\n", - "\u001b[93m26: Truth: $7.99. Errors (k=1, k=20, k=100): ($2.01, $2.80, $7.32) Item: WAFJAMF Roller Stamp...\u001b[0m\n", - "\u001b[93m27: Truth: $199.99. Errors (k=1, k=20, k=100): ($13.99, $15.86, $6.92) Item: Capulina Tiffany Flo...\u001b[0m\n", - "\u001b[93m28: Truth: $251.45. Errors (k=1, k=20, k=100): ($1.45, $6.34, $3.83) Item: Apple Watch Series 6...\u001b[0m\n", - "\u001b[93m29: Truth: $231.62. Errors (k=1, k=20, k=100): ($60.62, $69.85, $51.89) Item: ICON 01725 Tandem Ax...\u001b[0m\n", - "\u001b[93m30: Truth: $135.00. Errors (k=1, k=20, k=100): ($35.00, $52.41, $46.66) Item: SanDisk 128GB Ultra ...\u001b[0m\n", - "\u001b[93m31: Truth: $356.62. Errors (k=1, k=20, k=100): ($163.62, $137.01, $135.61) Item: Velvac - 715427\n", - "2020...\u001b[0m\n", - "\u001b[93m32: Truth: $257.99. Errors (k=1, k=20, k=100): ($7.99, $40.21, $38.93) Item: TCMT Passenger Backr...\u001b[0m\n", - "\u001b[93m33: Truth: $27.99. Errors (k=1, k=20, k=100): ($11.99, $10.64, $1.70) Item: Alnicov 63.5MM Brass...\u001b[0m\n", - "\u001b[93m34: Truth: $171.20. Errors (k=1, k=20, k=100): ($80.20, $55.81, $54.29) Item: Subaru Forester Outb...\u001b[0m\n", - "\u001b[93m35: Truth: $225.00. Errors (k=1, k=20, k=100): ($24.00, $38.28, $58.60) Item: Richmond Auto Uphols...\u001b[0m\n", - "\u001b[93m36: Truth: $105.00. Errors (k=1, k=20, k=100): ($54.00, $64.77, $73.80) Item: AP-39 Automotive Pai...\u001b[0m\n", - "\u001b[93m37: Truth: $299.99. Errors (k=1, k=20, k=100): ($0.99, $31.50, $49.48) Item: Road Top Wireless Ca...\u001b[0m\n", - "\u001b[93m38: Truth: $535.09. Errors (k=1, k=20, k=100): ($9.09, $33.06, $35.93) Item: Gibson Performance E...\u001b[0m\n", - "\u001b[93m39: Truth: $12.33. Errors (k=1, k=20, k=100): ($0.33, $3.27, $7.63) Item: Bella Tunno Happy Li...\u001b[0m\n", - "\u001b[93m40: Truth: $84.99. Errors (k=1, k=20, k=100): ($4.99, $1.12, $5.35) Item: CANMORE H300 Handhel...\u001b[0m\n", - "\u001b[93m41: Truth: $15.99. Errors (k=1, k=20, k=100): ($2.99, $0.54, $1.80) Item: DCPOWER AC Adapter C...\u001b[0m\n", - "\u001b[93m42: Truth: $62.44. Errors (k=1, k=20, k=100): ($17.44, $16.50, $3.31) Item: Sharp, Commercial De...\u001b[0m\n", - "\u001b[93m43: Truth: $82.99. Errors (k=1, k=20, k=100): ($17.99, $20.16, $10.23) Item: Melissa & Doug Lifel...\u001b[0m\n", - "\u001b[93m44: Truth: $599.95. Errors (k=1, k=20, k=100): ($201.95, $213.92, $204.53) Item: Sony SSCS8 2-Way Cen...\u001b[0m\n", - "\u001b[93m45: Truth: $194.99. Errors (k=1, k=20, k=100): ($54.01, $45.19, $38.85) Item: ASUS Chromebook CX1,...\u001b[0m\n", - "\u001b[93m46: Truth: $344.95. Errors (k=1, k=20, k=100): ($55.05, $53.22, $53.36) Item: FiiO X7 32GB Hi-Res ...\u001b[0m\n", - "\u001b[93m47: Truth: $37.99. Errors (k=1, k=20, k=100): ($2.01, $2.70, $6.62) Item: TORRO Leather Case C...\u001b[0m\n", - "\u001b[93m48: Truth: $224.35. Errors (k=1, k=20, k=100): ($19.35, $15.58, $1.77) Item: Universal Air Condit...\u001b[0m\n", - "\u001b[93m49: Truth: $814.00. Errors (k=1, k=20, k=100): ($14.00, $42.04, $48.29) Item: Street Series Stainl...\u001b[0m\n", - "\u001b[93m50: Truth: $439.88. Errors (k=1, k=20, k=100): ($40.88, $66.90, $64.77) Item: Lenovo IdeaPad 3 Lap...\u001b[0m\n", - "\u001b[93m51: Truth: $341.43. Errors (k=1, k=20, k=100): ($92.43, $74.61, $76.63) Item: Access Bed Covers To...\u001b[0m\n", - "\u001b[93m52: Truth: $46.78. Errors (k=1, k=20, k=100): ($1.78, $12.93, $22.48) Item: G.I. JOE Hasbro 3 3/...\u001b[0m\n", - "\u001b[93m53: Truth: $171.44. Errors (k=1, k=20, k=100): ($12.56, $6.77, $16.96) Item: T&S Brass Double Pan...\u001b[0m\n", - "\u001b[93m54: Truth: $458.00. Errors (k=1, k=20, k=100): ($158.00, $108.54, $51.39) Item: ZTUOAUMA Fuel Inject...\u001b[0m\n", - "\u001b[93m55: Truth: $130.75. Errors (k=1, k=20, k=100): ($119.25, $52.77, $40.15) Item: Hp Prime Graphing Ca...\u001b[0m\n", - "\u001b[93m56: Truth: $83.81. Errors (k=1, k=20, k=100): ($52.81, $51.58, $40.19) Item: Lowrance Nmea 2000 2...\u001b[0m\n", - "\u001b[93m57: Truth: $386.39. Errors (k=1, k=20, k=100): ($245.39, $232.53, $228.40) Item: Jeep Genuine Accesso...\u001b[0m\n", - "\u001b[93m58: Truth: $169.00. Errors (k=1, k=20, k=100): ($130.00, $47.50, $55.03) Item: GODOX CB-06 Hard Car...\u001b[0m\n", - "\u001b[93m59: Truth: $17.95. Errors (k=1, k=20, k=100): ($2.95, $1.67, $0.61) Item: Au-Tomotive Gold, IN...\u001b[0m\n", - "\u001b[93m60: Truth: $269.00. Errors (k=1, k=20, k=100): ($20.00, $40.42, $51.28) Item: Snailfly Black Roof ...\u001b[0m\n", - "\u001b[93m61: Truth: $77.77. Errors (k=1, k=20, k=100): ($8.77, $22.18, $10.43) Item: KING SHA Anti Glare ...\u001b[0m\n", - "\u001b[93m62: Truth: $88.99. Errors (k=1, k=20, k=100): ($7.99, $3.89, $3.66) Item: APS Compatible with ...\u001b[0m\n", - "\u001b[93m63: Truth: $364.41. Errors (k=1, k=20, k=100): ($65.41, $100.45, $88.50) Item: Wilwood Engineering ...\u001b[0m\n", - "\u001b[93m64: Truth: $127.03. Errors (k=1, k=20, k=100): ($13.97, $19.73, $26.43) Item: ACDelco Gold Starter...\u001b[0m\n", - "\u001b[93m65: Truth: $778.95. Errors (k=1, k=20, k=100): ($242.95, $210.19, $194.48) Item: UWS Matte Black Heav...\u001b[0m\n", - "\u001b[93m66: Truth: $206.66. Errors (k=1, k=20, k=100): ($43.34, $3.27, $1.90) Item: Dell Latitude E5440 ...\u001b[0m\n", - "\u001b[93m67: Truth: $35.94. Errors (k=1, k=20, k=100): ($10.06, $6.42, $19.62) Item: (Plug and Play) Spar...\u001b[0m\n", - "\u001b[93m68: Truth: $149.00. Errors (k=1, k=20, k=100): ($101.00, $13.55, $4.29) Item: The Ultimate Roadsid...\u001b[0m\n", - "\u001b[93m69: Truth: $251.98. Errors (k=1, k=20, k=100): ($42.98, $31.22, $27.12) Item: Brand New 18 x 8.5 R...\u001b[0m\n", - "\u001b[93m70: Truth: $160.00. Errors (k=1, k=20, k=100): ($90.00, $76.21, $65.07) Item: Headlight Headlamp L...\u001b[0m\n", - "\u001b[93m71: Truth: $39.99. Errors (k=1, k=20, k=100): ($4.99, $7.22, $1.65) Item: Lilo And Stitch Delu...\u001b[0m\n", - "\u001b[93m72: Truth: $362.41. Errors (k=1, k=20, k=100): ($112.41, $109.19, $107.49) Item: AC Compressor & A/C ...\u001b[0m\n", - "\u001b[93m73: Truth: $344.00. Errors (k=1, k=20, k=100): ($44.00, $27.91, $20.34) Item: House Of Troy Pinnac...\u001b[0m\n", - "\u001b[93m74: Truth: $25.09. Errors (k=1, k=20, k=100): ($25.91, $32.84, $44.71) Item: Juno T29 WH Floating...\u001b[0m\n", - "\u001b[93m75: Truth: $175.95. Errors (k=1, k=20, k=100): ($104.95, $102.92, $92.80) Item: Sherman GO-PARTS - f...\u001b[0m\n", - "\u001b[93m76: Truth: $132.64. Errors (k=1, k=20, k=100): ($167.36, $175.31, $170.27) Item: Roland RPU-3 Electro...\u001b[0m\n", - "\u001b[93m77: Truth: $422.99. Errors (k=1, k=20, k=100): ($122.99, $82.91, $70.93) Item: Rockland VMI14 12,00...\u001b[0m\n", - "\u001b[93m78: Truth: $146.48. Errors (k=1, k=20, k=100): ($0.52, $5.95, $11.84) Item: Max Advanced Brakes ...\u001b[0m\n", - "\u001b[93m79: Truth: $156.83. Errors (k=1, k=20, k=100): ($2.83, $6.12, $1.31) Item: Quality-Built 11030 ...\u001b[0m\n", - "\u001b[93m80: Truth: $251.99. Errors (k=1, k=20, k=100): ($101.99, $88.95, $98.62) Item: Lucida LG-510 Studen...\u001b[0m\n", - "\u001b[93m81: Truth: $940.33. Errors (k=1, k=20, k=100): ($799.33, $794.77, $789.80) Item: Longacre Aluminum Tu...\u001b[0m\n", - "\u001b[93m82: Truth: $52.99. Errors (k=1, k=20, k=100): ($8.01, $14.94, $26.77) Item: Motion Pro Adjustabl...\u001b[0m\n", - "\u001b[93m83: Truth: $219.95. Errors (k=1, k=20, k=100): ($30.05, $57.96, $65.86) Item: Glyph Thunderbolt 3 ...\u001b[0m\n", - "\u001b[93m84: Truth: $441.03. Errors (k=1, k=20, k=100): ($141.03, $138.31, $135.56) Item: TOYO Open Country MT...\u001b[0m\n", - "\u001b[93m85: Truth: $168.98. Errors (k=1, k=20, k=100): ($18.98, $28.33, $27.46) Item: Razer Seiren X USB S...\u001b[0m\n", - "\u001b[93m86: Truth: $2.49. Errors (k=1, k=20, k=100): ($1.51, $1.95, $2.65) Item: Happy Birthday to Da...\u001b[0m\n", - "\u001b[93m87: Truth: $98.62. Errors (k=1, k=20, k=100): ($1.38, $5.55, $1.24) Item: Little Tikes My Real...\u001b[0m\n", - "\u001b[93m88: Truth: $256.95. Errors (k=1, k=20, k=100): ($43.05, $24.48, $23.78) Item: Studio M Peace and H...\u001b[0m\n", - "\u001b[93m89: Truth: $30.99. Errors (k=1, k=20, k=100): ($10.99, $9.62, $6.62) Item: MyVolts 12V Power Su...\u001b[0m\n", - "\u001b[93m90: Truth: $569.84. Errors (k=1, k=20, k=100): ($69.84, $22.40, $24.21) Item: Dell Latitude 7212 R...\u001b[0m\n", - "\u001b[93m91: Truth: $177.99. Errors (k=1, k=20, k=100): ($16.99, $15.89, $19.06) Item: Covermates Contour F...\u001b[0m\n", - "\u001b[93m92: Truth: $997.99. Errors (k=1, k=20, k=100): ($0.01, $1.90, $3.08) Item: Westin Black HDX Gri...\u001b[0m\n", - "\u001b[93m93: Truth: $219.00. Errors (k=1, k=20, k=100): ($31.00, $27.85, $41.71) Item: Fieldpiece JL2 Job L...\u001b[0m\n", - "\u001b[93m94: Truth: $225.55. Errors (k=1, k=20, k=100): ($74.45, $63.37, $49.08) Item: hansgrohe Talis S Mo...\u001b[0m\n", - "\u001b[93m95: Truth: $495.95. Errors (k=1, k=20, k=100): ($503.05, $207.91, $189.62) Item: G-Technology G-SPEED...\u001b[0m\n", - "\u001b[93m96: Truth: $942.37. Errors (k=1, k=20, k=100): ($42.37, $108.81, $141.76) Item: DreamLine Shower Doo...\u001b[0m\n", - "\u001b[93m97: Truth: $1.94. Errors (k=1, k=20, k=100): ($69.06, $62.71, $71.10) Item: Sanctuary Square Bac...\u001b[0m\n", - "\u001b[93m98: Truth: $284.34. Errors (k=1, k=20, k=100): ($15.66, $0.19, $1.80) Item: Pelican Protector 17...\u001b[0m\n", - "\u001b[93m99: Truth: $171.90. Errors (k=1, k=20, k=100): ($30.90, $32.72, $31.78) Item: Brock Replacement Dr...\u001b[0m\n", - "\u001b[93m100: Truth: $144.99. Errors (k=1, k=20, k=100): ($24.01, $13.93, $32.85) Item: Carlinkit Ai Box Min...\u001b[0m\n", - "\u001b[93m101: Truth: $470.47. Errors (k=1, k=20, k=100): ($70.47, $23.99, $46.56) Item: StarDot YouTube Live...\u001b[0m\n", - "\u001b[93m102: Truth: $66.95. Errors (k=1, k=20, k=100): ($5.95, $4.51, $2.47) Item: Atomic Compatible ME...\u001b[0m\n", - "\u001b[93m103: Truth: $117.00. Errors (k=1, k=20, k=100): ($25.00, $10.61, $0.31) Item: Bandai Awakening of ...\u001b[0m\n", - "\u001b[93m104: Truth: $172.14. Errors (k=1, k=20, k=100): ($1.14, $9.89, $24.14) Item: Fit System 62135G Pa...\u001b[0m\n", - "\u001b[93m105: Truth: $392.74. Errors (k=1, k=20, k=100): ($8.74, $13.62, $7.93) Item: Black Horse Black Al...\u001b[0m\n", - "\u001b[93m106: Truth: $16.99. Errors (k=1, k=20, k=100): ($2.99, $1.77, $4.41) Item: Dearsun Twinkle Star...\u001b[0m\n", - "\u001b[93m107: Truth: $1.34. Errors (k=1, k=20, k=100): ($0.34, $0.91, $1.48) Item: Pokemon - Gallade Sp...\u001b[0m\n", - "\u001b[93m108: Truth: $349.98. Errors (k=1, k=20, k=100): ($99.98, $119.63, $121.66) Item: Ibanez GIO Series Cl...\u001b[0m\n", - "\u001b[93m109: Truth: $370.71. Errors (k=1, k=20, k=100): ($130.71, $84.50, $97.73) Item: Set 2 Heavy Duty 12 ...\u001b[0m\n", - "\u001b[93m110: Truth: $65.88. Errors (k=1, k=20, k=100): ($12.88, $15.71, $6.02) Item: Hairpin Table Legs 2...\u001b[0m\n", - "\u001b[93m111: Truth: $229.99. Errors (k=1, k=20, k=100): ($10.01, $37.54, $2.27) Item: Marada Racing Seat w...\u001b[0m\n", - "\u001b[93m112: Truth: $9.14. Errors (k=1, k=20, k=100): ($5.14, $2.90, $1.03) Item: Remington Industries...\u001b[0m\n", - "\u001b[93m113: Truth: $199.00. Errors (k=1, k=20, k=100): ($201.00, $310.61, $293.43) Item: Acer Ultrabook, Inte...\u001b[0m\n", - "\u001b[93m114: Truth: $109.99. Errors (k=1, k=20, k=100): ($140.01, $145.60, $127.75) Item: ICBEAMER 7 RGB LED H...\u001b[0m\n", - "\u001b[93m115: Truth: $570.42. Errors (k=1, k=20, k=100): ($194.42, $213.78, $222.40) Item: R1 Concepts Front Re...\u001b[0m\n", - "\u001b[93m116: Truth: $279.99. Errors (k=1, k=20, k=100): ($20.01, $18.13, $11.73) Item: Camplux 2.64 GPM Tan...\u001b[0m\n", - "\u001b[93m117: Truth: $30.99. Errors (k=1, k=20, k=100): ($6.01, $4.87, $10.38) Item: KNOKLOCK 10 Pack 3.7...\u001b[0m\n", - "\u001b[93m118: Truth: $31.99. Errors (k=1, k=20, k=100): ($13.01, $13.06, $20.99) Item: Valley Enterprises Y...\u001b[0m\n", - "\u001b[93m119: Truth: $15.90. Errors (k=1, k=20, k=100): ($13.10, $11.35, $27.35) Item: G9 LED Light 100W re...\u001b[0m\n", - "\u001b[93m120: Truth: $45.99. Errors (k=1, k=20, k=100): ($24.01, $41.82, $45.33) Item: ZCHAOZ 4 Lights Anti...\u001b[0m\n", - "\u001b[93m121: Truth: $113.52. Errors (k=1, k=20, k=100): ($136.48, $79.33, $60.98) Item: Honeywell Honeywell ...\u001b[0m\n", - "\u001b[93m122: Truth: $516.99. Errors (k=1, k=20, k=100): ($216.99, $179.92, $178.28) Item: Patriot Exhaust 1-7/...\u001b[0m\n", - "\u001b[93m123: Truth: $196.99. Errors (k=1, k=20, k=100): ($105.99, $102.06, $92.63) Item: Fitrite Autopart New...\u001b[0m\n", - "\u001b[93m124: Truth: $46.55. Errors (k=1, k=20, k=100): ($5.55, $6.70, $4.97) Item: Technical Precision ...\u001b[0m\n", - "\u001b[93m125: Truth: $356.99. Errors (k=1, k=20, k=100): ($63.99, $19.36, $20.80) Item: Covercraft Carhartt ...\u001b[0m\n", - "\u001b[93m126: Truth: $319.95. Errors (k=1, k=20, k=100): ($20.95, $18.09, $10.82) Item: Sennheiser SD Pro 2 ...\u001b[0m\n", - "\u001b[93m127: Truth: $96.06. Errors (k=1, k=20, k=100): ($4.94, $18.64, $21.38) Item: Hitachi Mass Air Flo...\u001b[0m\n", - "\u001b[93m128: Truth: $190.99. Errors (k=1, k=20, k=100): ($59.01, $0.13, $2.20) Item: AmScope LED Cordless...\u001b[0m\n", - "\u001b[93m129: Truth: $257.95. Errors (k=1, k=20, k=100): ($196.95, $194.13, $186.50) Item: Front Left Driver Si...\u001b[0m\n", - "\u001b[93m130: Truth: $62.95. Errors (k=1, k=20, k=100): ($51.05, $55.18, $52.94) Item: Premium Replica Hubc...\u001b[0m\n", - "\u001b[93m131: Truth: $47.66. Errors (k=1, k=20, k=100): ($15.34, $8.95, $23.66) Item: Excellerations Phoni...\u001b[0m\n", - "\u001b[93m132: Truth: $226.99. Errors (k=1, k=20, k=100): ($23.01, $72.83, $58.39) Item: RC4WD BigDog Dual Ax...\u001b[0m\n", - "\u001b[93m133: Truth: $359.95. Errors (k=1, k=20, k=100): ($109.95, $70.13, $79.04) Item: Unknown Stage 2 Clut...\u001b[0m\n", - "\u001b[93m134: Truth: $78.40. Errors (k=1, k=20, k=100): ($37.40, $12.13, $4.54) Item: Dodge Ram 1500 Mopar...\u001b[0m\n", - "\u001b[93m135: Truth: $172.77. Errors (k=1, k=20, k=100): ($18.77, $12.86, $8.96) Item: Pro Comp Alloys Seri...\u001b[0m\n", - "\u001b[93m136: Truth: $316.45. Errors (k=1, k=20, k=100): ($13.55, $8.57, $16.25) Item: Detroit Axle - Front...\u001b[0m\n", - "\u001b[93m137: Truth: $87.99. Errors (k=1, k=20, k=100): ($3.01, $4.59, $13.82) Item: ECCPP Rear Wheel Axl...\u001b[0m\n", - "\u001b[93m138: Truth: $226.63. Errors (k=1, k=20, k=100): ($23.37, $6.66, $2.56) Item: Dell Latitude E6520 ...\u001b[0m\n", - "\u001b[93m139: Truth: $31.49. Errors (k=1, k=20, k=100): ($10.49, $5.58, $2.45) Item: F FIERCE CYCLE 251pc...\u001b[0m\n", - "\u001b[93m140: Truth: $196.00. Errors (k=1, k=20, k=100): ($44.00, $1.28, $7.85) Item: Flash Furniture 4 Pk...\u001b[0m\n", - "\u001b[93m141: Truth: $78.40. Errors (k=1, k=20, k=100): ($2.60, $24.09, $27.28) Item: B&M 30287 Throttle V...\u001b[0m\n", - "\u001b[93m142: Truth: $116.25. Errors (k=1, k=20, k=100): ($24.75, $29.03, $30.67) Item: Gates TCK226 PowerGr...\u001b[0m\n", - "\u001b[93m143: Truth: $112.78. Errors (k=1, k=20, k=100): ($28.22, $26.80, $26.36) Item: Monroe Shocks & Stru...\u001b[0m\n", - "\u001b[93m144: Truth: $27.32. Errors (k=1, k=20, k=100): ($13.68, $26.25, $38.11) Item: Feit Electric 35W EQ...\u001b[0m\n", - "\u001b[93m145: Truth: $145.91. Errors (k=1, k=20, k=100): ($41.91, $36.59, $28.75) Item: Yellow Jacket 2806 C...\u001b[0m\n", - "\u001b[93m146: Truth: $171.09. Errors (k=1, k=20, k=100): ($30.09, $21.15, $9.71) Item: Garage-Pro Tailgate ...\u001b[0m\n", - "\u001b[93m147: Truth: $167.95. Errors (k=1, k=20, k=100): ($23.95, $30.33, $16.20) Item: 3M Perfect It Buffin...\u001b[0m\n", - "\u001b[93m148: Truth: $28.49. Errors (k=1, k=20, k=100): ($17.51, $14.91, $25.36) Item: Chinese Style Dollho...\u001b[0m\n", - "\u001b[93m149: Truth: $122.23. Errors (k=1, k=20, k=100): ($61.23, $56.11, $44.20) Item: Generic NRG Innovati...\u001b[0m\n", - "\u001b[93m150: Truth: $32.99. Errors (k=1, k=20, k=100): ($7.01, $8.25, $16.22) Item: Learning Resources C...\u001b[0m\n", - "\u001b[93m151: Truth: $71.20. Errors (k=1, k=20, k=100): ($29.80, $35.02, $37.36) Item: Bosch Automotive 154...\u001b[0m\n", - "\u001b[93m152: Truth: $112.75. Errors (k=1, k=20, k=100): ($51.75, $46.92, $37.04) Item: Case of 24-2 Inch Bl...\u001b[0m\n", - "\u001b[93m153: Truth: $142.43. Errors (k=1, k=20, k=100): ($39.43, $34.56, $33.71) Item: MOCA Engine Water Pu...\u001b[0m\n", - "\u001b[93m154: Truth: $398.99. Errors (k=1, k=20, k=100): ($99.99, $89.42, $84.44) Item: SAREMAS Foot Step Ba...\u001b[0m\n", - "\u001b[93m155: Truth: $449.00. Errors (k=1, k=20, k=100): ($151.00, $151.79, $140.86) Item: Gretsch G9210 Square...\u001b[0m\n", - "\u001b[93m156: Truth: $189.00. Errors (k=1, k=20, k=100): ($61.00, $2.60, $6.69) Item: NikoMaku Mirror Dash...\u001b[0m\n", - "\u001b[93m157: Truth: $120.91. Errors (k=1, k=20, k=100): ($9.09, $24.58, $20.46) Item: Fenix HP25R v2.0 USB...\u001b[0m\n", - "\u001b[93m158: Truth: $203.53. Errors (k=1, k=20, k=100): ($31.53, $33.44, $31.24) Item: R&L Racing Heavy Dut...\u001b[0m\n", - "\u001b[93m159: Truth: $349.99. Errors (k=1, k=20, k=100): ($99.99, $75.43, $83.50) Item: Garmin GPSMAP 64sx, ...\u001b[0m\n", - "\u001b[93m160: Truth: $34.35. Errors (k=1, k=20, k=100): ($23.35, $22.26, $17.86) Item: Brown 5-7/8 X 8-1/2 ...\u001b[0m\n", - "\u001b[93m161: Truth: $384.99. Errors (k=1, k=20, k=100): ($85.99, $79.46, $66.34) Item: GAOMON PD2200 Pen Di...\u001b[0m\n", - "\u001b[93m162: Truth: $211.00. Errors (k=1, k=20, k=100): ($25.00, $27.41, $21.76) Item: VXMOTOR for 97-03 Fo...\u001b[0m\n", - "\u001b[93m163: Truth: $129.00. Errors (k=1, k=20, k=100): ($121.00, $40.35, $34.05) Item: HP EliteBook 2540p I...\u001b[0m\n", - "\u001b[93m164: Truth: $111.45. Errors (k=1, k=20, k=100): ($87.45, $82.40, $70.87) Item: Green EPX Mixing Noz...\u001b[0m\n", - "\u001b[93m165: Truth: $81.12. Errors (k=1, k=20, k=100): ($50.12, $46.44, $38.33) Item: Box Partners 6 1/4 x...\u001b[0m\n", - "\u001b[93m166: Truth: $457.08. Errors (k=1, k=20, k=100): ($57.08, $81.73, $84.94) Item: Vixen Air 1/2 NPT Ai...\u001b[0m\n", - "\u001b[93m167: Truth: $49.49. Errors (k=1, k=20, k=100): ($40.51, $41.52, $43.42) Item: Smart Floor Lamp, Mu...\u001b[0m\n", - "\u001b[93m168: Truth: $80.56. Errors (k=1, k=20, k=100): ($49.56, $47.97, $35.94) Item: SOZG 324mm Wheelbase...\u001b[0m\n", - "\u001b[93m169: Truth: $278.39. Errors (k=1, k=20, k=100): ($10.61, $8.25, $8.67) Item: Mickey Thompson ET S...\u001b[0m\n", - "\u001b[93m170: Truth: $364.50. Errors (k=1, k=20, k=100): ($109.50, $96.25, $93.58) Item: Pirelli 106W XL RFT ...\u001b[0m\n", - "\u001b[93m171: Truth: $378.99. Errors (k=1, k=20, k=100): ($78.99, $93.39, $97.83) Item: Torklift C3212 Rear ...\u001b[0m\n", - "\u001b[93m172: Truth: $165.28. Errors (k=1, k=20, k=100): ($27.72, $17.15, $35.74) Item: Cardone Remanufactur...\u001b[0m\n", - "\u001b[93m173: Truth: $56.74. Errors (k=1, k=20, k=100): ($15.74, $3.36, $9.43) Item: Kidde AccessPoint 00...\u001b[0m\n", - "\u001b[93m174: Truth: $307.95. Errors (k=1, k=20, k=100): ($7.95, $3.05, $8.73) Item: 3M Protecta Self Ret...\u001b[0m\n", - "\u001b[93m175: Truth: $38.00. Errors (k=1, k=20, k=100): ($11.00, $18.24, $30.56) Item: Plantronics Wired He...\u001b[0m\n", - "\u001b[93m176: Truth: $53.00. Errors (k=1, k=20, k=100): ($47.00, $65.60, $56.80) Item: Logitech K750 Wirele...\u001b[0m\n", - "\u001b[93m177: Truth: $498.00. Errors (k=1, k=20, k=100): ($98.00, $26.16, $34.45) Item: Olympus PEN E-PL9 Bo...\u001b[0m\n", - "\u001b[93m178: Truth: $53.99. Errors (k=1, k=20, k=100): ($87.01, $89.49, $88.42) Item: Beck/Arnley Hub & Be...\u001b[0m\n", - "\u001b[93m179: Truth: $350.00. Errors (k=1, k=20, k=100): ($0.00, $4.69, $8.70) Item: Eibach Pro-Kit Perfo...\u001b[0m\n", - "\u001b[93m180: Truth: $299.95. Errors (k=1, k=20, k=100): ($100.05, $44.21, $55.26) Item: LEGO DC Batman 1989 ...\u001b[0m\n", - "\u001b[93m181: Truth: $94.93. Errors (k=1, k=20, k=100): ($13.93, $8.68, $4.13) Item: Kingston Brass Resto...\u001b[0m\n", - "\u001b[93m182: Truth: $379.00. Errors (k=1, k=20, k=100): ($80.00, $46.76, $31.08) Item: Polk Vanishing Serie...\u001b[0m\n", - "\u001b[93m183: Truth: $299.95. Errors (k=1, k=20, k=100): ($49.95, $23.89, $25.73) Item: Spec-D Tuning LED Pr...\u001b[0m\n", - "\u001b[93m184: Truth: $24.99. Errors (k=1, k=20, k=100): ($9.99, $8.24, $6.19) Item: RICHMOND & FINCH Air...\u001b[0m\n", - "\u001b[93m185: Truth: $41.04. Errors (k=1, k=20, k=100): ($72.96, $68.47, $71.88) Item: LFA Industries - mm ...\u001b[0m\n", - "\u001b[93m186: Truth: $327.90. Errors (k=1, k=20, k=100): ($87.90, $104.82, $120.04) Item: SAUTVS LED Headlight...\u001b[0m\n", - "\u001b[93m187: Truth: $10.99. Errors (k=1, k=20, k=100): ($11.01, $9.26, $16.89) Item: 2 Pack Combo Womens ...\u001b[0m\n", - "\u001b[93m188: Truth: $14.99. Errors (k=1, k=20, k=100): ($0.01, $0.01, $0.12) Item: Arepa - Venezuelan c...\u001b[0m\n", - "\u001b[93m189: Truth: $84.95. Errors (k=1, k=20, k=100): ($43.95, $41.84, $29.38) Item: Schlage Lock Company...\u001b[0m\n", - "\u001b[93m190: Truth: $111.00. Errors (k=1, k=20, k=100): ($10.00, $9.44, $6.47) Item: Techni Mobili White ...\u001b[0m\n", - "\u001b[93m191: Truth: $123.73. Errors (k=1, k=20, k=100): ($42.27, $44.12, $48.62) Item: Special Lite Product...\u001b[0m\n", - "\u001b[93m192: Truth: $557.38. Errors (k=1, k=20, k=100): ($58.38, $36.33, $42.06) Item: Tascam Digital Porta...\u001b[0m\n", - "\u001b[93m193: Truth: $95.55. Errors (k=1, k=20, k=100): ($3.55, $1.53, $10.52) Item: Glow Lighting Vista ...\u001b[0m\n", - "\u001b[93m194: Truth: $154.00. Errors (k=1, k=20, k=100): ($15.00, $2.99, $2.65) Item: Z3 Wind Deflector, S...\u001b[0m\n", - "\u001b[93m195: Truth: $198.99. Errors (k=1, k=20, k=100): ($101.01, $15.42, $2.67) Item: Olympus E-20 5MP Dig...\u001b[0m\n", - "\u001b[93m196: Truth: $430.44. Errors (k=1, k=20, k=100): ($180.44, $182.10, $197.87) Item: PHYNEDI 1 1000 World...\u001b[0m\n", - "\u001b[93m197: Truth: $45.67. Errors (k=1, k=20, k=100): ($27.67, $24.15, $16.33) Item: YANGHUAN Unstable Un...\u001b[0m\n", - "\u001b[93m198: Truth: $249.00. Errors (k=1, k=20, k=100): ($51.00, $35.81, $5.07) Item: Interlogix NetworX T...\u001b[0m\n", - "\u001b[93m199: Truth: $42.99. Errors (k=1, k=20, k=100): ($21.99, $17.87, $6.42) Item: Steering Damper,Univ...\u001b[0m\n", - "\u001b[93m200: Truth: $181.33. Errors (k=1, k=20, k=100): ($50.33, $46.08, $35.77) Item: Amprobe TIC 410A Hot...\u001b[0m\n", - "\u001b[93m201: Truth: $6.03. Errors (k=1, k=20, k=100): ($3.03, $0.78, $0.03) Item: MyCableMart 3.5mm Pl...\u001b[0m\n", - "\u001b[93m202: Truth: $29.99. Errors (k=1, k=20, k=100): ($15.01, $12.97, $16.20) Item: OtterBox + Pop Symme...\u001b[0m\n", - "\u001b[93m203: Truth: $899.00. Errors (k=1, k=20, k=100): ($100.00, $182.77, $192.07) Item: Dell XPS Desktop ( I...\u001b[0m\n", - "\u001b[93m204: Truth: $399.99. Errors (k=1, k=20, k=100): ($0.01, $174.15, $173.35) Item: Franklin Iron Works ...\u001b[0m\n", - "\u001b[93m205: Truth: $4.66. Errors (k=1, k=20, k=100): ($0.66, $6.52, $19.16) Item: Avery Legal Dividers...\u001b[0m\n", - "\u001b[93m206: Truth: $261.41. Errors (k=1, k=20, k=100): ($117.41, $93.60, $91.01) Item: Moen 8346 Commercial...\u001b[0m\n", - "\u001b[93m207: Truth: $136.97. Errors (k=1, k=20, k=100): ($4.03, $1.57, $3.64) Item: Carlisle Versa Trail...\u001b[0m\n", - "\u001b[93m208: Truth: $79.00. Errors (k=1, k=20, k=100): ($70.00, $95.33, $97.39) Item: SUNWAYFOTO 44mm Trip...\u001b[0m\n", - "\u001b[93m209: Truth: $444.99. Errors (k=1, k=20, k=100): ($144.99, $97.76, $112.55) Item: NanoBeam AC 4 Units ...\u001b[0m\n", - "\u001b[93m210: Truth: $411.94. Errors (k=1, k=20, k=100): ($88.06, $114.89, $110.64) Item: WULF 4 Front 2 Rear ...\u001b[0m\n", - "\u001b[93m211: Truth: $148.40. Errors (k=1, k=20, k=100): ($27.40, $28.75, $18.00) Item: Alera ALEVABFMC Vale...\u001b[0m\n", - "\u001b[93m212: Truth: $244.99. Errors (k=1, k=20, k=100): ($5.01, $78.49, $97.24) Item: YU-GI-OH! Ignition A...\u001b[0m\n", - "\u001b[93m213: Truth: $86.50. Errors (k=1, k=20, k=100): ($28.50, $51.46, $52.45) Item: 48 x 36 Extra-Large ...\u001b[0m\n", - "\u001b[93m214: Truth: $297.95. Errors (k=1, k=20, k=100): ($158.95, $159.79, $146.91) Item: Dell Latitude D620 R...\u001b[0m\n", - "\u001b[93m215: Truth: $399.99. Errors (k=1, k=20, k=100): ($0.99, $46.74, $48.73) Item: acer Aspire 5 Laptop...\u001b[0m\n", - "\u001b[93m216: Truth: $599.00. Errors (k=1, k=20, k=100): ($299.00, $317.34, $342.01) Item: Elk 30 by 6-Inch Viv...\u001b[0m\n", - "\u001b[93m217: Truth: $105.99. Errors (k=1, k=20, k=100): ($194.01, $42.37, $24.46) Item: Barbie Top Model Dol...\u001b[0m\n", - "\u001b[93m218: Truth: $689.00. Errors (k=1, k=20, k=100): ($189.00, $130.34, $134.86) Item: Danby Designer 20-In...\u001b[0m\n", - "\u001b[93m219: Truth: $404.99. Errors (k=1, k=20, k=100): ($95.01, $116.74, $109.73) Item: FixtureDisplays® Met...\u001b[0m\n", - "\u001b[93m220: Truth: $207.76. Errors (k=1, k=20, k=100): ($15.76, $17.54, $0.57) Item: ACDelco GM Original ...\u001b[0m\n", - "\u001b[93m221: Truth: $171.82. Errors (k=1, k=20, k=100): ($30.82, $15.15, $5.86) Item: EBC Premium Street B...\u001b[0m\n", - "\u001b[93m222: Truth: $293.24. Errors (k=1, k=20, k=100): ($6.76, $22.32, $15.65) Item: FXR Men's Boost FX J...\u001b[0m\n", - "\u001b[93m223: Truth: $374.95. Errors (k=1, k=20, k=100): ($25.05, $39.60, $60.36) Item: SuperATV Scratch Res...\u001b[0m\n", - "\u001b[93m224: Truth: $111.99. Errors (k=1, k=20, k=100): ($27.99, $12.01, $13.37) Item: SBU 3 Layer All Weat...\u001b[0m\n", - "\u001b[93m225: Truth: $42.99. Errors (k=1, k=20, k=100): ($6.99, $2.35, $8.75) Item: 2 Pack Outdoor Broch...\u001b[0m\n", - "\u001b[93m226: Truth: $116.71. Errors (k=1, k=20, k=100): ($24.29, $21.56, $19.41) Item: Monroe Shocks & Stru...\u001b[0m\n", - "\u001b[93m227: Truth: $118.61. Errors (k=1, k=20, k=100): ($25.39, $43.88, $48.05) Item: Elements of Design M...\u001b[0m\n", - "\u001b[93m228: Truth: $147.12. Errors (k=1, k=20, k=100): ($24.12, $20.59, $15.90) Item: GM Genuine Parts Air...\u001b[0m\n", - "\u001b[93m229: Truth: $119.99. Errors (k=1, k=20, k=100): ($10.01, $38.84, $28.18) Item: Baseus USB C Docking...\u001b[0m\n", - "\u001b[93m230: Truth: $369.98. Errors (k=1, k=20, k=100): ($69.98, $41.61, $25.95) Item: Whitehall™ Personali...\u001b[0m\n", - "\u001b[93m231: Truth: $315.55. Errors (k=1, k=20, k=100): ($65.55, $75.42, $88.26) Item: Pro Circuit Works Pi...\u001b[0m\n", - "\u001b[93m232: Truth: $190.99. Errors (k=1, k=20, k=100): ($109.01, $70.62, $75.89) Item: HYANKA 15 1200W Prof...\u001b[0m\n", - "\u001b[93m233: Truth: $155.00. Errors (k=1, k=20, k=100): ($144.00, $86.94, $82.73) Item: Bluetooth X6BT Card ...\u001b[0m\n", - "\u001b[93m234: Truth: $349.99. Errors (k=1, k=20, k=100): ($49.99, $19.31, $20.42) Item: AIRAID Cold Air Inta...\u001b[0m\n", - "\u001b[93m235: Truth: $249.99. Errors (k=1, k=20, k=100): ($0.01, $29.00, $35.02) Item: Bostingner Shower Fa...\u001b[0m\n", - "\u001b[93m236: Truth: $42.99. Errors (k=1, k=20, k=100): ($3.01, $2.81, $9.71) Item: PIT66 Front Bumper T...\u001b[0m\n", - "\u001b[93m237: Truth: $17.99. Errors (k=1, k=20, k=100): ($2.01, $2.03, $3.38) Item: Caseology Bumpy Comp...\u001b[0m\n", - "\u001b[93m238: Truth: $425.00. Errors (k=1, k=20, k=100): ($25.00, $20.35, $10.61) Item: Fleck 2510 Timer Mec...\u001b[0m\n", - "\u001b[93m239: Truth: $249.99. Errors (k=1, k=20, k=100): ($0.01, $2.44, $0.73) Item: Haloview MC7108 Wire...\u001b[0m\n", - "\u001b[93m240: Truth: $138.23. Errors (k=1, k=20, k=100): ($77.23, $78.48, $66.13) Item: Schmidt Spiele - Man...\u001b[0m\n", - "\u001b[93m241: Truth: $414.99. Errors (k=1, k=20, k=100): ($114.99, $97.73, $106.89) Item: Corsa 14333 Tip Kit ...\u001b[0m\n", - "\u001b[93m242: Truth: $168.28. Errors (k=1, k=20, k=100): ($11.28, $6.72, $3.01) Item: Hoshizaki FM116A Fan...\u001b[0m\n", - "\u001b[93m243: Truth: $199.99. Errors (k=1, k=20, k=100): ($99.01, $23.29, $22.49) Item: BAINUO Antler Chande...\u001b[0m\n", - "\u001b[93m244: Truth: $126.70. Errors (k=1, k=20, k=100): ($4.30, $1.45, $2.97) Item: DNA MOTORING Smoke L...\u001b[0m\n", - "\u001b[93m245: Truth: $5.91. Errors (k=1, k=20, k=100): ($1.91, $1.28, $4.17) Item: Wera Stainless 3840/...\u001b[0m\n", - "\u001b[93m246: Truth: $193.06. Errors (k=1, k=20, k=100): ($56.94, $68.25, $65.88) Item: Celestron - PowerSee...\u001b[0m\n", - "\u001b[93m247: Truth: $249.99. Errors (k=1, k=20, k=100): ($0.01, $5.61, $7.57) Item: NHOPEEW Android Car ...\u001b[0m\n", - "\u001b[93m248: Truth: $64.12. Errors (k=1, k=20, k=100): ($27.88, $42.30, $45.82) Item: Other Harmonica A)\n", - "F...\u001b[0m\n", - "\u001b[93m249: Truth: $114.99. Errors (k=1, k=20, k=100): ($145.01, $145.33, $137.70) Item: Harley Air Filter Ve...\u001b[0m\n", - "\u001b[93m250: Truth: $926.00. Errors (k=1, k=20, k=100): ($526.00, $557.60, $547.04) Item: Elite Screens Edge F...\u001b[0m\n", - "\n", - "--- Optimal k Analysis Report ---\n", - "Model: model-2025-10-23_23.41.24:v22\n", - "Inferences Run: 250\n", - "Analyzed k from 1 to 100\n", - "===================================\n", - "==> Best k: 99\n", - "==> Minimum Average Error: $51.44\n", - "===================================\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "--- Probability Spread Analysis ---\n", - "Lowest spread (std): 0.000323 (Inference 48)\n", - "Median spread (std): 0.004324 (Inference 59)\n", - "Highest spread (std): 0.102938 (Inference 91)\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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IN102LNM0NVV19exYVUEYKYoiuUGonryz5Hc+79gaKGW0rWdAjm0qY5nqyp3a1m09OT07WtZMzV0x4Pul+4/oh0emdXymLscyVGgVR9Tc+JwdKVIYRipkrDQR2L5MZHt1cN0N9Km7n9XIXEPbunNybDNNjjRb15svfuewillLA6WsHj42q3orMdtXcDRebuqeZ8bl+qF29uUURtKJmboqbpBeF/0wkhdGcv1QWceS5wUKIrV+4EcKQ8mLQo3MNVXI2urJ2+ovZDRZaerZ0Yos01BvwdH+oZKOTdc0XfPUm3f00t39i87Hq7kfXSlryw9DlRueBkuZ9HhL+vhHR2b0qbuf1baevLb35rS9N69q09fBiYqKWVv/5podeuW+gTRAcbpkbvJ+3TlH45WG6q6vph/K9UOZpuQFoarNuJgoa1syjHgJzlLOlmUaaniBDoxXtHtLUa9/8bAaXqijUzUdmaqlSVrXCxRIavihvDBSfzGjIIw0Wm4qiCIpjNSdi7f70ERNYRSpJ2crY8cJNz+MZBlxsdHoXEMXDZW0pZjRt54ZV7nhqdb0FbauP0EYqe7G+zCx8NobLvh7fBxIY+WmIkk525RjGcplLE1WXDW8QPVW0VkYSTIM1RWokLF0bLquZhCq4QbywzhRmrENuX6kUFLdC/WDwzPa2p3XllJWW7vjpU5t69Q5ZKrq6qGjM6q7vnoLjmpuvD+rbjDvWGpPBm/rzskwpN6Co7FyU8+OVVRpeJJhKMlXh9GphGckyYykKDJUdQM9PTKnC3rzaQJ14TG6kQmv9hkB0zUv3dZSzpGTs5WxfT03VtFn7jmg975u/7Lvu5aE03KzEKaqrh4+NnvaZV3PpaXLkn0xWXE11JXV2FxDNS9UwwuUcyxt68nJNg0dm6nLbx3/Z8pmze7YjMKbcynpe64614L2P042siB5Iz53pcfbC4T3bCnIMIwVizPDMNLzU1X9sFUwun+wpK29OTX9cMXXLFdU3V4YXXAsBVGk5yfjorPlVjJaKsFTzNgKglAPHJlJC1mv3t6rh0/M6JnRinKOpWt298oxzXlF2AuLvw9NVvSNJ8Y01/B08XBJV2zrkRtFi9q9VEHxUoWwyxXSr6bQXNKyRdpDXVnt3lJUd9ZR1Zv/+cdn6zo4XtFkxdWWroz2DpTmtWG5c3KyzPp0zZXnh5qqNnXv02PxsdFfkB+EuvuZMT0/UdNc3ZOpSHNuIMswtW+wpF+4dof2D3bp2HRNB8YrGi83NF1zVXBsXby1Ky0sXmkSwsLHt/fkF/X5wokZ331uQt94YkzPjpU1OltX1Q2UsU1d0FfQyy/s10t296m/lJlX5L3e2yqc7ru20vNWW/x8Ns7NyT5Y6ThZ7nVcNzrTubbvzomk32c+8xn90R/9kUZGRnTVVVfpT//0T3Xdddct+/y//uu/1u/+7u/q8OHDuuiii/SJT3xCb3zjG89ii5fGoHdlC4Pxk9Wmak1fkQwZrR//Pzg8JUl62Z6+0wbaT/cZ7dX7//TE6At637Vu5+3feFYPH52RacQ/lCNFmqi4uuuxUUmR+gqOBrqyMmRopubpn54c1Vilqd+68aI0KbaWY2lx37qqNn0ZijRpxMs3LezbEzM1/dOTo4sfW6Kv7j/UCqB053TR8OJq+rUEQNpnG2zryanhWXrg+SnN1D31FRxds6tPOcda9N7J+8YJvIaCIFLGNjQ2FwefChlLXhAnlDKWoUkjDsI9cnxWYRhpe09OQ915zdRcPXFyTk+cLKuUtdT048p6zw/iIJUkefGsv7AtApYEPg0zrjR/5NisPv4PTyrr2Hrk2IwsQ9rSlUn36T88NqJvPDWm3VsKGixldHiiohMzjThIKsm24uDSXN3X9w9NyzSkQsZWww9lKFLGNhUGkRpBpIOTVQVRpBv2btEzo2WNzDUUhPExdWiiqrrra6grq3LD04nZup4eKaf7sDef0QPPT2miEleK5x1TzSAO3AahIcc0dGy6pqYfzpv94gWBnhmtqNoKEkqSY8YXsZOzTf3tIyf03ERFXTknnZkwWW1qpu6pXPdkGIZ68rYmq66CIJIMQ5ZpaKgro7rr64kTDdmWqe6crXLD00TVW/Y7leyLStPTo8dn5frhaYPDW4oZPXxsVjU3Ttxm7XjWxshcQ48cm9FLdvXpoqGSnh2r6C+/d0R1L9R0zdXW7pxOzNQVtRKUz45VVcw6y86uaQ8yTVddOZah/mJGs/W4rRcOFHVkqqZjMzWZMtSdt2W1fuQ9cXJOv/e1JyQpTfylx/lUTePlhoIwVFcrmfXYzJzcIJBjmRooZVSvxzNtqq6vsXIz/TFxcjZQ3jHlWHHgeaCUOevXofVcD5c8d0Zx0r4ZhGp4oUbmGoqiSI5pyAvi4LFhhGr6gboMJ01ySDrjgUau+Yu1L4H57Fgc8MxnrHQpxf5iRq9/8fAZH/iuFHCtNIM4gRLF/92VM3RgrKK668eJ9SDUTC0+HyXniPZjqer6agahdvQV5t0HLpHPWDo0ERdX7N5SXDLgu7U7q38+MK7pqifbNJR1LJmmoSiSwjBUEEWyTcmPlB7P/QuWiUyqg8tNT3/z4HGNl5va2p1V1omXjszaRrq05Ewt/vPyvf16bjy+ZvQXM2nbillb4+WmQsX3h6vIV80LZCqSZRlq+nExhiHJMOLAsSEp7xjywkh+KGUdUwXH0Gwjvg/eUKkg0zTlh5EafqC9A0VV3UCPHpuVYxna1ZfXdM3T4cmqrt3dt2Rfr7Qv42CJpy3FjKZrnsoNX935UwGVmutrvNzUS3b2pufG7ryjqy7o1bNjFT16bFav3Ddwar+tkMyNoih9P9NQWrgjSd05WzU3np3vh/F1I4wideVs5W1L03VPDc9XEEov3t6jt157gfYOlPRn9xxQww/lWIaSrTJNQ5bi/jTCSIoiTVXjgqW8Y6rcCNRXzGi80lQQhrJMyQ0i2Vb8mY4Z7w/bMlVtxKsYPDdelWnE1/Ww9RmmIflRtGiMszDpt5QgDNX6+sgPQ+VtW00vaG1HXHAShPHxZbZmoY+Vm+or2JqaasqPJMc00qUXDSOQqfgaX254evz4jF518ZC8MJJjG6o0fJUbvrpydvo97S9mFEZxYrOQsecdSy/d1TsvGWzbccFRd9aRYwY6OVOXF8TfL8s0FLYSf+3bHo/TIuUsQ24Q6UdHp/WWwZ2LjlFJG5rwSmYE5J2cnjw5s+T31PXjVShWet+1JJyWmoUQRVHa14Nd8XgmiCL15RaPg86Vpcvax38XD5c0UHJ0bKauhheomImLICarTRUytgZLGXXnHX3jyVHtHzozY4TNmnF3tgtvzqWk77mK4rAzZy19u5H7Ya3LjV+6rUvfOTCRFgh7retod87Rjr78ksWZB8bK+rNvHtA/H5hUpekpaBUnF7K29g0Wtau/uORrliuqdv0wLQgvZCxNVz3NNXzJiJQxTfUUHL187xb94vW7Fr1fexFqUsQ010jaFI8pkkFEGEmRIlmmqS3FjC4cKC7avgNjZX38H57Udw9OpcWwkpRzTO0dLGpbT35Bu+cXFC9VCLtcIf1qCs3bi+AXFmkn4yTblHKOpWLWVnfeST9/rNzQTM1LV1AY6s7p1RcNpm1Y6pw8VXXjZeSrTY1Xmooi6UdHZ5ScrmvNQHU/UBieGps4ptRXzKiQsfXkyTl965kx7ewr6NBERcdnG2p68WDGNOLryyXDXXrTS3bo0Hh16UkIkfTUaOVUga1jqreQUV9ruxZNzMg7mq67eujIjOYavhpe/FvKajV6ouLqseNzyt7/vApZOy3yXq6wfC3FwKtdpnyliSWne82ZOjcnxczfenZcY3ONZY+T9W43zj3n4r7b9KTfV77yFd1666363Oc+p+uvv1633367brrpJj399NMaGhpa9Pzvfve7evvb367bbrtNP/uzP6svfelLetOb3qQHH3xQl19++SZsQYxB78ra+2f/YFEPPD+jphdqqDsnSZqsunpmpCLHlAzT1MhcUxf0FdbUf0vtgyiKNDLbVMYyJMNY1/uudTvvenREz4yWlbHjwU7SDkOSF8SBIUNSzrbiJRy74yTdM6Nlff2xEe35yeKajqWFffvD52fU9AINd2fTvn16pBwHZFp9sKM3r5Oz8RJgiqLWY4Ul+6qUteX7y1fTryUA8sxoWV/87mEVM7YuHo6f/+TJ+N43p4I2tUUBwD39cZ9MVprywzhJ1190dHK2KduKkzl1N07aFTOWBruymqp6OjY1pzAMlbVtNf1IpqSmFyljxRXc07V4ecuwtVxWGMTl3u2DrIWiUK2q/kj/8tyEtnbnlbEMbSll0+0d6jL03ERV5aan7d0ZnZgJNV6Jl48ypdagzlDGMuRGobwwSWrFy005ZrwPfMXV8GEUV10/PTqnajPQ1u6sKs1APzoyPe+zI0U6PtNQsTUzKl4KLJ6hkbMNTbuhoiiS1Uq2NVqBSy+I5Puhnh6Jl+X66UsH9X8fPplWQSeBXhmGcrahhhffr+nRY7PaUszophdv1YNHZtTwQpmKA2mGYWi2FieeQ0lGFMmS1PTCVrAwUilraLbuabq2fMJvfufHrx+da2iglFk2OLy1O6unRuKAVSljaabmpYFwzw9Vbvh6bryia3f3aWt3VvcdnNJgV0ZXXdCrcsPXTN1TbzGjjGXOC7IvDGDt6M2nx/vW7qwOT1bVlXfi+zjZpiYrTT34/KQmqp6MSLIsyTZN2Va8xFTN9TVVbeqL3zmkV180KNM0Wse5K7/142ZLKf4ez9ZcNfw4OGob0lTVi2fO5C3N1H01/Ejdufiodf1QxYyloa44GD4y19RLd/XqwHj1rFyH1nM9XO7c2fQCWZYhOzI0Vm4o71jaO1DQsZmGvCD+LkuGvCBSpelrW3dW0zVPNc/Xs6PlMxZo5Jq/vP1DXbr5lXvSAe/oXENZ20qXUjwbA96VAq5uEM8Glwy5QZhW2pZyjgwjXvKw2vTlBuGSQevVzKQwDVNBFKmwzGwKP4w0U/dkxKf69EdzEEYKonhGddi6R5sfRgqiaN4ykeWGL8OQsralSut8lrENZVr3ikskr5mueWr4oRp+NG9bTz0v/n/HNFRpBumUpySpmS59aMSP+a3gU1LM4QVhq722LCM+d1XdUFlbagahLNNQJClrmRovN7WjLy/TNOdtT3feWTJBsNy+dINQfhCqp+Boru619mus3PBVbvrK2HESbGGfLPc5yyVzk/czW0GhXCY+78b3wzOUcyy5QXx9bXiBenKOGl6oqy/olSQdGK/osu3d+o+vv0S2beroVE2PHp+VbRra3ptXpRGkBTamYcg2I0WRVHUD2WZ8vai7cYJwa09Ok1VXpiEZivveC1rHiySrlcxqBKGm656ma67yGUtBJb63nmXGiaz2GX7tCb9kzLFQEtMLWwk/U5IfxMeAF4TK2mZa0GIY8Qw+w2gFBE1D3fmMgqieHleGpFDxdpqmIaN17J+ca6rc8JWxTGXtuDBrqe9pshJDV86edyydnG3MSwbv8EPZphkfo61jOIxObVD7pibbnqzEYFmmrCjUbN3Xybm6dvQW5h07kjY04ZWcW8ZaFfsLv6deEMq2LG3rya/4vmtJOC11Pmvvaz+Mg7cZy1xyu86V+7guTD46lqVCxkq/I6FCVZuBtvfkddn2bjmWeUaTkZu11PXZLrw5V5K+5yqKw86ctfTtRu6HtS43fv/BSX3lh0fl+oFytqlSztJ4OYhnxnvhksWZkvSxv3tCPzw8rTCKi7frri8/lObqnp4ZrSyafS5pXrvai6oLGUuWEf9W8sJ4lQG/dSEsZCwVC5ZqTV//9MSoxspxEXryfu1FqBnb0ImZhhpeKBlSxpQsy1DVjcdfhqRixpQfxonBkzN1GYrmbd9PXTqkz957QA8dnVXYKnALWnGQuhfqqZNlTZRdyYhvzWGbcfFQUlA8WMqo3gzmFcLu6M0p59iLCulX6pOk0LzhBWkR/KVbu3R4oqoTs/V0JQDTiMfAXih5SdGgpJOzDTW8eLycteN+9IO4GOkfHhuZV8zffk7OO6aeHo2XnPeCII7BtIq8HMtUwwvUDBYPwrxQGi+72tVvqTtn69BkTc+NV2SaZlqIlozR6m6gR4/P6YmTcypm45mZ6SSEcmsSgiH15R0NdmVV93ydmGnoROsakXcs+UG4aGLGbMNTMWPLMiTXDyTDkGHGqz5Umr6aClR14/HnBT15Nf1gycLytRQDr+Z7e7qJJUsVP5+Nc3NSzPzD56dVbXitAv/lj5O1bjfOPefqvlv6ZkJn0Z/8yZ/o3e9+t26++WZddtll+tznPqdCoaAvfOELSz7/v/23/6Y3vOEN+o//8T/qRS96kT72sY/pmmuu0ac//emz3PL51jLoPR+190+lGd8vo9SqoDYMQ1nL1EzdUzZjq6stCCStvv+W2gflhq+pmquuvLPu913rdj56fFZBGKmr7ce664eqtZaiNCXV3DC990qcQIsrYh45NqsHj06v6Vha2LftgQLDMJS1477NOVbaB3FQxFVXzlFX3okfm6sv2Vflhq/p+vxq+uXac7rvQVfO1uGJqrpb+749sLAwANj+3kmfdLWCl6WcI691P6GMbcq2TDX9UBnLVN0L5QWRDDMOfOQcW1nHVN0LVGkGqnuBHDte3iII44Fc0Aq0SfHfl/spnASCIqmVwIpUbfrqyi8MzMRLnVmGoROzTR2ZqskLwjRgGgeW4gGd2fa6KIrfP0kUh1HUCuzF1fVj5Tjxk7EtZS1Ts3VPGcdKPzuKpGZrKciuvKPRuabG5prKWqYafqSsY6nhh/Fx2Kq0r3txsG6s0oyXFgsjHZyopTNdpKTNcVAxkhEvHeeHqnuBmkGokXJDUzU3/lHixUu/WYahhh/Isqx4VkAUybYtVZqBas24jTU3VN3z0wDiSiGI9n+bqcUJr4Xf3ST4FIRKj6tQcV9bhpEG1nOOmR5nfhhptu6qvxAnmdwgXgbVsebfiyk57vMZS00/nl3XfrzHs13i1yXHe8axNFX15AehHDvewmTxUsNQ2k/JMZ68X3fO1nTdS8+R8fkjbCVfDRlm/KPAMgyF0ankQLkRqOqGcYCn9T1I2l9pBmftOrSe6+Fy586gNRslY8c/QPwwTiY0vFYCtNXftmWo7gbpNsfJW/eMBRq55q9s/1CX/sNr9um3f/pivfd1F+m3f/pi/fqr9521gW57wHWhOHhtpP+dJI+c1jKCXhDOC3K3f+elUzMpTs7Gs07bJTMp9g+V1Jd3lvx8KR6bmDKUc+K2JEGEKEmCtLIwyXk3uU44lik/jGe2Jp9TytoKojgB2H5/vYRjmQpaiTfXD+Zt66l2x9+lbOvcHrcnOrXkYWszrVaiSYq7sP16GUZSGIXpdvitGYtBq6rVagUHgnDx9iQJu4V9vdK+zFhmKxkWzNtfUpwQdP0wLsCwFv/UWcvntL+fbRrxbLl0v7T6pXV/PNuKi2K8MJQXxBXoo+Wmdm8p6t9euzO9h2vV9VX14s/pyjna3pvTri0F9RXiYEtfISPHbN2D1gsVhvH+29OqmHdMU4VM696KreVok33YlbVbuycerPhBqLBVjZ+xTRUytkpZq3VPv0WbuuL4J1rwdyleKjRMxy7x52ZtU0PdWe3oy2tXf169eSc+V0dKA1NJG43WQZa8fxDG29OVs9WVteX6oRzTmPc9TZbFciwjTZokx1KttTx6PmPFQUrLVH8hXmrKbBsHhGE0bzsW/sXQqTFZUli28Ng5lfBaOnGz1HG2kvZzy8LvaRRFqjTimX+DXdkV33elY1man3Ba6nyW9LVtKv3MrtypbWzfrrV81pm0cF+4QTzWvaAvp519eV3QV1B/MaNLtnapv5hd875Zq9VeJ87EUtdJ4c3l23s0U/N0eKKqmZqnK3b0bHjAaaO/Az9OFhaHdeUcWWZcNHvRUElTVbd1/9/VzLFGu7X07Ubuh+Xeq325cT8IVcra6eNeEGiq2lTTCzTYlVO1GSdoevKOHMtI4zL7B4uaqrr6+mMj+odHTuix43GSpDdvKQglwzCVz1iyzHjJwqdHKto3UGi9ZlR3PTqStquUtXVoIl6yfmdvTrM1T1M1V0NdGRmKlxCPovi9wyhSrRlqsCurjGXomZGy/r9HR3TXoyOLilAbreXnpbgANVJ8P+NEJKnmxcvT5+34d/R45dT2TVZcfeFfDuqx47OKong2fTIiaN1ZRUEUF6xf0JPTXN3TVDWeiWaZcZFZvEJToIYfJ9xs02gVmZka7s4qY5t6ZrSsux49qbseW7pPdvXl5QeRDk1UdXK2oUxrqfKnTs5prDXzLp28uOCwaPqhKo34dh3J8RXH2+JZgLZpyA3ipcG//tiIwjBKz8kv3tatp0Yqmqw0lbVb42LFSd2evKOmHy6Z8FPSFsVL7881PEVRKD+Mi5mluGjPNk/FqYIwXiGn0vA01JVRzrFaEw7iSQh+0FpZyjJUa8bJ00zreJyquhrqzmm4O6uG6+uZkUq8YkRrbFBu+nEcyTLSAjW1+iqKIrleIMc2taWUlR9E8v1AT4+U9cxoWfsHi6v6Dq72e+v74bzJDyOzzXRiyXB3Vk0v0MhcM/1+LXzNmTo3J8XMT4+U5bZmyBazTjpbdKnjZC3bzXXj3HMu77tNTfq5rqsHHnhAN954Y/qYaZq68cYbdd999y35mvvuu2/e8yXppptuWvb5zWZTc3Nz8/6cCQx6V9beP+1B9UR7IGhhEEhaXf8ttQ/aP2u977vW7UwCOe0/1pPAVxLpCKIoDfLFz437oub5mqy6azqWFvXtgkCBYRjx8gttfZsERRwrfiwIQ9XdYMm+Svown7EW9d/C9pzue5DMCEgqTBe2N2nLwgBg0ieWaabPD1pJsWTAFEXxgDFq9W088FB6T7koioOQSTV80vth67lpjy1zHl4YGIpaL4halVntkoCpaZwKFp7aIcnrlFa4L/UZUdtzjFbiLwnceUG4KHgqKb13U6ZtHybPje+vl7xf/EmWcSph5AWhglYLyq7fOmZag92kIj5qLdlhGK0lN6M0GOaH8ayYdJ8Ycb8qiuJUV5RUoMX7J9mHQduF73SXwKQv4uXTtGzQdq71o8ux4gG1aRjpdy5JhCfHcrnhS5HSSuyMZaazAqTFQen2AFb78b7wdZLSz42/6lEaEE3E/RT3/WTVTd/PNI1558hT54u2vmr1RdSKlhqG0u+EYxrp96C9/WfrOrSe6+Fy585k/yXJELWSCe3HQpJMD9u22fVDmYZ5xgKNnXjNP1vjoYRpGtrZX9ClW7u1s3/l+xZstJUCrqVsnGyPAzJWmjzygmheYD0Jci8MWiczKfqLGT07VomDPK1gz7NjFfUXM3rLS3do/1DXsgHf6ZqrLaWMBrtyMiS5reWNkiRIGMX3U8vapkpZO66g9YP4HB/FSedkxkZXzonvaZm1VWn4iz7PbS1PtKuvoIYXJ668IJrXnqYfqrfgtIpoDAVRmLYhik7d281uv9Yl1ybDSBMjyfXIkCHbNONzYBgvV52xTUXhqe+q1Jq51JawWypBsNy+7MrZ6is4mqy66is485ISjhkXS5Ry9rzHE2v5nPb368o5yttmmohNLl/Jeae/kFE+Y8lt3Ues7gZLBtqLGVtFx271QXxdyjmWhruz6XJWWdtUX8FRb8FRvpWcuWJHj7K2pZxjKp9xZJumihlL23vz6i9m5Fin+ry/mFFfwWkFxOJCm2RGom2a6Yz81q5s7belr8PJKbm9vsEw4mBbMWOnfZGMFwzDUN6xVMjYMlszFbcUMzLN9NBJ3yMtKIriH6U559TxUMjaGuzKamSuIbd1baw2fU1VXRUylrrzjrzW+pzJsVRwrHnJ4Kxtad9QUfmMpUozvgdsUkzl+uG8H8LJcW62GheEUdr+fMZadOxsdMIrObdsKWVVc5PZn5GafhBXsGcs7RssquGt/L5rSTgtdT5LgoYT5Wb6me3FLadLGi73WWfSwn2RnNfjpYet+N6TjqWsvXg/ngmruU6cyaWuz1bhzbmS9F2LszUWojjszFlL327kfljLcuPJ4+NlNy42MQxVXD8tWE6KbyNJo3PNtDjzkWOz+pcDk2r68XU7jOLfhMkMeceKC5enq65G5prxa47P6NHjs2m75s/Wbt0mI5Qqblx4m1z3wigpAI6LJrvyjoIo0g8OT+nR47PzilBdP1S5EZwqhol7MF01oL0AzJBa1854ZYjj0/XWcvq2njpZlhvEcQDDPFVAFRflxO/hh5Emqm5aCFR1A2WdeBWdajNQpXmqEDa53rt+OK+Q/vuHppftk6TQPCmO7srFyZjJWnwrFEWnxj3t4yO1+rHuxSs7RJFk26eWdk8KesMwjsk8cmw2Pa72D3Xp567erp39ed2wd4uuvKBXTus4yLaKp9pjIu1Havt/e6E0Uz9VmB2preiqNR6XlO4XP4jiFTwUj3mqzUBmK3ZWc8NWQXw85nRsS34YzzxM+jPjWJqpx8XWdmsmYjLeMltxDL81VoqiZKUSqdFa9aGUszVWduX6cWF50pZ021YoBl7N97Z9ssRSE0uWKn5e6wSL9UiKmd3W7YOyjpWOoVc6TrhudK5zed9tatJvYmJCQRBoeHh43uPDw8MaGRlZ8jUjIyNrev5tt92mnp6e9M/OnTs3pvELdOKg92xq75+lguPtgaCFQSBpdf231D5o/6z1vu9at7M9kJNIAozJyKF91lH83LgvCo6tLcXMmo6lRX3bCl4moiiKl9Zq69skKOIFUTqrIZ+xluyrpA/rbrCo/xa253TfgyCMA2NJhcPC9i6cYZG8d9InQRimz29P5kRKEgDxSTVJpiRJgfiWcqeCkO2V5smAJe0xQ/NHV20PS6eScUZr0JtxFs+uiPdtPOjJWHGw89QOab2fcSrgtfAz0ma0gr/JQCvnnKpYDxcET5NlxXKOJdMw0n3oWHGgNQ4EthJFrYxl0ApcRlGc3LJaLejK2K1jRmkVfhrgbc1KMdK+M9JjJwn6xomuVgW/YaTLK0RKBvVGug/bl1E7XegjSUImsyWXC9pO19w0sJ2xTeUdS64fym3NgkyOBcc0NF1z1Vtw0nZ05ey0j6MF56OFAaz5yzfNf52ktgSoFATxfZbatzdJCDqtgGjyfmEYzTtHnjpfGIv6wpCRJmST74TXSvJbbcdBpvWj6Gxch9ZzPVzu3JnsP88PW4NlI10i1WrNhLFNo3VPiXibXT9OtJ/JQGMnXvPP1njoXLBSwPXAeFUXb+3SxcNdOjBelRSpJ29ruupqsjI/yL1c0Pp0MykuHu5eMeC7ozevl+/domLWVn8xoyCSqk1fUWt2XdOPAxrD3Tm9fO8WDXflVHcDjbWWSr1uT3+aSNrRm9f+oS4VsnY6i7npBwqjSA3P18hcQ4NdWf37V+/VBX15hYqDRUFrxmCSQLnqgh5Zpqm+Yia+v4niAIHnxzN+HCu+poVh2FYkE8Uzgqw4cOT6gYIoXu7JMeMlq4sZO74ORZGaQajevCPXCxSG4bwE63J9vdy+rDR92aaprlw8i6zS9NM+TrZ5qaT8Wj+n/f16i476Cpn4WuLEKwyEreBI3jElw9BFgyXtGSzqpy8b1n/5mRctGWhPEniWaajc8NJrRj5ja7g7F68GoDh41VuIrw1X7exRfzEbJzuL2db+iAMtBcfSQCkrQ1G6ssPFw6V4DKH4urujNxcndVuz5kyptSJB2/dmmaxfGMWJz56crYyVFFLFxQ39RSfti3h2mCE7CWa2JdH3DcRL4xpS22zSZBwTt9O24h/FUqRnxyra1V/Q+153ka7Y0Ss/iL8bMzVPg11ZvezCfm3rzrfGQ6eOpW09uUXJ4P5iVlfv7NVQKRsvF926DmdtU3ln/sjDNpQGKtUqNuktONrWnV907JyJhNf+oS6957X7tG+wpJmap+lqU41W1frVO3vVV8ic9n3XmnBaeD6LZyFYytjxeaG/mF12uzY7uZVYuC/iooCMKg1v1eeajXY2Z9wt5WwU3pwrSd+1OFtjoU4sDusUa+nbjdwPy71XUsSczDBPCkXdVkwl/v0Sj5eS34bSqeuv11acWfP81rggvu4mRY/tCQMpLnKsecnKOYGqnj9vpnN7kbQUrzXjB61rb5rQitLC6KR4SYpvl1D1/HlFqEkBahJDWfB1n/cbPjkXWK0f/0kRuWUaaqZJNSMtbl7qTeL7WUdpkbHVSgouLIQ1dKr90qlC+orrqeYu3SfJ85Li6OQWOEEYnWpPW1siLdi+1v9EkkwZaVF0us2Kiwhrnj/vuKq1Znzt6Cso61jp713TSFb6OLVNS0keT4qfk78bK5zao0hpcVRSHBzHc+I+Swrik2KopA1JfyaFZHZrpmUad0oHi3FHRK3jal5MrdXPXuszJC2aPCAtXwy8mu9t+2SJpSaWLFX8vNYJFuuRFDOHrYPFWrCTljtOuG50rnN535070agz5IMf/KBuvfXW9O9zc3NnZHB3tm+c3Wna+2f/YFH9hYzGyg1livFJOQkENV1frm9quDuXVmevtv+W2gdJIH50ri4Zxrred63becWOHh2arKrc8JRp3ZcqXlLJUrkRr0deyJxKBCXLFFmmoSsv6NE1O/v0g0PTqz6WFvZtXyGj8XJDmWIm7ls/7tuGF88SGO7OaVtPTsdnGhorN6Qo0nBPXtu68zox3Viyr/ryjg5OVrV3oDivan6p9qz0PSg3fO0ZKGqu4Wtr24/y8XJDTsFRpeFrqPW57e+d9Mmjx2fUV3A0Xm6qr+Ao71iqunGSJWvHy7T1ZePlMqIwDsw3PF9RZKuUjZe0KjcslRtx9ZgVx+hkGUrv+2MYcRJ6YewrGfAFrWp0w5QGu7IqZmyV654yJTPdXscy4vv++ZG292Qlw9R0zVXTC1uzzU7dK8kPwtYgNv4MP5RMI2oFV+PPdMz4x/twd077Bot66OiMxsuuSllLTTdQwzJUbVXQ9RUzrdlrkYZb98wcKzfje/rVfOXseOCeBCqLmfieOUOlrCaqrgxD2jtQ0KPH4+VBpfmzqQxF8vx4ucq8Ey8zurUrpxOFhkbLDeWdOPhqGEacqA2CNFno+4FKOUeR4sBdb96WYdhqeJ4WDwHnax9w9xYcBWG0bND2+EwtXsK26mqwK6NSLp79J8X3Gqg0fPUWHY3MNeYFLLpalWH7hooqN73WPSSlrW1ByPYAVvt376KhUvq6qaqrYmvfdOdtTVbjm53nbCMdmEdRvBRrEMXbcc3OPpmmoX2DJT16fFZ9eUfjlaYyRbN1/jBVcePvURSGyjlW/AOtlTj2w0hdubiCfabuq68Qfw+ma56GunMqZS0dGK+elevQeq6Hy507DcNQX9HRTK0ptRKoeSdeYrbmBml1Y8MLVGqdN0bKTW3tzuktL91xxgKNnXjNP1vjoXPF6e4tKJ26oXzWtuJ7eJmmLhoqqTvvqNzwVrwH0v6hLu19TSm9r1Uy62VhIH2lz7/jO4clxcnt0bmGqs14lrVtmRooZnTlBT3a1pvXlmJGBycM7R8q6d9dt0uv3DeQfk77PZwkqdaM7z/n+q5cP9K27pze+7qL9LoXDevCgaJytqV7nh7Tsem6iq2ZVNt7cqp7oa5qJUaen6rqkWOz8X3xrHjG2XTNVbVVoVvImHK9UHUvkmUZKrWWjJyuecracSHIRNXTtt6cLt3apQNjFR2Zrqu3kFn0991bCqo0/dP29VJ9ecO+Lbpka5eeOlme9/iVF/Tqrdd26ZtPja3pvlbLfU77+x2ZqsluzSaWpLmGHy8HrbjC2bZNXdBX0C+/fLd2bykueWyapqE3XLFVT42W9fDRGY3ONdVTcCRFmqvHS5hfNNylf3fdTuUdS//foyOarLrK2JbyGUvberI6Nl2Lk8aFjGpeINcPZFuW+gq2LhwoqNzw5fqRXrV/QKPlpmbrnhqt6vSmF6qpUxXi8bJZcUV4IMlsjUdOzfCXsk5cwJN1LIVeICOKZ+U1g1CFrK2Zuq8gDNVfcJTL2Bqda0iKq6y3dmd1cLKuq3f16okTc5queXK9ME4yGqeWNe8tOMo5tmbr/rx7gL72kiEdn6nryZE5/f3DJ9Pl3PcMFDRZbc47lqqtArX2ZHA+Y8mxTPUUMnrFvgHt7Mvr7ifHNFuPZ7VlXF9zrWIqGWoFSuOxT8E2dcWOHlXdpY/RM3H/tIuHu/W7P3uZPnPPAU1Wm9rWk9dgV1YNL1h1Im2t91ZdeD4bLzcXHXfLbde5cB/Xpe5lt2egoKnk+Mg72rOKc81GO911otOd7XsIboSzNRbarPs6ng/W2rcbtR+W+9zllhtPVt6JkuLjZDZ+FMk2Ts2Sc9qKMwuOra5cKMOoywtPFY8mib+odXG2jLjgp+4GKmQsKVLarvai6iTZECn+/CQRp7SQ91TBdFxsGi9LmnfmF6EmBahJfGRhomle7s44VVwsQ2kRecX3lbVM1Yz492+84sDSb5K1zVYBXFzImhYKxUEFeTLSlyTtl04V0pcyTishurhPsvapolhDSlf6sMy2xF9bWxbGhFo1qDKi+N7E8cy5tm1W/Pu14NiLClzbC/WT5FQYJSt9xDt4qSRjexssw5AfLViCf5lTrGHEx1fyuqTYOzmGkoL45H7Gyb5Ll0Jv9YtlmvHtYrx4jBUEkUzbSDsiLjCPV6WKC9WtdH84Zpx4k7Ro8oC0fDHwar637ZMl2id7JLP6lyp+bn/NmTo3J8XMZutgSb7zieWOE64bnetc3neberQMDAzIsiyNjo7Oe3x0dFRbt25d8jVbt25d0/Oz2ayy2eyS/7aROnHQeza198+B8aq29mQ123DnBQUuHCjo6ZGKFIba2p1VEEWqr+HH2XL7YGtPVkena5Kidb3vWrdzuUBOvAykpaQ2puEHkgzN1jyFUaSrdvbqpsu3yrbNNR1LC/t2W09Wcw2vlbCJ72+1d6Cop0bKaR+EUho0ktR6LFq6r9xAtm2qOxcvJZUEUJZrz0pt31LK6q2XDs0LxK3mR3l7n1SbcZXUVNVT1jE0W4+XtCxk4oqpMIqTXKWcrcu2d+uR47Nq+oG2FOP7u2UdQ5PVeF+UslZrSUxDYWswksxos1qzPPy2UV4SIHMsU1u7c/p/XnWhvndoqrWvG619He/TjGWplHXkOLYGSxmdmMno+EwjThq2qn7cIFIYxknhHb15TVRclZteHEg14htDx3VuhnpyjvYMFJWxLfUVs7Ja9/Q5PlPXeNnVtt6cLhnuUsML9IPD05KkbT05ZR1LU1VXDT+u4Ms7pppBtGSg8pKtcWDm4GRdl27r0lzDb80+ac0kiyI1vLiDillbL97era6co+cmTn2n4xuNxxVjPQVbM3VPZqtTAxnx0mWtJeYafqSevCPTkCaqp5aqWJYRL4cw3J3TQFdu2aDtr/7EhYsC2xcOFOQFkWZqnmzLUF8hoysv6J0XfE+Oye58vPb2Y8fnFLYSyguDkAu/e8lrr9jRo6dHyjo525BtGbpwoKS809DIXEOVZqBCFCfpGl4gP4w01JXTu155YXqvp/Q4d+NCgMlKU1nHkmmYytmW3CCQH0kDJUdzdV8z9aCVCDPTmsdMa/m5sbKbBlwPjFfP2nVoPdfD1QTBB1szNk/MNuVYZjqganrJvQQijVfcNMlx8XD3ObWNm+1sjYfOJacLuC4Mcj98dEYHx6s6PFFdVdA6mUmx3s9PAuUHxsoa6srJDUJt78nppXv6NFlx57Xl+gu3LNuW9qD7gbFyfN41pH1DJf3CNTt1cevcvn+oSx/62cv02hcN6e4nR3VytpHOIk62de9AKU2w/NPjI3r8+JxmG/HsMds8dQ/NrB2p4ceJG6v1o/YlO/v0M1dtU38xox8emtJ4uSk3CLWzv6AhL1TWiV/f/ve5uqemF562r1fqyyQptPDx3VsKa05ErPQ5yfv96Oh0fK/eVkDOtgwVsrZ29hd0za6+VSU69g916bduvEhfuv+I7j80pclKXGTTm3d0/d4t+sXrd6Xvsa0nt2g7Xn/ZsCLFszbb9/ebr75AxZw9r+0HJyrz2i0FKuZsdeccZaw46DNZczVddeUG8T30kkS43xoHun4oX/G9ii4eLqk3n9GJ2bomK64kabgrq96Co75iRq4faqLSlGRoSzEz7/h6frKmz9zzrJ4drcQBPcPQlpKtV+4b0JtfekFaTNX+PUm+Zzv7C9o7UEz7oukHSx5LyyeDT+37n7tqh776wFE9N15RGEl119fR6ZqafhxIs8x4Och45qHSGVoL9+2ZSnhdvLVL733d/vR9n59c3Tlp4TG2loRT+/ns0q1LH3erTRpuRnJr4b5Ij4/WcrlJ4vtsJiOl018nOt25kPRdi7M1FurE4rBOsda+3aj9sNznJsuNH5yYXyDdlbM12JXRaLkhM5JKGVtlJ57VY9px8ZAhabg7mxZnXnlBj4Iw1HPjVdXdQN25UysxGZYhLwgUyVBfMaOt3Vk9N1HTlTt6FUWRHj85p1LWnldU3ZuPZ9gbplTKWCpkLVVcX5biIuOGH7VWZIjv52aZpl62p1+GpMdOzKVFqPFM6bjYOoziWfFqzRRMCpelU0t/hmEUr8ZiGdrRl1cpa+nkbEOXbuvSA89PywsimTo1kyyeeRa/h20aGmgVMVumkRZ5G4ahYtZSGIaq+0EcpwlClXJOurpA8prrLuyTDEOPn1jcJ0mh+XB3fB4YKzcVRZG2FDIar7pyo3DRsqXp9plS3o5jKYYRyfcjFbJmukJS0wvSJTuvvGBxgWt7of5QV1YzNVdNP1TOjt8jKUJfmOhL/t824kJBPwgVqJV4bIstJSuEWkb8uG3FtzKQ4tcVs5YqzVOTEOKCeFNVN16BY+FqDa4XxCt0+GHrvWw1vfhWQa4XxAW5ildrsBSvUtGbs5VtW+1hqCujiaonw1DaltN9B1f7HW+fLLHUxJJkQkF78fNaJ1isR1LMfHCiKtPw1fQCWRk7Tdwvd5xw3ehc5/K+M6KFazGcZddff72uu+46/emf/qmkeNmgXbt26ZZbbtHv/M7vLHr+2972NtVqNX3ta19LH3vFK16hK6+8Up/73OdO+3lzc3Pq6enR7Oysurs3PiB4YKw878dG1ra0f6h0Tg56N0N7/0xUmmlQYKCU0UApDhgoiteqXm//LbUPNuJ917qdSSBnth4HRHrzji7Z2i0p0lMjZc221uPuyWd0w95+vb0twLPcdqzU5sV968pQfOPl5fq2r3Bq1tXp+qo9gHK69pyu7Qv/vemHarZ+lMd/ln7v5HU/Ojqto1M11dwgvbFz1rZUyJiqueG87Zak5yerGis3W0s4mBrqzmp3X0FzTV8HxiqarrqtpZziAZZtmYpzMPEMqoYXz76wLFOljKUXbevRu165R6970XC8r793RPcfnFy0T1+xfyDts4lKU4cmqhqvNOW17r1nGlJ3ztFrLxnUf3jtfvl+pP/2zWf16LEZNVqV7IWMpZ5CvKRYe9/c+KJh5TOWnhyZmxdcXWq/Jv3rt+4dN1f30oF4T96ZF6iUTs1+OTJV1XPj1XT2SaR4ID7cndPrLxvW26/fNe/5yXe66YfyWze3Tu6pmFQ5BWGkQsZSfzEj2zTT+xc9M1qObxgenlowImG0+qor5+hF27r0qosGT/vdDcNI33luYl5gO2ubGuzO6to9/XrR1u55Aamljtl9gyVdubNnySDkSsd7xpr/OXU30Oe+dUD//OyEKk1fURQPvC8eLuk/vGa/Xvei4SXfLwnQJlWc/a1g6kzdS9fUlwx1Zy31FrOtpSaN1vcgLipIzq2bcR1az/VwuXPn9Xu36N+9bJdGy410n9abgSaqrqQo/UG3MMlxLm7jem30+OVMj4c6URhGZz1ovdxnrqcta3nNap4bhpGOTtd0aKIqSdrTX1BkSM9PxgVDu3rzGqs203vZXLOzLy1gWPj+27pzOjnXWPbvZ6qvN3qfJu9XbniqNH0VspZqzaB1DnLW/P4L+3jvQFEX9C1eim+p7ZC05v2dtLuUs9WVddL9kG5Pa6mwUs5Ol+Ms130dmqrIMAwNlrLpzPRj0zUdbLX7woGidvTk031acCxFkupesKhtvh/qh0em9OxoRTnH0sv29GlXf3HV/Xa6Y2u136GF/z5cyuqh4zOarLraUszo6h29Gq00N+z7tB6bcU46lz5/PVZ7fGBjna1jpZPGQgfGyrrjO4c1VXWXLA47G8u8/rhaS99u5H5Y7r2eHa3o5FxD23pyumioNO/x56dqcv0g/h3vmJoou6p78azArd1ZXXlBj+pemLZFkj72d0/oh4en41uF2IYarq/WAgMqZm1dd2GfHMua95r2dtW9QA8+P62Zuqe8Y6W3vfCCQJWG30ouxSszDJQcNb34Xs5X7ezVb914Ufp+R6ZqGptrxPe6M6QTMw01vPi/M2YcM6m6ccMMxStB+GE8w8qUtL03p2v39Kfb91OXDumz9x7QQ0dnW7PI4pWNkplmlqE4fmNIeceSZRqqt2bjm4ahwVJGrh8vZe4GgRzL1PbeXLxKQFshffs2LNUnvXlHL93dN69Y+tKtXXpqpKwTs3Ulq1CabSseqLV93TlH1Waghh/f4zBnx6sg+H6kQJFKWUfX7unTb9140ZIxsqRNecfUI8dmNTLXVBDGcZ+GF6iZLPupeFUqr205pKFSRgPdWY3NNeP4TKQ0rpScbZOVS2wzvjdyMWOnhbQT5aama75kxCt5DXRl1fB8nZhpxPeDLmSUy1jx/m5NzNjWk40nZrT6aHSuoefGK2p4ceFW1m7Nuoziz+1tLa3e9ALZlqmh7px6WrcNCMJo1d/B1X5vF/bp06NlVRp+2v5Lhkvzvl8LX3Omzs0Hxsq6/RvP6ofPT6vS8GRJsm3ztMcJ143Odbb33WrHL5ue9PvKV76id77znfrv//2/67rrrtPtt9+uv/qrv9JTTz2l4eFhveMd79COHTt02223SZK++93v6tWvfrU+/vGP62d+5mf05S9/WX/4h3+oBx98UJdffvlpP+9sBLk68QfS2dTeP0sFBaTVBzFW8xkb+b5rbcNSgRxJiwIlO5cI8Cy3HSu1uf35eSe+Q1vtNH272sfWGoRca7BltT/K08BV01Ol4beW7bTT42ip7Q7DSA8enU4DOUnAKnmfubqnuhsoklTMWCrmbNWagYqOrarnK29ben66Oi/glQQ2kzYtt08XHu9+GOqBw9M6OdfQ1u6cXnZhv3a3BbuWOm62twXSVtOXS+3DRYG9FQKV7e+XtU2NzNb17GhFdT/Q/qGS9g92zTtml/pOV5t+GlgsZeL9U3X9dJ915Zx5+7zgWGoGgb755LjmGq62FLPa1pfVkcm68qal3i5HewdL6sll1vTd3egg+Ho/JwwjHZmq6geHp9XwAl00XNK1u/rnHUdLvd9SAdoTs/X0WNu9pSDLMFTzgnnn05UCrmfTehMXKwXBT3eeO9vbSaALAADgzOu0sRAF4WfOWvp2I/fDcu+1XIH0JVu79J0DE2mBcLIEZVfW0Y6+/JLFmQfGyvqzbx7QPx+YVKXptWbDxQm/vYNF7eovLvma5Yqq4xn4cUF4PhPfx7rc9CXFKwEttcrAUkWolhmvVjPXSNpkpLdaiKIkeRcvC7qlmNGegeKi7TswVtbH/+FJfffglBpeoOQ2g1nH1N7Bkrb15Ba0e35B8VKFsNLShfSrKTRvL5ZeWKSdzJyzTSnnWCpmbfXknfTzx8oNzdQ8+a373g115/SaiwcXFfMvd/xMVJo6Ph3/3k+ydrVmoLofpLd5MQwpZ5u6oL+g7b159bY+f6rm6pGjMzo+21CzlRk0jfjeYZdu7dLPX71Dh8arS09CiCI9NVpJH8/b8RLoC1drSIqH2/uo4QWabXiqNf10VYRIcZLSDyNNVV3VWgXLyxWWr6UYeDWvOd3EktO95kydm5Ni5m89Ox4nz1d5nHDd6FznYkH4pif9JOnTn/60/uiP/kgjIyO6+uqr9alPfUrXX3+9JOk1r3mN9uzZoy9+8Yvp8//6r/9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- }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import math\n", - "import torch\n", - "import torch.nn.functional as F\n", - "\n", - "# ... (All your other functions like calculate_weighted_price,\n", - "# get_top_k_predictions, set_seed, etc. remain the same) ...\n", - "\n", - "# Set the maximum number of probabilities to fetch\n", - "TOP_K = 100\n", - "\n", - "class Tester:\n", - " \"\"\"\n", - " MODIFIED: This class now also analyzes and plots probability spread\n", - " for ALL inferences.\n", - " \"\"\"\n", - " def __init__(self, predictor, data, title=None, size=250):\n", - " self.predictor = predictor\n", - " self.data = data\n", - " self.title = title or predictor.__name__.replace(\"_\", \" \").title()\n", - " self.size = size\n", - " self.truths = []\n", - "\n", - " # From previous step\n", - " self.all_k_errors = []\n", - " self.max_k = TOP_K\n", - "\n", - " # For SPREAD ANALYSIS\n", - " self.all_prob_lists = []\n", - " self.prob_std_devs = []\n", - "\n", - " def run_datapoint(self, i):\n", - " datapoint = self.data[i]\n", - " base_prompt = datapoint[\"text\"]\n", - " prompt = make_prompt(base_prompt)\n", - " truth = datapoint[\"price\"]\n", - " self.truths.append(truth)\n", - "\n", - " # 1. Get the raw lists of prices and probabilities\n", - " prices, probabilities = self.predictor(prompt)\n", - "\n", - " # Store probability info for spread analysis\n", - " self.all_prob_lists.append(probabilities)\n", - " if probabilities:\n", - " self.prob_std_devs.append(np.std(probabilities))\n", - " else:\n", - " self.prob_std_devs.append(0.0)\n", - "\n", - " # --- k-analysis ---\n", - " errors_for_this_datapoint = []\n", - " if not prices:\n", - " # Handle cases where the model returned no valid prices\n", - " print(f\"{COLOR_MAP.get('red', '')}{i+1}: No valid prices found. \"\n", - " f\"Truth: ${truth:,.2f}.{RESET}\")\n", - " error = np.abs(0 - truth)\n", - " errors_for_this_datapoint = [error] * self.max_k\n", - " self.all_k_errors.append(errors_for_this_datapoint)\n", - " return\n", - "\n", - " for k in range(1, self.max_k + 1):\n", - " k_prices = prices[:k]\n", - " k_probabilities = probabilities[:k]\n", - " guess = calculate_weighted_price(k_prices, k_probabilities)\n", - " error = np.abs(guess - truth)\n", - " errors_for_this_datapoint.append(error)\n", - "\n", - " self.all_k_errors.append(errors_for_this_datapoint)\n", - "\n", - " # --- Print progress ---\n", - " title = datapoint[\"text\"].split(\"\\n\\n\")[1][:20] + \"...\"\n", - " k_1_err = errors_for_this_datapoint[0]\n", - " k_20_err = errors_for_this_datapoint[19]\n", - " k_max_err = errors_for_this_datapoint[-1]\n", - "\n", - " print(f\"{COLOR_MAP.get('orange', '')}{i+1}: Truth: ${truth:,.2f}. \"\n", - " f\"Errors (k=1, k=20, k={self.max_k}): \"\n", - " f\"(${k_1_err:,.2f}, ${k_20_err:,.2f}, ${k_max_err:,.2f}) \"\n", - " f\"Item: {title}{RESET}\")\n", - "\n", - " def plot_k_vs_error(self, k_values, avg_errors_by_k, best_k, min_error):\n", - " # (This function is unchanged)\n", - " plt.figure(figsize=(12, 8))\n", - " plt.plot(k_values, avg_errors_by_k, label='Average Error vs. k')\n", - " plt.axvline(x=best_k, color='red', linestyle='--',\n", - " label=f'Best k = {best_k} (Avg Error: ${min_error:,.2f})')\n", - " plt.xlabel('Number of Top Probabilities/Prices (k)')\n", - " plt.ylabel('Average Absolute Error ($)')\n", - " plt.title(f'Optimal k Analysis for {self.title}')\n", - " plt.legend()\n", - " plt.grid(True, which='both', linestyle='--', linewidth=0.5)\n", - " plt.xlim(left=1)\n", - " plt.savefig(\"k_vs_error_plot.png\") # Save the plot\n", - " print(\"Saved k_vs_error_plot.png\")\n", - " plt.close() # Close plot to free memory\n", - "\n", - " def plot_probability_spread(self, idx_min_std, idx_med_std, idx_max_std):\n", - " # (This function is unchanged)\n", - " probs_min = self.all_prob_lists[idx_min_std]\n", - " probs_med = self.all_prob_lists[idx_med_std]\n", - " probs_max = self.all_prob_lists[idx_max_std]\n", - " std_min = self.prob_std_devs[idx_min_std]\n", - " std_med = self.prob_std_devs[idx_med_std]\n", - " std_max = self.prob_std_devs[idx_max_std]\n", - "\n", - " fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(18, 7), sharey=True)\n", - " fig.suptitle('Probability Distribution Spread Analysis (Examples)', fontsize=16)\n", - "\n", - " def plot_strip(ax, probs, title):\n", - " if not probs:\n", - " ax.set_title(f\"{title}\\n(No probabilities found)\")\n", - " return\n", - " jitter = np.random.normal(0, 0.01, size=len(probs))\n", - " ax.scatter(jitter, probs, alpha=0.5, s=10) # Made points slightly larger\n", - " ax.set_title(title)\n", - " ax.set_xlabel(\"Jitter\")\n", - " ax.get_xaxis().set_ticks([])\n", - "\n", - " plot_strip(ax1, probs_min,\n", - " f'Inference {idx_min_std} (Lowest Spread)\\nStd Dev: {std_min:.6f}')\n", - " ax1.set_ylabel('Probability')\n", - " plot_strip(ax2, probs_med,\n", - " f'Inference {idx_med_std} (Median Spread)\\nStd Dev: {std_med:.6f}')\n", - " plot_strip(ax3, probs_max,\n", - " f'Inference {idx_max_std} (Highest Spread)\\nStd Dev: {std_max:.6f}')\n", - "\n", - " plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n", - " plt.savefig(\"spread_examples_plot.png\") # Save the plot\n", - " print(\"Saved spread_examples_plot.png\")\n", - " plt.close() # Close plot to free memory\n", - "\n", - " def plot_all_std_devs(self):\n", - " \"\"\"\n", - " NEW: Plots a histogram and a line plot of the standard deviation\n", - " for ALL inferences.\n", - " \"\"\"\n", - " if not self.prob_std_devs:\n", - " print(\"No probability spreads recorded, skipping all-std plot.\")\n", - " return\n", - "\n", - " # Create a figure with two subplots\n", - " fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 12))\n", - " fig.suptitle('Full Spread Analysis for All Inferences', fontsize=16)\n", - "\n", - " # --- Plot 1: Histogram ---\n", - " ax1.hist(self.prob_std_devs, bins=50, edgecolor='black')\n", - " ax1.set_title('Distribution of Probability Standard Deviations')\n", - " ax1.set_xlabel('Standard Deviation')\n", - " ax1.set_ylabel('Frequency (Number of Inferences)')\n", - "\n", - " mean_std = np.mean(self.prob_std_devs)\n", - " ax1.axvline(mean_std, color='red', linestyle='--',\n", - " label=f'Mean Std Dev: {mean_std:.6f}')\n", - " ax1.legend()\n", - "\n", - " # --- Plot 2: Line Plot ---\n", - " ax2.plot(self.prob_std_devs, marker='o', linestyle='-',\n", - " markersize=3, alpha=0.7, label='Std Dev per Inference')\n", - " ax2.set_title('Probability Standard Deviation per Inference')\n", - " ax2.set_xlabel('Inference Index (0 to 249)')\n", - " ax2.set_ylabel('Standard Deviation')\n", - "\n", - " ax2.axhline(mean_std, color='red', linestyle='--',\n", - " label=f'Mean Std Dev: {mean_std:.6f}')\n", - " ax2.legend()\n", - " ax2.set_xlim(0, len(self.prob_std_devs) - 1)\n", - "\n", - " plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n", - " plt.savefig(\"all_std_devs_plot.png\") # Save the plot\n", - " print(\"Saved all_std_devs_plot.png\")\n", - " plt.close() # Close plot to free memory\n", - "\n", - " def report(self):\n", - " \"\"\"\n", - " MODIFIED: Now calls all three plotting functions.\n", - " \"\"\"\n", - " if not self.all_k_errors:\n", - " print(\"\\nNo data to report on. Exiting.\")\n", - " return\n", - "\n", - " # --- 1. Optimal k Analysis ---\n", - " errors_array = np.array(self.all_k_errors)\n", - " avg_errors_by_k = np.mean(errors_array, axis=0)\n", - " best_k_index = np.argmin(avg_errors_by_k)\n", - " min_error = avg_errors_by_k[best_k_index]\n", - " best_k = best_k_index + 1\n", - "\n", - " print(\"\\n\" + \"=\"*40)\n", - " print(\"--- Optimal k Analysis Report ---\")\n", - " print(f\"Model: {self.title}\")\n", - " print(f\"Inferences Run: {self.size}\")\n", - " print(f\"Analyzed k from 1 to {self.max_k}\")\n", - " print(f\"===================================\")\n", - " print(f\"==> Best k: {best_k}\")\n", - " print(f\"==> Minimum Average Error: ${min_error:,.2f}\")\n", - " print(\"=\"*40 + \"\\n\")\n", - "\n", - " k_values = np.arange(1, self.max_k + 1)\n", - " self.plot_k_vs_error(k_values, avg_errors_by_k, best_k, min_error)\n", - "\n", - " # --- 2. Probability Spread Analysis ---\n", - " if not self.prob_std_devs:\n", - " print(\"\\nNo probability spreads recorded, skipping spread plots.\")\n", - " return\n", - "\n", - " print(\"\\n\" + \"=\"*40)\n", - " print(\"--- Probability Spread Analysis ---\")\n", - "\n", - " # Find indices for examples\n", - " std_sorted_indices = np.argsort(self.prob_std_devs)\n", - " idx_min_std = std_sorted_indices[0]\n", - " idx_med_std = std_sorted_indices[len(std_sorted_indices) // 2]\n", - " idx_max_std = std_sorted_indices[-1]\n", - "\n", - " print(f\"Lowest spread (std): {self.prob_std_devs[idx_min_std]:.6f} (Inference {idx_min_std})\")\n", - " print(f\"Median spread (std): {self.prob_std_devs[idx_med_std]:.6f} (Inference {idx_med_std})\")\n", - " print(f\"Highest spread (std): {self.prob_std_devs[idx_max_std]:.6f} (Inference {idx_max_std})\")\n", - " print(\"=\"*40 + \"\\n\")\n", - "\n", - " # Plot example spreads\n", - " self.plot_probability_spread(idx_min_std, idx_med_std, idx_max_std)\n", - "\n", - " # Plot all spreads\n", - " self.plot_all_std_devs()\n", - "\n", - "\n", - " def run(self):\n", - " # (This function is unchanged)\n", - " for i in range(self.size):\n", - " try:\n", - " self.run_datapoint(i)\n", - " except Exception as e:\n", - " print(f\"Error on datapoint {i}: {e}\")\n", - " self.report()\n", - "\n", - " @classmethod\n", - " def test(cls, function, data):\n", - " # (This function is unchanged)\n", - " cls(function, data).run()\n", - "\n", - "# --- EXECUTION (Unchanged) ---\n", - "# Assuming all your variables (tokenizer, test, etc.) are defined\n", - "tester = Tester(get_top_k_predictions, test, title=f\"{MODEL_ARTIFACT_NAME}:{REVISION_TAG}\")\n", - "tester.run()" - ], - "metadata": { - "id": "RrkPe0Y97KT4", - "outputId": "7cdeba58-f36b-40cf-c1db-dd81feb22581", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 57, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[93m1: Truth: $374.41. Errors (k=1, k=20, k=100): ($81.41, $73.24, $67.97) Item: OEM AC Compressor w/...\u001b[0m\n", - "\u001b[93m2: Truth: $225.11. Errors (k=1, k=20, k=100): ($84.11, $80.03, $82.84) Item: Motorcraft YB3125 Fa...\u001b[0m\n", - "\u001b[93m3: Truth: $61.68. Errors (k=1, k=20, k=100): ($20.68, $15.16, $3.86) Item: Dorman Front Washer ...\u001b[0m\n", - "\u001b[93m4: Truth: $599.99. Errors (k=1, k=20, k=100): ($99.99, $102.15, $89.32) Item: HP Premium HD Plus T...\u001b[0m\n", - "\u001b[93m5: Truth: $16.99. Errors (k=1, k=20, k=100): ($7.99, $5.32, $1.49) Item: Super Switch Pickup ...\u001b[0m\n", - "\u001b[93m6: Truth: $31.99. Errors (k=1, k=20, k=100): ($19.99, $17.74, $13.03) Item: Horror Bookmarks, Re...\u001b[0m\n", - "\u001b[93m7: Truth: $101.79. Errors (k=1, k=20, k=100): ($60.79, $57.40, $45.58) Item: SK6241 - Stinger 4 G...\u001b[0m\n", - "\u001b[93m8: Truth: $289.00. Errors (k=1, k=20, k=100): ($10.00, $22.44, $12.82) Item: Godox ML60Bi LED Lig...\u001b[0m\n", - "\u001b[93m9: Truth: $635.86. Errors (k=1, k=20, k=100): ($135.86, $32.93, $33.48) Item: Randall G3 Plus Comb...\u001b[0m\n", - "\u001b[93m10: Truth: $65.99. Errors (k=1, k=20, k=100): ($44.01, $53.82, $52.82) Item: HOLDWILL 6 Pack LED ...\u001b[0m\n", - "\u001b[93m11: Truth: $254.21. Errors (k=1, k=20, k=100): ($45.79, $49.20, $45.24) Item: Viking Horns 3 Gallo...\u001b[0m\n", - "\u001b[93m12: Truth: $412.99. Errors (k=1, k=20, k=100): ($12.99, $18.22, $11.09) Item: CURT 70110 Custom To...\u001b[0m\n", - "\u001b[93m13: Truth: $205.50. Errors (k=1, k=20, k=100): ($34.50, $58.06, $42.22) Item: Solar HAMMERED BRONZ...\u001b[0m\n", - "\u001b[93m14: Truth: $248.23. Errors (k=1, k=20, k=100): ($51.77, $21.44, $24.60) Item: COSTWAY Electric Tum...\u001b[0m\n", - "\u001b[93m15: Truth: $399.00. Errors (k=1, k=20, k=100): ($99.00, $51.34, $28.05) Item: FREE SIGNAL TV Trans...\u001b[0m\n", - "\u001b[93m16: Truth: $373.94. Errors (k=1, k=20, k=100): ($35.94, $28.00, $26.70) Item: Bilstein 5100 Monotu...\u001b[0m\n", - "\u001b[93m17: Truth: $92.89. Errors (k=1, k=20, k=100): ($2.11, $4.40, $1.19) Item: Sangean K-200 Multi-...\u001b[0m\n", - "\u001b[93m18: Truth: $51.99. Errors (k=1, k=20, k=100): ($52.01, $67.21, $72.74) Item: Charles Leonard Magn...\u001b[0m\n", - "\u001b[93m19: Truth: $179.00. Errors (k=1, k=20, k=100): ($20.00, $65.59, $64.52) Item: Gigabyte AMD Radeon ...\u001b[0m\n", - "\u001b[93m20: Truth: $19.42. Errors (k=1, k=20, k=100): ($0.42, $2.47, $2.24) Item: 3dRose LLC 8 x 8 x 0...\u001b[0m\n", - "\u001b[93m21: Truth: $539.95. Errors (k=1, k=20, k=100): ($40.95, $17.93, $10.77) Item: ROKINON 85mm F1.4 Au...\u001b[0m\n", - "\u001b[93m22: Truth: $147.67. Errors (k=1, k=20, k=100): ($40.67, $43.67, $37.13) Item: Headlight Assembly C...\u001b[0m\n", - "\u001b[93m23: Truth: $24.99. Errors (k=1, k=20, k=100): ($24.01, $18.60, $27.46) Item: ASI NAUTICAL 2.5 Inc...\u001b[0m\n", - "\u001b[93m24: Truth: $149.00. Errors (k=1, k=20, k=100): ($80.00, $68.07, $66.76) Item: Behringer TUBE OVERD...\u001b[0m\n", - "\u001b[93m25: Truth: $16.99. Errors (k=1, k=20, k=100): ($4.99, $4.12, $2.07) Item: Fun Express Insect F...\u001b[0m\n", - "\u001b[93m26: Truth: $7.99. Errors (k=1, k=20, k=100): ($2.01, $2.80, $7.32) Item: WAFJAMF Roller Stamp...\u001b[0m\n", - "\u001b[93m27: Truth: $199.99. Errors (k=1, k=20, k=100): ($13.99, $15.86, $6.92) Item: Capulina Tiffany Flo...\u001b[0m\n", - "\u001b[93m28: Truth: $251.45. Errors (k=1, k=20, k=100): ($1.45, $6.34, $3.83) Item: Apple Watch Series 6...\u001b[0m\n", - "\u001b[93m29: Truth: $231.62. Errors (k=1, k=20, k=100): ($60.62, $69.85, $51.89) Item: ICON 01725 Tandem Ax...\u001b[0m\n", - "\u001b[93m30: Truth: $135.00. Errors (k=1, k=20, k=100): ($35.00, $52.41, $46.66) Item: SanDisk 128GB Ultra ...\u001b[0m\n", - "\u001b[93m31: Truth: $356.62. Errors (k=1, k=20, k=100): ($163.62, $137.01, $135.61) Item: Velvac - 715427\n", - "2020...\u001b[0m\n", - "\u001b[93m32: Truth: $257.99. Errors (k=1, k=20, k=100): ($7.99, $40.21, $38.93) Item: TCMT Passenger Backr...\u001b[0m\n", - "\u001b[93m33: Truth: $27.99. Errors (k=1, k=20, k=100): ($11.99, $10.64, $1.70) Item: Alnicov 63.5MM Brass...\u001b[0m\n", - "\u001b[93m34: Truth: $171.20. Errors (k=1, k=20, k=100): ($80.20, $55.81, $54.29) Item: Subaru Forester Outb...\u001b[0m\n", - "\u001b[93m35: Truth: $225.00. Errors (k=1, k=20, k=100): ($24.00, $38.28, $58.60) Item: Richmond Auto Uphols...\u001b[0m\n", - "\u001b[93m36: Truth: $105.00. Errors (k=1, k=20, k=100): ($54.00, $64.77, $73.80) Item: AP-39 Automotive Pai...\u001b[0m\n", - "\u001b[93m37: Truth: $299.99. Errors (k=1, k=20, k=100): ($0.99, $31.50, $49.48) Item: Road Top Wireless Ca...\u001b[0m\n", - "\u001b[93m38: Truth: $535.09. Errors (k=1, k=20, k=100): ($9.09, $33.06, $35.93) Item: Gibson Performance E...\u001b[0m\n", - "\u001b[93m39: Truth: $12.33. Errors (k=1, k=20, k=100): ($0.33, $3.27, $7.63) Item: Bella Tunno Happy Li...\u001b[0m\n", - "\u001b[93m40: Truth: $84.99. Errors (k=1, k=20, k=100): ($4.99, $1.12, $5.35) Item: CANMORE H300 Handhel...\u001b[0m\n", - "\u001b[93m41: Truth: $15.99. Errors (k=1, k=20, k=100): ($2.99, $0.54, $1.80) Item: DCPOWER AC Adapter C...\u001b[0m\n", - "\u001b[93m42: Truth: $62.44. Errors (k=1, k=20, k=100): ($17.44, $16.50, $3.31) Item: Sharp, Commercial De...\u001b[0m\n", - "\u001b[93m43: Truth: $82.99. Errors (k=1, k=20, k=100): ($17.99, $20.16, $10.23) Item: Melissa & Doug Lifel...\u001b[0m\n", - "\u001b[93m44: Truth: $599.95. Errors (k=1, k=20, k=100): ($201.95, $213.92, $204.53) Item: Sony SSCS8 2-Way Cen...\u001b[0m\n", - "\u001b[93m45: Truth: $194.99. Errors (k=1, k=20, k=100): ($54.01, $45.19, $38.85) Item: ASUS Chromebook CX1,...\u001b[0m\n", - "\u001b[93m46: Truth: $344.95. Errors (k=1, k=20, k=100): ($55.05, $53.22, $53.36) Item: FiiO X7 32GB Hi-Res ...\u001b[0m\n", - "\u001b[93m47: Truth: $37.99. Errors (k=1, k=20, k=100): ($2.01, $2.70, $6.62) Item: TORRO Leather Case C...\u001b[0m\n", - "\u001b[93m48: Truth: $224.35. Errors (k=1, k=20, k=100): ($19.35, $15.58, $1.77) Item: Universal Air Condit...\u001b[0m\n", - "\u001b[93m49: Truth: $814.00. Errors (k=1, k=20, k=100): ($14.00, $42.04, $48.29) Item: Street Series Stainl...\u001b[0m\n", - "\u001b[93m50: Truth: $439.88. Errors (k=1, k=20, k=100): ($40.88, $66.90, $64.77) Item: Lenovo IdeaPad 3 Lap...\u001b[0m\n", - "\u001b[93m51: Truth: $341.43. Errors (k=1, k=20, k=100): ($92.43, $74.61, $76.63) Item: Access Bed Covers To...\u001b[0m\n", - "\u001b[93m52: Truth: $46.78. Errors (k=1, k=20, k=100): ($1.78, $12.93, $22.48) Item: G.I. JOE Hasbro 3 3/...\u001b[0m\n", - "\u001b[93m53: Truth: $171.44. Errors (k=1, k=20, k=100): ($12.56, $6.77, $16.96) Item: T&S Brass Double Pan...\u001b[0m\n", - "\u001b[93m54: Truth: $458.00. Errors (k=1, k=20, k=100): ($158.00, $108.54, $51.39) Item: ZTUOAUMA Fuel Inject...\u001b[0m\n", - "\u001b[93m55: Truth: $130.75. Errors (k=1, k=20, k=100): ($119.25, $52.77, $40.15) Item: Hp Prime Graphing Ca...\u001b[0m\n", - "\u001b[93m56: Truth: $83.81. Errors (k=1, k=20, k=100): ($52.81, $51.58, $40.19) Item: Lowrance Nmea 2000 2...\u001b[0m\n", - "\u001b[93m57: Truth: $386.39. Errors (k=1, k=20, k=100): ($245.39, $232.53, $228.40) Item: Jeep Genuine Accesso...\u001b[0m\n", - "\u001b[93m58: Truth: $169.00. Errors (k=1, k=20, k=100): ($130.00, $47.50, $55.03) Item: GODOX CB-06 Hard Car...\u001b[0m\n", - "\u001b[93m59: Truth: $17.95. Errors (k=1, k=20, k=100): ($2.95, $1.67, $0.61) Item: Au-Tomotive Gold, IN...\u001b[0m\n", - "\u001b[93m60: Truth: $269.00. Errors (k=1, k=20, k=100): ($20.00, $40.42, $51.28) Item: Snailfly Black Roof ...\u001b[0m\n", - "\u001b[93m61: Truth: $77.77. Errors (k=1, k=20, k=100): ($8.77, $22.18, $10.43) Item: KING SHA Anti Glare ...\u001b[0m\n", - "\u001b[93m62: Truth: $88.99. Errors (k=1, k=20, k=100): ($7.99, $3.89, $3.66) Item: APS Compatible with ...\u001b[0m\n", - "\u001b[93m63: Truth: $364.41. Errors (k=1, k=20, k=100): ($65.41, $100.45, $88.50) Item: Wilwood Engineering ...\u001b[0m\n", - "\u001b[93m64: Truth: $127.03. Errors (k=1, k=20, k=100): ($13.97, $19.73, $26.43) Item: ACDelco Gold Starter...\u001b[0m\n", - "\u001b[93m65: Truth: $778.95. Errors (k=1, k=20, k=100): ($242.95, $210.19, $194.48) Item: UWS Matte Black Heav...\u001b[0m\n", - "\u001b[93m66: Truth: $206.66. Errors (k=1, k=20, k=100): ($43.34, $3.27, $1.90) Item: Dell Latitude E5440 ...\u001b[0m\n", - "\u001b[93m67: Truth: $35.94. Errors (k=1, k=20, k=100): ($10.06, $6.42, $19.62) Item: (Plug and Play) Spar...\u001b[0m\n", - "\u001b[93m68: Truth: $149.00. Errors (k=1, k=20, k=100): ($101.00, $13.55, $4.29) Item: The Ultimate Roadsid...\u001b[0m\n", - "\u001b[93m69: Truth: $251.98. Errors (k=1, k=20, k=100): ($42.98, $31.22, $27.12) Item: Brand New 18 x 8.5 R...\u001b[0m\n", - "\u001b[93m70: Truth: $160.00. Errors (k=1, k=20, k=100): ($90.00, $76.21, $65.07) Item: Headlight Headlamp L...\u001b[0m\n", - "\u001b[93m71: Truth: $39.99. Errors (k=1, k=20, k=100): ($4.99, $7.22, $1.65) Item: Lilo And Stitch Delu...\u001b[0m\n", - "\u001b[93m72: Truth: $362.41. Errors (k=1, k=20, k=100): ($112.41, $109.19, $107.49) Item: AC Compressor & A/C ...\u001b[0m\n", - "\u001b[93m73: Truth: $344.00. Errors (k=1, k=20, k=100): ($44.00, $27.91, $20.34) Item: House Of Troy Pinnac...\u001b[0m\n", - "\u001b[93m74: Truth: $25.09. Errors (k=1, k=20, k=100): ($25.91, $32.84, $44.71) Item: Juno T29 WH Floating...\u001b[0m\n", - "\u001b[93m75: Truth: $175.95. Errors (k=1, k=20, k=100): ($104.95, $102.92, $92.80) Item: Sherman GO-PARTS - f...\u001b[0m\n", - "\u001b[93m76: Truth: $132.64. Errors (k=1, k=20, k=100): ($167.36, $175.31, $170.27) Item: Roland RPU-3 Electro...\u001b[0m\n", - "\u001b[93m77: Truth: $422.99. Errors (k=1, k=20, k=100): ($122.99, $82.91, $70.93) Item: Rockland VMI14 12,00...\u001b[0m\n", - "\u001b[93m78: Truth: $146.48. Errors (k=1, k=20, k=100): ($0.52, $5.95, $11.84) Item: Max Advanced Brakes ...\u001b[0m\n", - "\u001b[93m79: Truth: $156.83. Errors (k=1, k=20, k=100): ($2.83, $6.12, $1.31) Item: Quality-Built 11030 ...\u001b[0m\n", - "\u001b[93m80: Truth: $251.99. Errors (k=1, k=20, k=100): ($101.99, $88.95, $98.62) Item: Lucida LG-510 Studen...\u001b[0m\n", - "\u001b[93m81: Truth: $940.33. Errors (k=1, k=20, k=100): ($799.33, $794.77, $789.80) Item: Longacre Aluminum Tu...\u001b[0m\n", - "\u001b[93m82: Truth: $52.99. Errors (k=1, k=20, k=100): ($8.01, $14.94, $26.77) Item: Motion Pro Adjustabl...\u001b[0m\n", - "\u001b[93m83: Truth: $219.95. Errors (k=1, k=20, k=100): ($30.05, $57.96, $65.86) Item: Glyph Thunderbolt 3 ...\u001b[0m\n", - "\u001b[93m84: Truth: $441.03. Errors (k=1, k=20, k=100): ($141.03, $138.31, $135.56) Item: TOYO Open Country MT...\u001b[0m\n", - "\u001b[93m85: Truth: $168.98. Errors (k=1, k=20, k=100): ($18.98, $28.33, $27.46) Item: Razer Seiren X USB S...\u001b[0m\n", - "\u001b[93m86: Truth: $2.49. Errors (k=1, k=20, k=100): ($1.51, $1.95, $2.65) Item: Happy Birthday to Da...\u001b[0m\n", - "\u001b[93m87: Truth: $98.62. Errors (k=1, k=20, k=100): ($1.38, $5.55, $1.24) Item: Little Tikes My Real...\u001b[0m\n", - "\u001b[93m88: Truth: $256.95. Errors (k=1, k=20, k=100): ($43.05, $24.48, $23.78) Item: Studio M Peace and H...\u001b[0m\n", - "\u001b[93m89: Truth: $30.99. Errors (k=1, k=20, k=100): ($10.99, $9.62, $6.62) Item: MyVolts 12V Power Su...\u001b[0m\n", - "\u001b[93m90: Truth: $569.84. Errors (k=1, k=20, k=100): ($69.84, $22.40, $24.21) Item: Dell Latitude 7212 R...\u001b[0m\n", - "\u001b[93m91: Truth: $177.99. Errors (k=1, k=20, k=100): ($16.99, $15.89, $19.06) Item: Covermates Contour F...\u001b[0m\n", - "\u001b[93m92: Truth: $997.99. Errors (k=1, k=20, k=100): ($0.01, $1.90, $3.08) Item: Westin Black HDX Gri...\u001b[0m\n", - "\u001b[93m93: Truth: $219.00. Errors (k=1, k=20, k=100): ($31.00, $27.85, $41.71) Item: Fieldpiece JL2 Job L...\u001b[0m\n", - "\u001b[93m94: Truth: $225.55. Errors (k=1, k=20, k=100): ($74.45, $63.37, $49.08) Item: hansgrohe Talis S Mo...\u001b[0m\n", - "\u001b[93m95: Truth: $495.95. Errors (k=1, k=20, k=100): ($503.05, $207.91, $189.62) Item: G-Technology G-SPEED...\u001b[0m\n", - "\u001b[93m96: Truth: $942.37. Errors (k=1, k=20, k=100): ($42.37, $108.81, $141.76) Item: DreamLine Shower Doo...\u001b[0m\n", - "\u001b[93m97: Truth: $1.94. Errors (k=1, k=20, k=100): ($69.06, $62.71, $71.10) Item: Sanctuary Square Bac...\u001b[0m\n", - "\u001b[93m98: Truth: $284.34. Errors (k=1, k=20, k=100): ($15.66, $0.19, $1.80) Item: Pelican Protector 17...\u001b[0m\n", - "\u001b[93m99: Truth: $171.90. Errors (k=1, k=20, k=100): ($30.90, $32.72, $31.78) Item: Brock Replacement Dr...\u001b[0m\n", - "\u001b[93m100: Truth: $144.99. Errors (k=1, k=20, k=100): ($24.01, $13.93, $32.85) Item: Carlinkit Ai Box Min...\u001b[0m\n", - "\u001b[93m101: Truth: $470.47. Errors (k=1, k=20, k=100): ($70.47, $23.99, $46.56) Item: StarDot YouTube Live...\u001b[0m\n", - "\u001b[93m102: Truth: $66.95. Errors (k=1, k=20, k=100): ($5.95, $4.51, $2.47) Item: Atomic Compatible ME...\u001b[0m\n", - "\u001b[93m103: Truth: $117.00. Errors (k=1, k=20, k=100): ($25.00, $10.61, $0.31) Item: Bandai Awakening of ...\u001b[0m\n", - "\u001b[93m104: Truth: $172.14. Errors (k=1, k=20, k=100): ($1.14, $9.89, $24.14) Item: Fit System 62135G Pa...\u001b[0m\n", - "\u001b[93m105: Truth: $392.74. Errors (k=1, k=20, k=100): ($8.74, $13.62, $7.93) Item: Black Horse Black Al...\u001b[0m\n", - "\u001b[93m106: Truth: $16.99. Errors (k=1, k=20, k=100): ($2.99, $1.77, $4.41) Item: Dearsun Twinkle Star...\u001b[0m\n", - "\u001b[93m107: Truth: $1.34. Errors (k=1, k=20, k=100): ($0.34, $0.91, $1.48) Item: Pokemon - Gallade Sp...\u001b[0m\n", - "\u001b[93m108: Truth: $349.98. Errors (k=1, k=20, k=100): ($99.98, $119.63, $121.66) Item: Ibanez GIO Series Cl...\u001b[0m\n", - "\u001b[93m109: Truth: $370.71. Errors (k=1, k=20, k=100): ($130.71, $84.50, $97.73) Item: Set 2 Heavy Duty 12 ...\u001b[0m\n", - "\u001b[93m110: Truth: $65.88. Errors (k=1, k=20, k=100): ($12.88, $15.71, $6.02) Item: Hairpin Table Legs 2...\u001b[0m\n", - "\u001b[93m111: Truth: $229.99. Errors (k=1, k=20, k=100): ($10.01, $37.54, $2.27) Item: Marada Racing Seat w...\u001b[0m\n", - "\u001b[93m112: Truth: $9.14. Errors (k=1, k=20, k=100): ($5.14, $2.90, $1.03) Item: Remington Industries...\u001b[0m\n", - "\u001b[93m113: Truth: $199.00. Errors (k=1, k=20, k=100): ($201.00, $310.61, $293.43) Item: Acer Ultrabook, Inte...\u001b[0m\n", - "\u001b[93m114: Truth: $109.99. Errors (k=1, k=20, k=100): ($140.01, $145.60, $127.75) Item: ICBEAMER 7 RGB LED H...\u001b[0m\n", - "\u001b[93m115: Truth: $570.42. Errors (k=1, k=20, k=100): ($194.42, $213.78, $222.40) Item: R1 Concepts Front Re...\u001b[0m\n", - "\u001b[93m116: Truth: $279.99. Errors (k=1, k=20, k=100): ($20.01, $18.13, $11.73) Item: Camplux 2.64 GPM Tan...\u001b[0m\n", - "\u001b[93m117: Truth: $30.99. Errors (k=1, k=20, k=100): ($6.01, $4.87, $10.38) Item: KNOKLOCK 10 Pack 3.7...\u001b[0m\n", - "\u001b[93m118: Truth: $31.99. Errors (k=1, k=20, k=100): ($13.01, $13.06, $20.99) Item: Valley Enterprises Y...\u001b[0m\n", - "\u001b[93m119: Truth: $15.90. Errors (k=1, k=20, k=100): ($13.10, $11.35, $27.35) Item: G9 LED Light 100W re...\u001b[0m\n", - "\u001b[93m120: Truth: $45.99. Errors (k=1, k=20, k=100): ($24.01, $41.82, $45.33) Item: ZCHAOZ 4 Lights Anti...\u001b[0m\n", - "\u001b[93m121: Truth: $113.52. Errors (k=1, k=20, k=100): ($136.48, $79.33, $60.98) Item: Honeywell Honeywell ...\u001b[0m\n", - "\u001b[93m122: Truth: $516.99. Errors (k=1, k=20, k=100): ($216.99, $179.92, $178.28) Item: Patriot Exhaust 1-7/...\u001b[0m\n", - "\u001b[93m123: Truth: $196.99. Errors (k=1, k=20, k=100): ($105.99, $102.06, $92.63) Item: Fitrite Autopart New...\u001b[0m\n", - "\u001b[93m124: Truth: $46.55. Errors (k=1, k=20, k=100): ($5.55, $6.70, $4.97) Item: Technical Precision ...\u001b[0m\n", - "\u001b[93m125: Truth: $356.99. Errors (k=1, k=20, k=100): ($63.99, $19.36, $20.80) Item: Covercraft Carhartt ...\u001b[0m\n", - "\u001b[93m126: Truth: $319.95. Errors (k=1, k=20, k=100): ($20.95, $18.09, $10.82) Item: Sennheiser SD Pro 2 ...\u001b[0m\n", - "\u001b[93m127: Truth: $96.06. Errors (k=1, k=20, k=100): ($4.94, $18.64, $21.38) Item: Hitachi Mass Air Flo...\u001b[0m\n", - "\u001b[93m128: Truth: $190.99. Errors (k=1, k=20, k=100): ($59.01, $0.13, $2.20) Item: AmScope LED Cordless...\u001b[0m\n", - "\u001b[93m129: Truth: $257.95. Errors (k=1, k=20, k=100): ($196.95, $194.13, $186.50) Item: Front Left Driver Si...\u001b[0m\n", - "\u001b[93m130: Truth: $62.95. Errors (k=1, k=20, k=100): ($51.05, $55.18, $52.94) Item: Premium Replica Hubc...\u001b[0m\n", - "\u001b[93m131: Truth: $47.66. Errors (k=1, k=20, k=100): ($15.34, $8.95, $23.66) Item: Excellerations Phoni...\u001b[0m\n", - "\u001b[93m132: Truth: $226.99. Errors (k=1, k=20, k=100): ($23.01, $72.83, $58.39) Item: RC4WD BigDog Dual Ax...\u001b[0m\n", - "\u001b[93m133: Truth: $359.95. Errors (k=1, k=20, k=100): ($109.95, $70.13, $79.04) Item: Unknown Stage 2 Clut...\u001b[0m\n", - "\u001b[93m134: Truth: $78.40. Errors (k=1, k=20, k=100): ($37.40, $12.13, $4.54) Item: Dodge Ram 1500 Mopar...\u001b[0m\n", - "\u001b[93m135: Truth: $172.77. Errors (k=1, k=20, k=100): ($18.77, $12.86, $8.96) Item: Pro Comp Alloys Seri...\u001b[0m\n", - "\u001b[93m136: Truth: $316.45. Errors (k=1, k=20, k=100): ($13.55, $8.57, $16.25) Item: Detroit Axle - Front...\u001b[0m\n", - "\u001b[93m137: Truth: $87.99. Errors (k=1, k=20, k=100): ($3.01, $4.59, $13.82) Item: ECCPP Rear Wheel Axl...\u001b[0m\n", - "\u001b[93m138: Truth: $226.63. Errors (k=1, k=20, k=100): ($23.37, $6.66, $2.56) Item: Dell Latitude E6520 ...\u001b[0m\n", - "\u001b[93m139: Truth: $31.49. Errors (k=1, k=20, k=100): ($10.49, $5.58, $2.45) Item: F FIERCE CYCLE 251pc...\u001b[0m\n", - "\u001b[93m140: Truth: $196.00. Errors (k=1, k=20, k=100): ($44.00, $1.28, $7.85) Item: Flash Furniture 4 Pk...\u001b[0m\n", - "\u001b[93m141: Truth: $78.40. Errors (k=1, k=20, k=100): ($2.60, $24.09, $27.28) Item: B&M 30287 Throttle V...\u001b[0m\n", - "\u001b[93m142: Truth: $116.25. Errors (k=1, k=20, k=100): ($24.75, $29.03, $30.67) Item: Gates TCK226 PowerGr...\u001b[0m\n", - "\u001b[93m143: Truth: $112.78. Errors (k=1, k=20, k=100): ($28.22, $26.80, $26.36) Item: Monroe Shocks & Stru...\u001b[0m\n", - "\u001b[93m144: Truth: $27.32. Errors (k=1, k=20, k=100): ($13.68, $26.25, $38.11) Item: Feit Electric 35W EQ...\u001b[0m\n", - "\u001b[93m145: Truth: $145.91. Errors (k=1, k=20, k=100): ($41.91, $36.59, $28.75) Item: Yellow Jacket 2806 C...\u001b[0m\n", - "\u001b[93m146: Truth: $171.09. Errors (k=1, k=20, k=100): ($30.09, $21.15, $9.71) Item: Garage-Pro Tailgate ...\u001b[0m\n", - "\u001b[93m147: Truth: $167.95. Errors (k=1, k=20, k=100): ($23.95, $30.33, $16.20) Item: 3M Perfect It Buffin...\u001b[0m\n", - "\u001b[93m148: Truth: $28.49. Errors (k=1, k=20, k=100): ($17.51, $14.91, $25.36) Item: Chinese Style Dollho...\u001b[0m\n", - "\u001b[93m149: Truth: $122.23. Errors (k=1, k=20, k=100): ($61.23, $56.11, $44.20) Item: Generic NRG Innovati...\u001b[0m\n", - "\u001b[93m150: Truth: $32.99. Errors (k=1, k=20, k=100): ($7.01, $8.25, $16.22) Item: Learning Resources C...\u001b[0m\n", - "\u001b[93m151: Truth: $71.20. Errors (k=1, k=20, k=100): ($29.80, $35.02, $37.36) Item: Bosch Automotive 154...\u001b[0m\n", - "\u001b[93m152: Truth: $112.75. Errors (k=1, k=20, k=100): ($51.75, $46.92, $37.04) Item: Case of 24-2 Inch Bl...\u001b[0m\n", - "\u001b[93m153: Truth: $142.43. Errors (k=1, k=20, k=100): ($39.43, $34.56, $33.71) Item: MOCA Engine Water Pu...\u001b[0m\n", - "\u001b[93m154: Truth: $398.99. Errors (k=1, k=20, k=100): ($99.99, $89.42, $84.44) Item: SAREMAS Foot Step Ba...\u001b[0m\n", - "\u001b[93m155: Truth: $449.00. Errors (k=1, k=20, k=100): ($151.00, $151.79, $140.86) Item: Gretsch G9210 Square...\u001b[0m\n", - "\u001b[93m156: Truth: $189.00. Errors (k=1, k=20, k=100): ($61.00, $2.60, $6.69) Item: NikoMaku Mirror Dash...\u001b[0m\n", - "\u001b[93m157: Truth: $120.91. Errors (k=1, k=20, k=100): ($9.09, $24.58, $20.46) Item: Fenix HP25R v2.0 USB...\u001b[0m\n", - "\u001b[93m158: Truth: $203.53. Errors (k=1, k=20, k=100): ($31.53, $33.44, $31.24) Item: R&L Racing Heavy Dut...\u001b[0m\n", - "\u001b[93m159: Truth: $349.99. Errors (k=1, k=20, k=100): ($99.99, $75.43, $83.50) Item: Garmin GPSMAP 64sx, ...\u001b[0m\n", - "\u001b[93m160: Truth: $34.35. Errors (k=1, k=20, k=100): ($23.35, $22.26, $17.86) Item: Brown 5-7/8 X 8-1/2 ...\u001b[0m\n", - "\u001b[93m161: Truth: $384.99. Errors (k=1, k=20, k=100): ($85.99, $79.46, $66.34) Item: GAOMON PD2200 Pen Di...\u001b[0m\n", - "\u001b[93m162: Truth: $211.00. Errors (k=1, k=20, k=100): ($25.00, $27.41, $21.76) Item: VXMOTOR for 97-03 Fo...\u001b[0m\n", - "\u001b[93m163: Truth: $129.00. Errors (k=1, k=20, k=100): ($121.00, $40.35, $34.05) Item: HP EliteBook 2540p I...\u001b[0m\n", - "\u001b[93m164: Truth: $111.45. Errors (k=1, k=20, k=100): ($87.45, $82.40, $70.87) Item: Green EPX Mixing Noz...\u001b[0m\n", - "\u001b[93m165: Truth: $81.12. Errors (k=1, k=20, k=100): ($50.12, $46.44, $38.33) Item: Box Partners 6 1/4 x...\u001b[0m\n", - "\u001b[93m166: Truth: $457.08. Errors (k=1, k=20, k=100): ($57.08, $81.73, $84.94) Item: Vixen Air 1/2 NPT Ai...\u001b[0m\n", - "\u001b[93m167: Truth: $49.49. Errors (k=1, k=20, k=100): ($40.51, $41.52, $43.42) Item: Smart Floor Lamp, Mu...\u001b[0m\n", - "\u001b[93m168: Truth: $80.56. Errors (k=1, k=20, k=100): ($49.56, $47.97, $35.94) Item: SOZG 324mm Wheelbase...\u001b[0m\n", - "\u001b[93m169: Truth: $278.39. Errors (k=1, k=20, k=100): ($10.61, $8.25, $8.67) Item: Mickey Thompson ET S...\u001b[0m\n", - "\u001b[93m170: Truth: $364.50. Errors (k=1, k=20, k=100): ($109.50, $96.25, $93.58) Item: Pirelli 106W XL RFT ...\u001b[0m\n", - "\u001b[93m171: Truth: $378.99. Errors (k=1, k=20, k=100): ($78.99, $93.39, $97.83) Item: Torklift C3212 Rear ...\u001b[0m\n", - "\u001b[93m172: Truth: $165.28. Errors (k=1, k=20, k=100): ($27.72, $17.15, $35.74) Item: Cardone Remanufactur...\u001b[0m\n", - "\u001b[93m173: Truth: $56.74. Errors (k=1, k=20, k=100): ($15.74, $3.36, $9.43) Item: Kidde AccessPoint 00...\u001b[0m\n", - "\u001b[93m174: Truth: $307.95. Errors (k=1, k=20, k=100): ($7.95, $3.05, $8.73) Item: 3M Protecta Self Ret...\u001b[0m\n", - "\u001b[93m175: Truth: $38.00. Errors (k=1, k=20, k=100): ($11.00, $18.24, $30.56) Item: Plantronics Wired He...\u001b[0m\n", - "\u001b[93m176: Truth: $53.00. Errors (k=1, k=20, k=100): ($47.00, $65.60, $56.80) Item: Logitech K750 Wirele...\u001b[0m\n", - "\u001b[93m177: Truth: $498.00. Errors (k=1, k=20, k=100): ($98.00, $26.16, $34.45) Item: Olympus PEN E-PL9 Bo...\u001b[0m\n", - "\u001b[93m178: Truth: $53.99. Errors (k=1, k=20, k=100): ($87.01, $89.49, $88.42) Item: Beck/Arnley Hub & Be...\u001b[0m\n", - "\u001b[93m179: Truth: $350.00. Errors (k=1, k=20, k=100): ($0.00, $4.69, $8.70) Item: Eibach Pro-Kit Perfo...\u001b[0m\n", - "\u001b[93m180: Truth: $299.95. Errors (k=1, k=20, k=100): ($100.05, $44.21, $55.26) Item: LEGO DC Batman 1989 ...\u001b[0m\n", - "\u001b[93m181: Truth: $94.93. Errors (k=1, k=20, k=100): ($13.93, $8.68, $4.13) Item: Kingston Brass Resto...\u001b[0m\n", - "\u001b[93m182: Truth: $379.00. Errors (k=1, k=20, k=100): ($80.00, $46.76, $31.08) Item: Polk Vanishing Serie...\u001b[0m\n", - "\u001b[93m183: Truth: $299.95. Errors (k=1, k=20, k=100): ($49.95, $23.89, $25.73) Item: Spec-D Tuning LED Pr...\u001b[0m\n", - "\u001b[93m184: Truth: $24.99. Errors (k=1, k=20, k=100): ($9.99, $8.24, $6.19) Item: RICHMOND & FINCH Air...\u001b[0m\n", - "\u001b[93m185: Truth: $41.04. Errors (k=1, k=20, k=100): ($72.96, $68.47, $71.88) Item: LFA Industries - mm ...\u001b[0m\n", - "\u001b[93m186: Truth: $327.90. Errors (k=1, k=20, k=100): ($87.90, $104.82, $120.04) Item: SAUTVS LED Headlight...\u001b[0m\n", - "\u001b[93m187: Truth: $10.99. Errors (k=1, k=20, k=100): ($11.01, $9.26, $16.89) Item: 2 Pack Combo Womens ...\u001b[0m\n", - "\u001b[93m188: Truth: $14.99. Errors (k=1, k=20, k=100): ($0.01, $0.01, $0.12) Item: Arepa - Venezuelan c...\u001b[0m\n", - "\u001b[93m189: Truth: $84.95. Errors (k=1, k=20, k=100): ($43.95, $41.84, $29.38) Item: Schlage Lock Company...\u001b[0m\n", - "\u001b[93m190: Truth: $111.00. Errors (k=1, k=20, k=100): ($10.00, $9.44, $6.47) Item: Techni Mobili White ...\u001b[0m\n", - "\u001b[93m191: Truth: $123.73. Errors (k=1, k=20, k=100): ($42.27, $44.12, $48.62) Item: Special Lite Product...\u001b[0m\n", - "\u001b[93m192: Truth: $557.38. Errors (k=1, k=20, k=100): ($58.38, $36.33, $42.06) Item: Tascam Digital Porta...\u001b[0m\n", - "\u001b[93m193: Truth: $95.55. Errors (k=1, k=20, k=100): ($3.55, $1.53, $10.52) Item: Glow Lighting Vista ...\u001b[0m\n", - "\u001b[93m194: Truth: $154.00. Errors (k=1, k=20, k=100): ($15.00, $2.99, $2.65) Item: Z3 Wind Deflector, S...\u001b[0m\n", - "\u001b[93m195: Truth: $198.99. Errors (k=1, k=20, k=100): ($101.01, $15.42, $2.67) Item: Olympus E-20 5MP Dig...\u001b[0m\n", - "\u001b[93m196: Truth: $430.44. Errors (k=1, k=20, k=100): ($180.44, $182.10, $197.87) Item: PHYNEDI 1 1000 World...\u001b[0m\n", - "\u001b[93m197: Truth: $45.67. Errors (k=1, k=20, k=100): ($27.67, $24.15, $16.33) Item: YANGHUAN Unstable Un...\u001b[0m\n", - "\u001b[93m198: Truth: $249.00. Errors (k=1, k=20, k=100): ($51.00, $35.81, $5.07) Item: Interlogix NetworX T...\u001b[0m\n", - "\u001b[93m199: Truth: $42.99. Errors (k=1, k=20, k=100): ($21.99, $17.87, $6.42) Item: Steering Damper,Univ...\u001b[0m\n", - "\u001b[93m200: Truth: $181.33. Errors (k=1, k=20, k=100): ($50.33, $46.08, $35.77) Item: Amprobe TIC 410A Hot...\u001b[0m\n", - "\u001b[93m201: Truth: $6.03. Errors (k=1, k=20, k=100): ($3.03, $0.78, $0.03) Item: MyCableMart 3.5mm Pl...\u001b[0m\n", - "\u001b[93m202: Truth: $29.99. Errors (k=1, k=20, k=100): ($15.01, $12.97, $16.20) Item: OtterBox + Pop Symme...\u001b[0m\n", - "\u001b[93m203: Truth: $899.00. Errors (k=1, k=20, k=100): ($100.00, $182.77, $192.07) Item: Dell XPS Desktop ( I...\u001b[0m\n", - "\u001b[93m204: Truth: $399.99. Errors (k=1, k=20, k=100): ($0.01, $174.15, $173.35) Item: Franklin Iron Works ...\u001b[0m\n", - "\u001b[93m205: Truth: $4.66. Errors (k=1, k=20, k=100): ($0.66, $6.52, $19.16) Item: Avery Legal Dividers...\u001b[0m\n", - "\u001b[93m206: Truth: $261.41. Errors (k=1, k=20, k=100): ($117.41, $93.60, $91.01) Item: Moen 8346 Commercial...\u001b[0m\n", - "\u001b[93m207: Truth: $136.97. Errors (k=1, k=20, k=100): ($4.03, $1.57, $3.64) Item: Carlisle Versa Trail...\u001b[0m\n", - "\u001b[93m208: Truth: $79.00. Errors (k=1, k=20, k=100): ($70.00, $95.33, $97.39) Item: SUNWAYFOTO 44mm Trip...\u001b[0m\n", - "\u001b[93m209: Truth: $444.99. Errors (k=1, k=20, k=100): ($144.99, $97.76, $112.55) Item: NanoBeam AC 4 Units ...\u001b[0m\n", - "\u001b[93m210: Truth: $411.94. Errors (k=1, k=20, k=100): ($88.06, $114.89, $110.64) Item: WULF 4 Front 2 Rear ...\u001b[0m\n", - "\u001b[93m211: Truth: $148.40. Errors (k=1, k=20, k=100): ($27.40, $28.75, $18.00) Item: Alera ALEVABFMC Vale...\u001b[0m\n", - "\u001b[93m212: Truth: $244.99. Errors (k=1, k=20, k=100): ($5.01, $78.49, $97.24) Item: YU-GI-OH! Ignition A...\u001b[0m\n", - "\u001b[93m213: Truth: $86.50. Errors (k=1, k=20, k=100): ($28.50, $51.46, $52.45) Item: 48 x 36 Extra-Large ...\u001b[0m\n", - "\u001b[93m214: Truth: $297.95. Errors (k=1, k=20, k=100): ($158.95, $159.79, $146.91) Item: Dell Latitude D620 R...\u001b[0m\n", - "\u001b[93m215: Truth: $399.99. Errors (k=1, k=20, k=100): ($0.99, $46.74, $48.73) Item: acer Aspire 5 Laptop...\u001b[0m\n", - "\u001b[93m216: Truth: $599.00. Errors (k=1, k=20, k=100): ($299.00, $317.34, $342.01) Item: Elk 30 by 6-Inch Viv...\u001b[0m\n", - "\u001b[93m217: Truth: $105.99. Errors (k=1, k=20, k=100): ($194.01, $42.37, $24.46) Item: Barbie Top Model Dol...\u001b[0m\n", - "\u001b[93m218: Truth: $689.00. Errors (k=1, k=20, k=100): ($189.00, $130.34, $134.86) Item: Danby Designer 20-In...\u001b[0m\n", - "\u001b[93m219: Truth: $404.99. Errors (k=1, k=20, k=100): ($95.01, $116.74, $109.73) Item: FixtureDisplays® Met...\u001b[0m\n", - "\u001b[93m220: Truth: $207.76. Errors (k=1, k=20, k=100): ($15.76, $17.54, $0.57) Item: ACDelco GM Original ...\u001b[0m\n", - "\u001b[93m221: Truth: $171.82. Errors (k=1, k=20, k=100): ($30.82, $15.15, $5.86) Item: EBC Premium Street B...\u001b[0m\n", - "\u001b[93m222: Truth: $293.24. Errors (k=1, k=20, k=100): ($6.76, $22.32, $15.65) Item: FXR Men's Boost FX J...\u001b[0m\n", - "\u001b[93m223: Truth: $374.95. Errors (k=1, k=20, k=100): ($25.05, $39.60, $60.36) Item: SuperATV Scratch Res...\u001b[0m\n", - "\u001b[93m224: Truth: $111.99. Errors (k=1, k=20, k=100): ($27.99, $12.01, $13.37) Item: SBU 3 Layer All Weat...\u001b[0m\n", - "\u001b[93m225: Truth: $42.99. Errors (k=1, k=20, k=100): ($6.99, $2.35, $8.75) Item: 2 Pack Outdoor Broch...\u001b[0m\n", - "\u001b[93m226: Truth: $116.71. Errors (k=1, k=20, k=100): ($24.29, $21.56, $19.41) Item: Monroe Shocks & Stru...\u001b[0m\n", - "\u001b[93m227: Truth: $118.61. Errors (k=1, k=20, k=100): ($25.39, $43.88, $48.05) Item: Elements of Design M...\u001b[0m\n", - "\u001b[93m228: Truth: $147.12. Errors (k=1, k=20, k=100): ($24.12, $20.59, $15.90) Item: GM Genuine Parts Air...\u001b[0m\n", - "\u001b[93m229: Truth: $119.99. Errors (k=1, k=20, k=100): ($10.01, $38.84, $28.18) Item: Baseus USB C Docking...\u001b[0m\n", - "\u001b[93m230: Truth: $369.98. Errors (k=1, k=20, k=100): ($69.98, $41.61, $25.95) Item: Whitehall™ Personali...\u001b[0m\n", - "\u001b[93m231: Truth: $315.55. Errors (k=1, k=20, k=100): ($65.55, $75.42, $88.26) Item: Pro Circuit Works Pi...\u001b[0m\n", - "\u001b[93m232: Truth: $190.99. Errors (k=1, k=20, k=100): ($109.01, $70.62, $75.89) Item: HYANKA 15 1200W Prof...\u001b[0m\n", - "\u001b[93m233: Truth: $155.00. Errors (k=1, k=20, k=100): ($144.00, $86.94, $82.73) Item: Bluetooth X6BT Card ...\u001b[0m\n", - "\u001b[93m234: Truth: $349.99. Errors (k=1, k=20, k=100): ($49.99, $19.31, $20.42) Item: AIRAID Cold Air Inta...\u001b[0m\n", - "\u001b[93m235: Truth: $249.99. Errors (k=1, k=20, k=100): ($0.01, $29.00, $35.02) Item: Bostingner Shower Fa...\u001b[0m\n", - "\u001b[93m236: Truth: $42.99. Errors (k=1, k=20, k=100): ($3.01, $2.81, $9.71) Item: PIT66 Front Bumper T...\u001b[0m\n", - "\u001b[93m237: Truth: $17.99. Errors (k=1, k=20, k=100): ($2.01, $2.03, $3.38) Item: Caseology Bumpy Comp...\u001b[0m\n", - "\u001b[93m238: Truth: $425.00. Errors (k=1, k=20, k=100): ($25.00, $20.35, $10.61) Item: Fleck 2510 Timer Mec...\u001b[0m\n", - "\u001b[93m239: Truth: $249.99. Errors (k=1, k=20, k=100): ($0.01, $2.44, $0.73) Item: Haloview MC7108 Wire...\u001b[0m\n", - "\u001b[93m240: Truth: $138.23. Errors (k=1, k=20, k=100): ($77.23, $78.48, $66.13) Item: Schmidt Spiele - Man...\u001b[0m\n", - "\u001b[93m241: Truth: $414.99. Errors (k=1, k=20, k=100): ($114.99, $97.73, $106.89) Item: Corsa 14333 Tip Kit ...\u001b[0m\n", - "\u001b[93m242: Truth: $168.28. Errors (k=1, k=20, k=100): ($11.28, $6.72, $3.01) Item: Hoshizaki FM116A Fan...\u001b[0m\n", - "\u001b[93m243: Truth: $199.99. Errors (k=1, k=20, k=100): ($99.01, $23.29, $22.49) Item: BAINUO Antler Chande...\u001b[0m\n", - "\u001b[93m244: Truth: $126.70. Errors (k=1, k=20, k=100): ($4.30, $1.45, $2.97) Item: DNA MOTORING Smoke L...\u001b[0m\n", - "\u001b[93m245: Truth: $5.91. Errors (k=1, k=20, k=100): ($1.91, $1.28, $4.17) Item: Wera Stainless 3840/...\u001b[0m\n", - "\u001b[93m246: Truth: $193.06. Errors (k=1, k=20, k=100): ($56.94, $68.25, $65.88) Item: Celestron - PowerSee...\u001b[0m\n", - "\u001b[93m247: Truth: $249.99. Errors (k=1, k=20, k=100): ($0.01, $5.61, $7.57) Item: NHOPEEW Android Car ...\u001b[0m\n", - "\u001b[93m248: Truth: $64.12. Errors (k=1, k=20, k=100): ($27.88, $42.30, $45.82) Item: Other Harmonica A)\n", - "F...\u001b[0m\n", - "\u001b[93m249: Truth: $114.99. Errors (k=1, k=20, k=100): ($145.01, $145.33, $137.70) Item: Harley Air Filter Ve...\u001b[0m\n", - "\u001b[93m250: Truth: $926.00. Errors (k=1, k=20, k=100): ($526.00, $557.60, $547.04) Item: Elite Screens Edge F...\u001b[0m\n", - "\n", - "========================================\n", - "--- Optimal k Analysis Report ---\n", - "Model: model-2025-10-23_23.41.24:v22\n", - "Inferences Run: 250\n", - "Analyzed k from 1 to 100\n", - "===================================\n", - "==> Best k: 99\n", - "==> Minimum Average Error: $51.44\n", - "========================================\n", - "\n", - "Saved k_vs_error_plot.png\n", - "\n", - "========================================\n", - "--- Probability Spread Analysis ---\n", - "Lowest spread (std): 0.000323 (Inference 48)\n", - "Median spread (std): 0.004324 (Inference 59)\n", - "Highest spread (std): 0.102938 (Inference 91)\n", - "========================================\n", - "\n", - "Saved spread_examples_plot.png\n", - "Saved all_std_devs_plot.png\n" - ] - } - ] - } - ] -} \ No newline at end of file diff --git a/Week_7_Excersise_fine_tuned_model.ipynb b/week7/community_contributions/dkisselev-zz/Week_7_Excersise_fine_tuned_model.ipynb similarity index 96% rename from Week_7_Excersise_fine_tuned_model.ipynb rename to week7/community_contributions/dkisselev-zz/Week_7_Excersise_fine_tuned_model.ipynb index 29b861e..2090543 100644 --- a/Week_7_Excersise_fine_tuned_model.ipynb +++ b/week7/community_contributions/dkisselev-zz/Week_7_Excersise_fine_tuned_model.ipynb @@ -1,27 +1,10 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "gpuType": "T4", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "accelerator": "GPU" - }, "cells": [ { "cell_type": "markdown", "metadata": { - "id": "view-in-github", - "colab_type": "text" + "colab_type": "text", + "id": "view-in-github" }, "source": [ "\"Open" @@ -29,65 +12,67 @@ }, { "cell_type": "markdown", + "metadata": { + "id": "GHsssBgWM_l0" + }, "source": [ "# Predict Product Prices\n", "\n", "Model evaluation and inference tuning\n", "\n" - ], - "metadata": { - "id": "GHsssBgWM_l0" - } + ] }, { "cell_type": "markdown", - "source": [ - "## Libraries and configuration" - ], "metadata": { "id": "HnwMdAP3IHad" - } + }, + "source": [ + "## Libraries and configuration" + ] }, { "cell_type": "code", - "source": [ - "!pip install -q --upgrade torch==2.5.1+cu124 torchvision==0.20.1+cu124 torchaudio==2.5.1+cu124 --index-url https://download.pytorch.org/whl/cu124\n", - "!pip install -q --upgrade requests==2.32.3 bitsandbytes==0.46.0 transformers==4.48.3 accelerate==1.3.0 datasets==3.2.0 peft==0.14.0 trl==0.14.0 matplotlib wandb" - ], + "execution_count": null, "metadata": { "id": "MDyR63OTNUJ6" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "!pip install -q --upgrade torch==2.5.1+cu124 torchvision==0.20.1+cu124 torchaudio==2.5.1+cu124 --index-url https://download.pytorch.org/whl/cu124\n", + "!pip install -q --upgrade requests==2.32.3 bitsandbytes==0.46.0 transformers==4.48.3 accelerate==1.3.0 datasets==3.2.0 peft==0.14.0 trl==0.14.0 matplotlib wandb" + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-yikV8pRBer9" + }, + "outputs": [], "source": [ "import os\n", "import re\n", "import math\n", "import numpy as np\n", - "from tqdm import tqdm\n", "from google.colab import userdata\n", "from huggingface_hub import login\n", "import wandb\n", "import torch\n", "import torch.nn.functional as F\n", - "import transformers\n", "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, set_seed\n", - "from datasets import load_dataset, Dataset, DatasetDict\n", - "from datetime import datetime\n", + "from datasets import load_dataset\n", "from peft import PeftModel\n", "import matplotlib.pyplot as plt" - ], - "metadata": { - "id": "-yikV8pRBer9" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uuTX-xonNeOK" + }, + "outputs": [], "source": [ "# Models\n", "\n", @@ -140,61 +125,61 @@ "BLUE = \"\\033[94m\"\n", "RESET = \"\\033[0m\"\n", "COLOR_MAP = {\"red\":RED, \"orange\": BLUE, \"green\": GREEN}" - ], - "metadata": { - "id": "uuTX-xonNeOK" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "markdown", + "metadata": { + "id": "8JArT3QAQAjx" + }, "source": [ "### Load Data\n", "\n", "Data is loaded from Huggin Face\n" - ], - "metadata": { - "id": "8JArT3QAQAjx" - } + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WyFPZeMcM88v" + }, + "outputs": [], "source": [ "# Log in to HuggingFace\n", "hf_token = userdata.get('HF_TOKEN')\n", "login(hf_token)" - ], - "metadata": { - "id": "WyFPZeMcM88v" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cvXVoJH8LS6u" + }, + "outputs": [], "source": [ "dataset = load_dataset(DATASET_NAME)\n", "train = dataset['train']\n", "test = dataset['test']" - ], - "metadata": { - "id": "cvXVoJH8LS6u" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "markdown", - "source": [ - "## Load Tokenizer and Model" - ], "metadata": { "id": "qJWQ0a3wZ0Bw" - } + }, + "source": [ + "## Load Tokenizer and Model" + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "lAUAAcEC6ido" + }, + "outputs": [], "source": [ "# 4 or 8 but quantization\n", "if QUANT_4_BIT:\n", @@ -208,29 +193,29 @@ " quant_config = BitsAndBytesConfig(\n", " load_in_8bit=True\n", " )" - ], - "metadata": { - "id": "lAUAAcEC6ido" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OQy4pCk-dutf" + }, + "outputs": [], "source": [ "# Load model from w&b\n", "if ARTIFCAT_LOCATTION==\"WB\":\n", " artifact = wandb.Api().artifact(WANDB_ARTIFACT_PATH, type='model')\n", " artifact_dir = artifact.download() # Downloads to a local cache dir" - ], - "metadata": { - "id": "OQy4pCk-dutf" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "R_O04fKxMMT-" + }, + "outputs": [], "source": [ "# Load the Tokenizer and the Model\n", "\n", @@ -256,24 +241,24 @@ " fine_tuned_model = PeftModel.from_pretrained(base_model, artifact_dir)\n", "\n", "print(f\"Memory footprint: {fine_tuned_model.get_memory_footprint() / 1e6:.1f} MB\")" - ], - "metadata": { - "id": "R_O04fKxMMT-" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "markdown", - "source": [ - "## Hyperparameter helpers" - ], "metadata": { "id": "UObo1-RqaNnT" - } + }, + "source": [ + "## Hyperparameter helpers" + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "n4u27kbwlekE" + }, + "outputs": [], "source": [ "def calculate_weighted_price(prices, probabilities):\n", " \"\"\"\n", @@ -303,15 +288,15 @@ " weighted_price = np.average(prices_array, weights=probs_array)\n", "\n", " return weighted_price" - ], - "metadata": { - "id": "n4u27kbwlekE" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ROjIbGuH0FWS" + }, + "outputs": [], "source": [ "def get_top_k_predictions(prompt, device=\"cuda\"):\n", " \"\"\"\n", @@ -351,15 +336,15 @@ " return [], []\n", "\n", " return prices, probabilities" - ], - "metadata": { - "id": "ROjIbGuH0FWS" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "tnmTAiEG32xK" + }, + "outputs": [], "source": [ "def make_prompt(text):\n", " if ARTIFCAT_LOCATTION==\"HF\":\n", @@ -373,15 +358,15 @@ " # prompt = p_array[0] + \"\\n\\n\\n\" + p_title + \"\\n\\n\" + p_descr + \"\\n\\n\" + p_price\n", " # return text\n", " return prompt" - ], - "metadata": { - "id": "tnmTAiEG32xK" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "VNAEw5Eg4ABk" + }, + "outputs": [], "source": [ "%matplotlib inline\n", "\n", @@ -457,15 +442,15 @@ " @classmethod\n", " def test(cls, function, data):\n", " cls(function, data).run()" - ], - "metadata": { - "id": "VNAEw5Eg4ABk" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dbWS1DPV4TPQ" + }, + "outputs": [], "source": [ "class Search_K:\n", " \"\"\"\n", @@ -710,28 +695,28 @@ " @classmethod\n", " def test(cls, function, data):\n", " cls(function, data).run()" - ], - "metadata": { - "id": "dbWS1DPV4TPQ" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Vtt13OuVE-t7" + }, + "outputs": [], "source": [ "# Search best K\n", "search_k = Search_K(get_top_k_predictions, test, title=f\"{MODEL_ARTIFACT_NAME}:{REVISION_TAG}\" if ARTIFCAT_LOCATTION==\"WB\" else None)\n", "best_k = search_k.run()" - ], - "metadata": { - "id": "Vtt13OuVE-t7" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "tuwYu1NYljIv" + }, + "outputs": [], "source": [ "top_K = best_k\n", "\n", @@ -785,39 +770,51 @@ " final_price = np.average(prices_np, weights=probs_np)\n", "\n", " return float(final_price) # Return as a standard python float" - ], - "metadata": { - "id": "tuwYu1NYljIv" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3SxpLBJH70E-" + }, + "outputs": [], "source": [ "prompt=make_prompt(test[80]['text'])\n", "print(prompt)\n", "\n", "improved_model_predict(prompt)" - ], - "metadata": { - "id": "3SxpLBJH70E-" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "W_KcLvyt6kbb" + }, + "outputs": [], "source": [ "# Run Estimate vs Ground Truth\n", "tester = Tester(improved_model_predict, test, title=f\"{MODEL_ARTIFACT_NAME}:{REVISION_TAG}\" if ARTIFCAT_LOCATTION==\"WB\" else None)\n", "tester.run()" - ], - "metadata": { - "id": "W_KcLvyt6kbb" - }, - "execution_count": null, - "outputs": [] + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "include_colab_link": true, + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}