02. NLP with Transformers/08. Transformers in Production/Transformers&Production.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UbY7GorFZD5-"
      },
      "source": [
        "### **Initialization**\n",
        "- I use these three lines of code on top of my each notebooks because it will help to prevent any problems while reloading the same project. And the third line of code helps to make visualization within the notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "w7ImxoBvYKYi"
      },
      "outputs": [],
      "source": [
        "#@ INITIALIZATION: \n",
        "%reload_ext autoreload\n",
        "%autoreload 2\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W1Tms8T-ZLtU"
      },
      "source": [
        "**Downloading Libraries and Dependencies**\n",
        "- I have downloaded all the libraries and dependencies required for the project in one particular cell."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "AdwWU82rZHcu"
      },
      "outputs": [],
      "source": [
        "#@ IMPORTING MODULES: UNCOMMENT BELOW:\n",
        "# !pip install transformers\n",
        "# !pip install datasets\n",
        "# !pip install optuna\n",
        "# !pip install onnx\n",
        "# !pip install onnxruntime\n",
        "import os\n",
        "import optuna\n",
        "import torch \n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import torch.nn as nn \n",
        "import torch.nn.functional as F\n",
        "from psutil import cpu_count\n",
        "from torch.nn.quantized import QFunctional\n",
        "from torch import quantize_per_tensor\n",
        "from torch.quantization import quantize_dynamic\n",
        "from time import perf_counter\n",
        "from pathlib import Path\n",
        "from scipy.special import softmax\n",
        "from datasets import load_dataset\n",
        "from datasets import load_metric\n",
        "from transformers import Trainer\n",
        "from transformers import pipeline\n",
        "from transformers import TrainingArguments\n",
        "from transformers import AutoTokenizer\n",
        "from transformers import AutoConfig\n",
        "from transformers.convert_graph_to_onnx import convert\n",
        "from transformers import AutoModelForSequenceClassification\n",
        "from onnxruntime import GraphOptimizationLevel\n",
        "from onnxruntime import InferenceSession\n",
        "from onnxruntime import SessionOptions\n",
        "from onnxruntime.quantization import quantize_dynamic, QuantType\n",
        "\n",
        "#@ IGNORING WARNINGS: \n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oP94CoI5ZZET"
      },
      "source": [
        "### **Case Study: Intent Detection**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OuobuopNZp2T"
      },
      "source": [
        "**The Dataset**\n",
        "- We will use CLINC150 dataset. This dataset includes 22,500 inscope queries across 150 intents and 10 domains like banking and travel, and also includes 1,200 out of scope queries. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
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        },
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        "outputId": "7b0af97a-92ec-4666-d7b6-0899061257d8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Reusing dataset clinc_oos (/root/.cache/huggingface/datasets/clinc_oos/plus/1.0.0/abcc41d382f8137f039adc747af44714941e8196e845dfbdd8ae7a7e020e6ba1)\n"
          ]
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          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
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              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'text': 'how would i say i need directions if i were french', 'intent': 61}\n",
            "translate\n"
          ]
        }
      ],
      "source": [
        "#@ LOADING CLINC150 DATASET:\n",
        "clinc = load_dataset(\"clinc_oos\", \"plus\")                      # Loading dataset.\n",
        "sample = clinc[\"test\"][23]                                     # Initializing sample.\n",
        "print(sample)                                                  # Inspection.\n",
        "intents = clinc[\"test\"].features[\"intent\"]                     # Getting intent attribute.\n",
        "print(intents.int2str(sample[\"intent\"]))                       # Inspection."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "B4-wJ5QsZi4k"
      },
      "outputs": [],
      "source": [
        "#@ INITIALIZING FINE-TUNED BERT BASE MODEL: \n",
        "bert_ckpt = \"transformersbook/bert-base-uncased-finetuned-clinc\"                    # Initializing model checkpoint.\n",
        "pipe = pipeline(\"text-classification\", model=bert_ckpt)                             # Initializing pretrained bert pipeline. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4cK8IqRpbCMX",
        "outputId": "ab307aec-b4d9-4655-a050-c92c53dd5733"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'car_rental', 'score': 0.5490034818649292}]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "#@ IMPLEMENTATION OF BERT:\n",
        "query = \"\"\"Hey, I'd like to rent a vehicle from Nov 1st to Nov 15th in\n",
        "Paris and I need a 15 passenger van\"\"\"                                              # Initializing example query.\n",
        "pipe(query)                                                                         # Implementation of bert model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DDjge7pYcvuz"
      },
      "source": [
        "**Performance Benchmark**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "kY1wtnTHbcyG"
      },
      "outputs": [],
      "source": [
        "#@ SKELETON OF PERFORMANCE BENCHMARK:\n",
        "class PerformanceBenchmark:                                            \n",
        "    def __init__(self, pipeline, dataset, optim_type=\"BERT baseline\"):\n",
        "        self.pipeline = pipeline\n",
        "        self.dataset = dataset\n",
        "        self.optim_type = optim_type\n",
        "    \n",
        "    def compute_accuracy(self):\n",
        "        pass\n",
        "    \n",
        "    def compute_size(self):\n",
        "        pass\n",
        "    \n",
        "    def time_pipeline(self):\n",
        "        pass\n",
        "    \n",
        "    def run_benchmark(self):\n",
        "        metrics = {}\n",
        "        metrics[self.optim_type] = self.compute_size()\n",
        "        metrics[self.optim_type].update(self.time_pipeline())\n",
        "        metrics[self.optim_type].update(self.compute_accuracy())\n",
        "        return metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "ma8fd7Bqge5C"
      },
      "outputs": [],
      "source": [
        "#@ FUNCTION FOR COMPUTING ACCURACY:\n",
        "accuracy_score = load_metric(\"accuracy\")                            # Initializing accuracy metric.\n",
        "def compute_accuracy(self):                                         # Defining compute accuracy function.\n",
        "    preds, labels = [], []                                          # Initialization.\n",
        "    for example in self.dataset:\n",
        "        pred = self.pipeline(example[\"text\"])[0][\"label\"]           # Initializing predictions.\n",
        "        label = example[\"intent\"]                                   # Initializing true label.\n",
        "        preds.append(intents.str2int(pred))                         # Adding to predictions.\n",
        "        labels.append(label)                                        # Adding to labels.\n",
        "    accuracy = accuracy_score.compute(predictions=preds, \n",
        "                                      references=labels)            # Getting accuracy score.\n",
        "    print(f\"Accuracy on test set - {accuracy['accuracy']:.3f}\")     # Inspecting accuracy.\n",
        "    return accuracy\n",
        "\n",
        "#@ ADDING TO PERFORMANCE BENCHMARK:\n",
        "PerformanceBenchmark.compute_accuracy = compute_accuracy            # Adding accuracy score."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9kQ0_xdUzxE-"
      },
      "outputs": [],
      "source": [
        "#@ INSPECTING SIZE OF MODEL: UNCOMMENT BELOW: \n",
        "# list(pipe.model.state_dict().items())[23]                           "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "G8YiMsGS0MvE"
      },
      "outputs": [],
      "source": [
        "#@ FUNCTION FOR COMPUTING MODEL SIZE:\n",
        "def compute_size(self):                                         # Defining function.\n",
        "    state_dict = self.pipeline.model.state_dict()               # Initializing model dictionary.\n",
        "    tmp_path = Path(\"model.pt\")                                 # Initializing model path.\n",
        "    torch.save(state_dict, tmp_path)                            # Saving the model.\n",
        "    size_mb = Path(tmp_path).stat().st_size / (1024 * 1024)     # Getting size in megabytes. \n",
        "    tmp_path.unlink()                                           # Deleting temporary file.\n",
        "    print(f\"Model size (MB) - {size_mb:.2f}\")                   # Inspecting size.\n",
        "    return {\"size_mb\": size_mb}\n",
        "\n",
        "#@ ADDING TO PERFORMANCE BENCHMARK:\n",
        "PerformanceBenchmark.compute_size = compute_size                # Adding to performance benchmark."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WZcwFGmY3oBf",
        "outputId": "cc7ec2b9-4b84-4f8c-b515-efd8248583c3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Latency (ms) - 128.266\n",
            "Latency (ms) - 145.453\n",
            "Latency (ms) - 136.828\n"
          ]
        }
      ],
      "source": [
        "#@ CALCULATING TIME OF PIPELINE: \n",
        "for _ in range(3):\n",
        "    start_time = perf_counter()                                 # Starting time.\n",
        "    _ = pipe(query)                                             # Running pipeline.\n",
        "    latency = perf_counter() - start_time                       # Calculating latency.\n",
        "    print(f\"Latency (ms) - {1000 * latency:.3f}\")               # Inspection."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "seeaQ7fn-bab"
      },
      "outputs": [],
      "source": [
        "#@ FUNCTION TO CALCULATE TIME OF PIPELINE:\n",
        "def time_pipeline(self, query=\"What is the pin number for my account?\"):    # Defining function.\n",
        "    latencies = []                                                          # Initialization.\n",
        "    for _ in range(10):\n",
        "        _ = self.pipeline(query)                                            # Implementation of pipeline. \n",
        "    for _ in range(100):\n",
        "        start_time = perf_counter()                                         # Starting time.\n",
        "        _ = self.pipeline(query)                                            # Running pipeline.\n",
        "        latency = perf_counter() - start_time                               # Calculating latency.\n",
        "        latencies.append(latency)                                           # Adding latencies.\n",
        "    time_avg_ms = 1000 * np.mean(latencies)                                 # Time average.\n",
        "    time_std_ms = 1000 * np.std(latencies)                                  # Time standard deviation. \n",
        "    print(f\"Average latency (ms) - {time_avg_ms:.2f} +\\- {time_std_ms:.2f}\")\n",
        "    return {\"time_avg_ms\": time_avg_ms, \"time_std_ms\":time_std_ms}      \n",
        "\n",
        "#@ ADDING TO PERFORMANCE BENCHMARK:\n",
        "PerformanceBenchmark.time_pipeline = time_pipeline                          # Adding to performance benchmark. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iQLixsAtCpi7",
        "outputId": "4b8e8042-2060-4c78-b754-b3daed17381b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model size (MB) - 418.16\n",
            "Average latency (ms) - 110.96 +\\- 49.53\n",
            "Accuracy on test set - 0.867\n"
          ]
        }
      ],
      "source": [
        "#@ INSPECTING PERFORMANCE BENCHMARK:\n",
        "pb = PerformanceBenchmark(pipe, clinc[\"test\"])                              # Initializing performance benchmark class.\n",
        "perf_metrics = pb.run_benchmark()                                           # Getting metrics. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cjo1ecM0KRp3"
      },
      "source": [
        "### **Knowledge Distillation**\n",
        "- Knowledge distillation is a general purpose method for training a smaller `student` model to mimic the behavior of a slower, larger, but better performing `teacher` model. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "KuIWpAFADTyB"
      },
      "outputs": [],
      "source": [
        "#@ INITIALIZING DISTILLATION TRAINING ARGUMENTS: \n",
        "class DistillationTrainingArguments(TrainingArguments):                         # Defining class. \n",
        "    def __init__(self, *args, alpha=0.5, temperature=2.0, **kwargs):            # Initializing constructor function. \n",
        "        super().__init__(*args, **kwargs)\n",
        "        self.alpha = alpha                                                      # Initializing hyperparameters.\n",
        "        self.temperature = temperature                                          # Initializing hyperparameters."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "S0n6UrVuRu8q"
      },
      "outputs": [],
      "source": [
        "#@ INITIALIZING DISTILLATION TRAINER:\n",
        "class DistillationTrainer(Trainer):                                             # Defining distillation trainer.\n",
        "    def __init__(self, *args, teacher_model=None, **kwargs):                    # Initializing constructor function.\n",
        "        super().__init__(*args, **kwargs)\n",
        "        self.teacher_model = teacher_model                                      # Initialization.\n",
        "    \n",
        "    def compute_loss(self, model, inputs, return_outputs=False):                # Defining compute loss function. \n",
        "        outputs_stu = model(**inputs)                                           # Implementation of model.\n",
        "        loss_ce = outputs_stu.loss                                              # Getting cross entropy loss.\n",
        "        logits_stu = outputs_stu.logits                                         # Getting logits.\n",
        "        with torch.no_grad():\n",
        "            outputs_tea = self.teacher_model(**inputs)                          # Implementation of model.\n",
        "            logits_tea = outputs_tea.logits                                     # Getting logits.\n",
        "        loss_fct = nn.KLDivLoss(reduction=\"batchmean\")                          # Computing KL divergence. \n",
        "        loss_kd = self.args.temperature ** 2 * loss_fct(\n",
        "            F.log_softmax(logits_stu / self.args.temperature, dim=-1),          # Normalizing with log softmax.\n",
        "            F.softmax(logits_tea / self.args.temperature, dim=-1))              # Normalizing with softmax.\n",
        "        loss = self.args.alpha * loss_ce + (1. - self.args.alpha) * loss_kd     # Weighted student loss.\n",
        "        return (loss, outputs_stu) if return_outputs else loss"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z-a61OO7X6by"
      },
      "source": [
        "**Note:**\n",
        "- The **KL divergence** expects the inputs in the form of log probabilities and labels as normal probabilities. So, we have used log softmax to normalize the student's logits while teacher's logits are converted to probabilities with a standard softmax."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lEZ2tM0fZH_M"
      },
      "source": [
        "**Good Student Initialization**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "outputId": "c4dd9bae-4313-4c6d-ff06-94cc1336b33c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loading cached processed dataset at /root/.cache/huggingface/datasets/clinc_oos/plus/1.0.0/abcc41d382f8137f039adc747af44714941e8196e845dfbdd8ae7a7e020e6ba1/cache-5b3a8a0d3de51067.arrow\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fd949b338d054df5a477636e2b1562fd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loading cached processed dataset at /root/.cache/huggingface/datasets/clinc_oos/plus/1.0.0/abcc41d382f8137f039adc747af44714941e8196e845dfbdd8ae7a7e020e6ba1/cache-f100bf1f0366ba32.arrow\n"
          ]
        }
      ],
      "source": [
        "#@ INITIALIZING GOOD PRETRAINED STUDENT MODEL:\n",
        "student_ckpt = \"distilbert-base-uncased\"                                    # Initializing student checkpoint. \n",
        "student_tokenizer = AutoTokenizer.from_pretrained(student_ckpt)             # Initializing pretrained tokenizer.\n",
        "\n",
        "#@ FUNCTION FOR TOKENIZATION:\n",
        "def tokenize_text(batch):                                                   # Defining function. \n",
        "    return student_tokenizer(batch[\"text\"], truncation=True)                # Implementation of tokenizer. \n",
        "clinc_enc = clinc.map(tokenize_text, batched=True, remove_columns=[\"text\"]) # Tokenizing whole dataset.\n",
        "clinc_enc = clinc_enc.rename_column(\"intent\", \"labels\")                     # Changing column name."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "C1Q3DgBybDZp"
      },
      "outputs": [],
      "source": [
        "#@ FUNCTION FOR COMPUTING METRICS:\n",
        "def compute_metrics(pred):                                  # Function for computing metrics.\n",
        "    predictions, labels = pred                              # Initialization.\n",
        "    predictions = np.argmax(predictions, axis=1)            # Initializing predictions.\n",
        "    return accuracy_score.compute(predictions=predictions,\n",
        "                                  references=labels)        # Computing accuracy score."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "BxWH_9sBcxv2"
      },
      "outputs": [],
      "source": [
        "#@ INITIALIZING DISTILLATION TRAINING ARGUMENTS:\n",
        "batch_size = 48                                             # Initializing batch size.\n",
        "finetuned_ckpt = \"distilbert-base-uncased-finetuned-clinc\"  # Initializing model checkpoint.\n",
        "student_training_args = DistillationTrainingArguments(\n",
        "    output_dir=finetuned_ckpt, evaluation_strategy=\"epoch\",\n",
        "    num_train_epochs=5, learning_rate=2e-5, \n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size, \n",
        "    alpha=1, weight_decay=0.01, \n",
        "    push_to_hub=False\n",
        ")                                                           # Initializing training arguments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "rm1cejNJrd-c"
      },
      "outputs": [],
      "source": [
        "#@ INITIALIZING AUTO MODEL CONFIGURATIONS:\n",
        "id2label = pipe.model.config.id2label                           # Initializing mappings.\n",
        "label2id = pipe.model.config.label2id                           # Initializing mappings.\n",
        "num_labels = intents.num_classes                                # Initializing labels.\n",
        "student_config = (AutoConfig.from_pretrained(\n",
        "    student_ckpt, num_labels=num_labels, id2label=id2label,\n",
        "    label2id=label2id))                                         # Initializing student model configurations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "cwvWfCuRtZql"
      },
      "outputs": [],
      "source": [
        "#@ FUNCTION FOR MODEL INITIALIZATION:\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")       # Initializing gpu.\n",
        "def student_init():                                                         # Defining function.\n",
        "    return (AutoModelForSequenceClassification.from_pretrained(\n",
        "        student_ckpt, config=student_config).to(device))                    # Function for student model initialization."
      ]
    },
    {
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      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "https://huggingface.co/distilbert-base-uncased/resolve/main/pytorch_model.bin not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpo07aj7bc\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "Downloading:   0%|          | 0.00/256M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "storing https://huggingface.co/distilbert-base-uncased/resolve/main/pytorch_model.bin in cache at /root/.cache/huggingface/transformers/9c169103d7e5a73936dd2b627e42851bec0831212b677c637033ee4bce9ab5ee.126183e36667471617ae2f0835fab707baa54b731f991507ebbb55ea85adb12a\n",
            "creating metadata file for /root/.cache/huggingface/transformers/9c169103d7e5a73936dd2b627e42851bec0831212b677c637033ee4bce9ab5ee.126183e36667471617ae2f0835fab707baa54b731f991507ebbb55ea85adb12a\n",
            "loading weights file https://huggingface.co/distilbert-base-uncased/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/9c169103d7e5a73936dd2b627e42851bec0831212b677c637033ee4bce9ab5ee.126183e36667471617ae2f0835fab707baa54b731f991507ebbb55ea85adb12a\n",
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_projector.weight', 'vocab_projector.bias', 'vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.bias', 'classifier.weight', 'pre_classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "loading weights file https://huggingface.co/distilbert-base-uncased/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/9c169103d7e5a73936dd2b627e42851bec0831212b677c637033ee4bce9ab5ee.126183e36667471617ae2f0835fab707baa54b731f991507ebbb55ea85adb12a\n",
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_projector.weight', 'vocab_projector.bias', 'vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.bias', 'classifier.weight', 'pre_classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "***** Running training *****\n",
            "  Num examples = 15250\n",
            "  Num Epochs = 5\n",
            "  Instantaneous batch size per device = 48\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 48\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 1590\n"
          ]
        },
        {
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1590' max='1590' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1590/1590 05:01, Epoch 5/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>3.288972</td>\n",
              "      <td>0.742903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>3.786800</td>\n",
              "      <td>1.875608</td>\n",
              "      <td>0.837419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>3.786800</td>\n",
              "      <td>1.157074</td>\n",
              "      <td>0.896129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.692900</td>\n",
              "      <td>0.857376</td>\n",
              "      <td>0.913226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.905700</td>\n",
              "      <td>0.772111</td>\n",
              "      <td>0.918387</td>\n",
              "    </tr>\n",
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          "output_type": "stream",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "Saving model checkpoint to distilbert-base-uncased-finetuned-clinc/checkpoint-500\n",
            "Configuration saved in distilbert-base-uncased-finetuned-clinc/checkpoint-500/config.json\n",
            "Model weights saved in distilbert-base-uncased-finetuned-clinc/checkpoint-500/pytorch_model.bin\n",
            "tokenizer config file saved in distilbert-base-uncased-finetuned-clinc/checkpoint-500/tokenizer_config.json\n",
            "Special tokens file saved in distilbert-base-uncased-finetuned-clinc/checkpoint-500/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "Saving model checkpoint to distilbert-base-uncased-finetuned-clinc/checkpoint-1000\n",
            "Configuration saved in distilbert-base-uncased-finetuned-clinc/checkpoint-1000/config.json\n",
            "Model weights saved in distilbert-base-uncased-finetuned-clinc/checkpoint-1000/pytorch_model.bin\n",
            "tokenizer config file saved in distilbert-base-uncased-finetuned-clinc/checkpoint-1000/tokenizer_config.json\n",
            "Special tokens file saved in distilbert-base-uncased-finetuned-clinc/checkpoint-1000/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "Saving model checkpoint to distilbert-base-uncased-finetuned-clinc/checkpoint-1500\n",
            "Configuration saved in distilbert-base-uncased-finetuned-clinc/checkpoint-1500/config.json\n",
            "Model weights saved in distilbert-base-uncased-finetuned-clinc/checkpoint-1500/pytorch_model.bin\n",
            "tokenizer config file saved in distilbert-base-uncased-finetuned-clinc/checkpoint-1500/tokenizer_config.json\n",
            "Special tokens file saved in distilbert-base-uncased-finetuned-clinc/checkpoint-1500/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1590, training_loss=2.0514166298152516, metrics={'train_runtime': 301.783, 'train_samples_per_second': 252.665, 'train_steps_per_second': 5.269, 'total_flos': 413896353421488.0, 'train_loss': 2.0514166298152516, 'epoch': 5.0})"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#@ TRAINING DISTILLATION TRAINER:\n",
        "teacher_ckpt = \"transformersbook/bert-base-uncased-finetuned-clinc\"         # Initializing teacher model checkpoint.\n",
        "teacher_model = (AutoModelForSequenceClassification.from_pretrained(\n",
        "    teacher_ckpt, num_labels=num_labels).to(device))                        # Initializing teacher model.\n",
        "\n",
        "#@ TRAINING DISTILLATION TRAINER:\n",
        "distilbert_trainer = DistillationTrainer(\n",
        "    model_init=student_init, teacher_model=teacher_model, \n",
        "    args=student_training_args, train_dataset=clinc_enc[\"train\"], \n",
        "    eval_dataset=clinc_enc[\"validation\"], \n",
        "    compute_metrics=compute_metrics, tokenizer=student_tokenizer)           # Initializing distillation trainer.\n",
        "distilbert_trainer.train()                                                  # Training distillation trainer."
      ]
    },
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          "text": [
            "https://huggingface.co/transformersbook/distilbert-base-uncased-finetuned-clinc/resolve/main/config.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpjcwpqpln\n"
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        {
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          "output_type": "stream",
          "text": [
            "storing https://huggingface.co/transformersbook/distilbert-base-uncased-finetuned-clinc/resolve/main/config.json in cache at /root/.cache/huggingface/transformers/b260e9da00881ac757ff45d4b2e2fdd57c1bffe839137733f8e862e013e80562.840d34be4a5ee5f1a66a2b196122fb7d112229d259e5712e44e13ffc8ec27b68\n",
            "creating metadata file for /root/.cache/huggingface/transformers/b260e9da00881ac757ff45d4b2e2fdd57c1bffe839137733f8e862e013e80562.840d34be4a5ee5f1a66a2b196122fb7d112229d259e5712e44e13ffc8ec27b68\n",
            "loading configuration file https://huggingface.co/transformersbook/distilbert-base-uncased-finetuned-clinc/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/b260e9da00881ac757ff45d4b2e2fdd57c1bffe839137733f8e862e013e80562.840d34be4a5ee5f1a66a2b196122fb7d112229d259e5712e44e13ffc8ec27b68\n",
            "Model config DistilBertConfig {\n",
            "  \"_name_or_path\": \"transformersbook/distilbert-base-uncased-finetuned-clinc\",\n",
            "  \"activation\": \"gelu\",\n",
            "  \"architectures\": [\n",
            "    \"DistilBertForSequenceClassification\"\n",
            "  ],\n",
            "  \"attention_dropout\": 0.1,\n",
            "  \"dim\": 768,\n",
            "  \"dropout\": 0.1,\n",
            "  \"hidden_dim\": 3072,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"restaurant_reviews\",\n",
            "    \"1\": \"nutrition_info\",\n",
            "    \"2\": \"account_blocked\",\n",
            "    \"3\": \"oil_change_how\",\n",
            "    \"4\": \"time\",\n",
            "    \"5\": \"weather\",\n",
            "    \"6\": \"redeem_rewards\",\n",
            "    \"7\": \"interest_rate\",\n",
            "    \"8\": \"gas_type\",\n",
            "    \"9\": \"accept_reservations\",\n",
            "    \"10\": \"smart_home\",\n",
            "    \"11\": \"user_name\",\n",
            "    \"12\": \"report_lost_card\",\n",
            "    \"13\": \"repeat\",\n",
            "    \"14\": \"whisper_mode\",\n",
            "    \"15\": \"what_are_your_hobbies\",\n",
            "    \"16\": \"order\",\n",
            "    \"17\": \"jump_start\",\n",
            "    \"18\": \"schedule_meeting\",\n",
            "    \"19\": \"meeting_schedule\",\n",
            "    \"20\": \"freeze_account\",\n",
            "    \"21\": \"what_song\",\n",
            "    \"22\": \"meaning_of_life\",\n",
            "    \"23\": \"restaurant_reservation\",\n",
            "    \"24\": \"traffic\",\n",
            "    \"25\": \"make_call\",\n",
            "    \"26\": \"text\",\n",
            "    \"27\": \"bill_balance\",\n",
            "    \"28\": \"improve_credit_score\",\n",
            "    \"29\": \"change_language\",\n",
            "    \"30\": \"no\",\n",
            "    \"31\": \"measurement_conversion\",\n",
            "    \"32\": \"timer\",\n",
            "    \"33\": \"flip_coin\",\n",
            "    \"34\": \"do_you_have_pets\",\n",
            "    \"35\": \"balance\",\n",
            "    \"36\": \"tell_joke\",\n",
            "    \"37\": \"last_maintenance\",\n",
            "    \"38\": \"exchange_rate\",\n",
            "    \"39\": \"uber\",\n",
            "    \"40\": \"car_rental\",\n",
            "    \"41\": \"credit_limit\",\n",
            "    \"42\": \"oos\",\n",
            "    \"43\": \"shopping_list\",\n",
            "    \"44\": \"expiration_date\",\n",
            "    \"45\": \"routing\",\n",
            "    \"46\": \"meal_suggestion\",\n",
            "    \"47\": \"tire_change\",\n",
            "    \"48\": \"todo_list\",\n",
            "    \"49\": \"card_declined\",\n",
            "    \"50\": \"rewards_balance\",\n",
            "    \"51\": \"change_accent\",\n",
            "    \"52\": \"vaccines\",\n",
            "    \"53\": \"reminder_update\",\n",
            "    \"54\": \"food_last\",\n",
            "    \"55\": \"change_ai_name\",\n",
            "    \"56\": \"bill_due\",\n",
            "    \"57\": \"who_do_you_work_for\",\n",
            "    \"58\": \"share_location\",\n",
            "    \"59\": \"international_visa\",\n",
            "    \"60\": \"calendar\",\n",
            "    \"61\": \"translate\",\n",
            "    \"62\": \"carry_on\",\n",
            "    \"63\": \"book_flight\",\n",
            "    \"64\": \"insurance_change\",\n",
            "    \"65\": \"todo_list_update\",\n",
            "    \"66\": \"timezone\",\n",
            "    \"67\": \"cancel_reservation\",\n",
            "    \"68\": \"transactions\",\n",
            "    \"69\": \"credit_score\",\n",
            "    \"70\": \"report_fraud\",\n",
            "    \"71\": \"spending_history\",\n",
            "    \"72\": \"directions\",\n",
            "    \"73\": \"spelling\",\n",
            "    \"74\": \"insurance\",\n",
            "    \"75\": \"what_is_your_name\",\n",
            "    \"76\": \"reminder\",\n",
            "    \"77\": \"where_are_you_from\",\n",
            "    \"78\": \"distance\",\n",
            "    \"79\": \"payday\",\n",
            "    \"80\": \"flight_status\",\n",
            "    \"81\": \"find_phone\",\n",
            "    \"82\": \"greeting\",\n",
            "    \"83\": \"alarm\",\n",
            "    \"84\": \"order_status\",\n",
            "    \"85\": \"confirm_reservation\",\n",
            "    \"86\": \"cook_time\",\n",
            "    \"87\": \"damaged_card\",\n",
            "    \"88\": \"reset_settings\",\n",
            "    \"89\": \"pin_change\",\n",
            "    \"90\": \"replacement_card_duration\",\n",
            "    \"91\": \"new_card\",\n",
            "    \"92\": \"roll_dice\",\n",
            "    \"93\": \"income\",\n",
            "    \"94\": \"taxes\",\n",
            "    \"95\": \"date\",\n",
            "    \"96\": \"who_made_you\",\n",
            "    \"97\": \"pto_request\",\n",
            "    \"98\": \"tire_pressure\",\n",
            "    \"99\": \"how_old_are_you\",\n",
            "    \"100\": \"rollover_401k\",\n",
            "    \"101\": \"pto_request_status\",\n",
            "    \"102\": \"how_busy\",\n",
            "    \"103\": \"application_status\",\n",
            "    \"104\": \"recipe\",\n",
            "    \"105\": \"calendar_update\",\n",
            "    \"106\": \"play_music\",\n",
            "    \"107\": \"yes\",\n",
            "    \"108\": \"direct_deposit\",\n",
            "    \"109\": \"credit_limit_change\",\n",
            "    \"110\": \"gas\",\n",
            "    \"111\": \"pay_bill\",\n",
            "    \"112\": \"ingredients_list\",\n",
            "    \"113\": \"lost_luggage\",\n",
            "    \"114\": \"goodbye\",\n",
            "    \"115\": \"what_can_i_ask_you\",\n",
            "    \"116\": \"book_hotel\",\n",
            "    \"117\": \"are_you_a_bot\",\n",
            "    \"118\": \"next_song\",\n",
            "    \"119\": \"change_speed\",\n",
            "    \"120\": \"plug_type\",\n",
            "    \"121\": \"maybe\",\n",
            "    \"122\": \"w2\",\n",
            "    \"123\": \"oil_change_when\",\n",
            "    \"124\": \"thank_you\",\n",
            "    \"125\": \"shopping_list_update\",\n",
            "    \"126\": \"pto_balance\",\n",
            "    \"127\": \"order_checks\",\n",
            "    \"128\": \"travel_alert\",\n",
            "    \"129\": \"fun_fact\",\n",
            "    \"130\": \"sync_device\",\n",
            "    \"131\": \"schedule_maintenance\",\n",
            "    \"132\": \"apr\",\n",
            "    \"133\": \"transfer\",\n",
            "    \"134\": \"ingredient_substitution\",\n",
            "    \"135\": \"calories\",\n",
            "    \"136\": \"current_location\",\n",
            "    \"137\": \"international_fees\",\n",
            "    \"138\": \"calculator\",\n",
            "    \"139\": \"definition\",\n",
            "    \"140\": \"next_holiday\",\n",
            "    \"141\": \"update_playlist\",\n",
            "    \"142\": \"mpg\",\n",
            "    \"143\": \"min_payment\",\n",
            "    \"144\": \"change_user_name\",\n",
            "    \"145\": \"restaurant_suggestion\",\n",
            "    \"146\": \"travel_notification\",\n",
            "    \"147\": \"cancel\",\n",
            "    \"148\": \"pto_used\",\n",
            "    \"149\": \"travel_suggestion\",\n",
            "    \"150\": \"change_volume\"\n",
            "  },\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"label2id\": {\n",
            "    \"accept_reservations\": 9,\n",
            "    \"account_blocked\": 2,\n",
            "    \"alarm\": 83,\n",
            "    \"application_status\": 103,\n",
            "    \"apr\": 132,\n",
            "    \"are_you_a_bot\": 117,\n",
            "    \"balance\": 35,\n",
            "    \"bill_balance\": 27,\n",
            "    \"bill_due\": 56,\n",
            "    \"book_flight\": 63,\n",
            "    \"book_hotel\": 116,\n",
            "    \"calculator\": 138,\n",
            "    \"calendar\": 60,\n",
            "    \"calendar_update\": 105,\n",
            "    \"calories\": 135,\n",
            "    \"cancel\": 147,\n",
            "    \"cancel_reservation\": 67,\n",
            "    \"car_rental\": 40,\n",
            "    \"card_declined\": 49,\n",
            "    \"carry_on\": 62,\n",
            "    \"change_accent\": 51,\n",
            "    \"change_ai_name\": 55,\n",
            "    \"change_language\": 29,\n",
            "    \"change_speed\": 119,\n",
            "    \"change_user_name\": 144,\n",
            "    \"change_volume\": 150,\n",
            "    \"confirm_reservation\": 85,\n",
            "    \"cook_time\": 86,\n",
            "    \"credit_limit\": 41,\n",
            "    \"credit_limit_change\": 109,\n",
            "    \"credit_score\": 69,\n",
            "    \"current_location\": 136,\n",
            "    \"damaged_card\": 87,\n",
            "    \"date\": 95,\n",
            "    \"definition\": 139,\n",
            "    \"direct_deposit\": 108,\n",
            "    \"directions\": 72,\n",
            "    \"distance\": 78,\n",
            "    \"do_you_have_pets\": 34,\n",
            "    \"exchange_rate\": 38,\n",
            "    \"expiration_date\": 44,\n",
            "    \"find_phone\": 81,\n",
            "    \"flight_status\": 80,\n",
            "    \"flip_coin\": 33,\n",
            "    \"food_last\": 54,\n",
            "    \"freeze_account\": 20,\n",
            "    \"fun_fact\": 129,\n",
            "    \"gas\": 110,\n",
            "    \"gas_type\": 8,\n",
            "    \"goodbye\": 114,\n",
            "    \"greeting\": 82,\n",
            "    \"how_busy\": 102,\n",
            "    \"how_old_are_you\": 99,\n",
            "    \"improve_credit_score\": 28,\n",
            "    \"income\": 93,\n",
            "    \"ingredient_substitution\": 134,\n",
            "    \"ingredients_list\": 112,\n",
            "    \"insurance\": 74,\n",
            "    \"insurance_change\": 64,\n",
            "    \"interest_rate\": 7,\n",
            "    \"international_fees\": 137,\n",
            "    \"international_visa\": 59,\n",
            "    \"jump_start\": 17,\n",
            "    \"last_maintenance\": 37,\n",
            "    \"lost_luggage\": 113,\n",
            "    \"make_call\": 25,\n",
            "    \"maybe\": 121,\n",
            "    \"meal_suggestion\": 46,\n",
            "    \"meaning_of_life\": 22,\n",
            "    \"measurement_conversion\": 31,\n",
            "    \"meeting_schedule\": 19,\n",
            "    \"min_payment\": 143,\n",
            "    \"mpg\": 142,\n",
            "    \"new_card\": 91,\n",
            "    \"next_holiday\": 140,\n",
            "    \"next_song\": 118,\n",
            "    \"no\": 30,\n",
            "    \"nutrition_info\": 1,\n",
            "    \"oil_change_how\": 3,\n",
            "    \"oil_change_when\": 123,\n",
            "    \"oos\": 42,\n",
            "    \"order\": 16,\n",
            "    \"order_checks\": 127,\n",
            "    \"order_status\": 84,\n",
            "    \"pay_bill\": 111,\n",
            "    \"payday\": 79,\n",
            "    \"pin_change\": 89,\n",
            "    \"play_music\": 106,\n",
            "    \"plug_type\": 120,\n",
            "    \"pto_balance\": 126,\n",
            "    \"pto_request\": 97,\n",
            "    \"pto_request_status\": 101,\n",
            "    \"pto_used\": 148,\n",
            "    \"recipe\": 104,\n",
            "    \"redeem_rewards\": 6,\n",
            "    \"reminder\": 76,\n",
            "    \"reminder_update\": 53,\n",
            "    \"repeat\": 13,\n",
            "    \"replacement_card_duration\": 90,\n",
            "    \"report_fraud\": 70,\n",
            "    \"report_lost_card\": 12,\n",
            "    \"reset_settings\": 88,\n",
            "    \"restaurant_reservation\": 23,\n",
            "    \"restaurant_reviews\": 0,\n",
            "    \"restaurant_suggestion\": 145,\n",
            "    \"rewards_balance\": 50,\n",
            "    \"roll_dice\": 92,\n",
            "    \"rollover_401k\": 100,\n",
            "    \"routing\": 45,\n",
            "    \"schedule_maintenance\": 131,\n",
            "    \"schedule_meeting\": 18,\n",
            "    \"share_location\": 58,\n",
            "    \"shopping_list\": 43,\n",
            "    \"shopping_list_update\": 125,\n",
            "    \"smart_home\": 10,\n",
            "    \"spelling\": 73,\n",
            "    \"spending_history\": 71,\n",
            "    \"sync_device\": 130,\n",
            "    \"taxes\": 94,\n",
            "    \"tell_joke\": 36,\n",
            "    \"text\": 26,\n",
            "    \"thank_you\": 124,\n",
            "    \"time\": 4,\n",
            "    \"timer\": 32,\n",
            "    \"timezone\": 66,\n",
            "    \"tire_change\": 47,\n",
            "    \"tire_pressure\": 98,\n",
            "    \"todo_list\": 48,\n",
            "    \"todo_list_update\": 65,\n",
            "    \"traffic\": 24,\n",
            "    \"transactions\": 68,\n",
            "    \"transfer\": 133,\n",
            "    \"translate\": 61,\n",
            "    \"travel_alert\": 128,\n",
            "    \"travel_notification\": 146,\n",
            "    \"travel_suggestion\": 149,\n",
            "    \"uber\": 39,\n",
            "    \"update_playlist\": 141,\n",
            "    \"user_name\": 11,\n",
            "    \"vaccines\": 52,\n",
            "    \"w2\": 122,\n",
            "    \"weather\": 5,\n",
            "    \"what_are_your_hobbies\": 15,\n",
            "    \"what_can_i_ask_you\": 115,\n",
            "    \"what_is_your_name\": 75,\n",
            "    \"what_song\": 21,\n",
            "    \"where_are_you_from\": 77,\n",
            "    \"whisper_mode\": 14,\n",
            "    \"who_do_you_work_for\": 57,\n",
            "    \"who_made_you\": 96,\n",
            "    \"yes\": 107\n",
            "  },\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"distilbert\",\n",
            "  \"n_heads\": 12,\n",
            "  \"n_layers\": 6,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"problem_type\": \"single_label_classification\",\n",
            "  \"qa_dropout\": 0.1,\n",
            "  \"seq_classif_dropout\": 0.2,\n",
            "  \"sinusoidal_pos_embds\": false,\n",
            "  \"tie_weights_\": true,\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.18.0\",\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n",
            "loading configuration file https://huggingface.co/transformersbook/distilbert-base-uncased-finetuned-clinc/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/b260e9da00881ac757ff45d4b2e2fdd57c1bffe839137733f8e862e013e80562.840d34be4a5ee5f1a66a2b196122fb7d112229d259e5712e44e13ffc8ec27b68\n",
            "Model config DistilBertConfig {\n",
            "  \"_name_or_path\": \"transformersbook/distilbert-base-uncased-finetuned-clinc\",\n",
            "  \"activation\": \"gelu\",\n",
            "  \"architectures\": [\n",
            "    \"DistilBertForSequenceClassification\"\n",
            "  ],\n",
            "  \"attention_dropout\": 0.1,\n",
            "  \"dim\": 768,\n",
            "  \"dropout\": 0.1,\n",
            "  \"hidden_dim\": 3072,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"restaurant_reviews\",\n",
            "    \"1\": \"nutrition_info\",\n",
            "    \"2\": \"account_blocked\",\n",
            "    \"3\": \"oil_change_how\",\n",
            "    \"4\": \"time\",\n",
            "    \"5\": \"weather\",\n",
            "    \"6\": \"redeem_rewards\",\n",
            "    \"7\": \"interest_rate\",\n",
            "    \"8\": \"gas_type\",\n",
            "    \"9\": \"accept_reservations\",\n",
            "    \"10\": \"smart_home\",\n",
            "    \"11\": \"user_name\",\n",
            "    \"12\": \"report_lost_card\",\n",
            "    \"13\": \"repeat\",\n",
            "    \"14\": \"whisper_mode\",\n",
            "    \"15\": \"what_are_your_hobbies\",\n",
            "    \"16\": \"order\",\n",
            "    \"17\": \"jump_start\",\n",
            "    \"18\": \"schedule_meeting\",\n",
            "    \"19\": \"meeting_schedule\",\n",
            "    \"20\": \"freeze_account\",\n",
            "    \"21\": \"what_song\",\n",
            "    \"22\": \"meaning_of_life\",\n",
            "    \"23\": \"restaurant_reservation\",\n",
            "    \"24\": \"traffic\",\n",
            "    \"25\": \"make_call\",\n",
            "    \"26\": \"text\",\n",
            "    \"27\": \"bill_balance\",\n",
            "    \"28\": \"improve_credit_score\",\n",
            "    \"29\": \"change_language\",\n",
            "    \"30\": \"no\",\n",
            "    \"31\": \"measurement_conversion\",\n",
            "    \"32\": \"timer\",\n",
            "    \"33\": \"flip_coin\",\n",
            "    \"34\": \"do_you_have_pets\",\n",
            "    \"35\": \"balance\",\n",
            "    \"36\": \"tell_joke\",\n",
            "    \"37\": \"last_maintenance\",\n",
            "    \"38\": \"exchange_rate\",\n",
            "    \"39\": \"uber\",\n",
            "    \"40\": \"car_rental\",\n",
            "    \"41\": \"credit_limit\",\n",
            "    \"42\": \"oos\",\n",
            "    \"43\": \"shopping_list\",\n",
            "    \"44\": \"expiration_date\",\n",
            "    \"45\": \"routing\",\n",
            "    \"46\": \"meal_suggestion\",\n",
            "    \"47\": \"tire_change\",\n",
            "    \"48\": \"todo_list\",\n",
            "    \"49\": \"card_declined\",\n",
            "    \"50\": \"rewards_balance\",\n",
            "    \"51\": \"change_accent\",\n",
            "    \"52\": \"vaccines\",\n",
            "    \"53\": \"reminder_update\",\n",
            "    \"54\": \"food_last\",\n",
            "    \"55\": \"change_ai_name\",\n",
            "    \"56\": \"bill_due\",\n",
            "    \"57\": \"who_do_you_work_for\",\n",
            "    \"58\": \"share_location\",\n",
            "    \"59\": \"international_visa\",\n",
            "    \"60\": \"calendar\",\n",
            "    \"61\": \"translate\",\n",
            "    \"62\": \"carry_on\",\n",
            "    \"63\": \"book_flight\",\n",
            "    \"64\": \"insurance_change\",\n",
            "    \"65\": \"todo_list_update\",\n",
            "    \"66\": \"timezone\",\n",
            "    \"67\": \"cancel_reservation\",\n",
            "    \"68\": \"transactions\",\n",
            "    \"69\": \"credit_score\",\n",
            "    \"70\": \"report_fraud\",\n",
            "    \"71\": \"spending_history\",\n",
            "    \"72\": \"directions\",\n",
            "    \"73\": \"spelling\",\n",
            "    \"74\": \"insurance\",\n",
            "    \"75\": \"what_is_your_name\",\n",
            "    \"76\": \"reminder\",\n",
            "    \"77\": \"where_are_you_from\",\n",
            "    \"78\": \"distance\",\n",
            "    \"79\": \"payday\",\n",
            "    \"80\": \"flight_status\",\n",
            "    \"81\": \"find_phone\",\n",
            "    \"82\": \"greeting\",\n",
            "    \"83\": \"alarm\",\n",
            "    \"84\": \"order_status\",\n",
            "    \"85\": \"confirm_reservation\",\n",
            "    \"86\": \"cook_time\",\n",
            "    \"87\": \"damaged_card\",\n",
            "    \"88\": \"reset_settings\",\n",
            "    \"89\": \"pin_change\",\n",
            "    \"90\": \"replacement_card_duration\",\n",
            "    \"91\": \"new_card\",\n",
            "    \"92\": \"roll_dice\",\n",
            "    \"93\": \"income\",\n",
            "    \"94\": \"taxes\",\n",
            "    \"95\": \"date\",\n",
            "    \"96\": \"who_made_you\",\n",
            "    \"97\": \"pto_request\",\n",
            "    \"98\": \"tire_pressure\",\n",
            "    \"99\": \"how_old_are_you\",\n",
            "    \"100\": \"rollover_401k\",\n",
            "    \"101\": \"pto_request_status\",\n",
            "    \"102\": \"how_busy\",\n",
            "    \"103\": \"application_status\",\n",
            "    \"104\": \"recipe\",\n",
            "    \"105\": \"calendar_update\",\n",
            "    \"106\": \"play_music\",\n",
            "    \"107\": \"yes\",\n",
            "    \"108\": \"direct_deposit\",\n",
            "    \"109\": \"credit_limit_change\",\n",
            "    \"110\": \"gas\",\n",
            "    \"111\": \"pay_bill\",\n",
            "    \"112\": \"ingredients_list\",\n",
            "    \"113\": \"lost_luggage\",\n",
            "    \"114\": \"goodbye\",\n",
            "    \"115\": \"what_can_i_ask_you\",\n",
            "    \"116\": \"book_hotel\",\n",
            "    \"117\": \"are_you_a_bot\",\n",
            "    \"118\": \"next_song\",\n",
            "    \"119\": \"change_speed\",\n",
            "    \"120\": \"plug_type\",\n",
            "    \"121\": \"maybe\",\n",
            "    \"122\": \"w2\",\n",
            "    \"123\": \"oil_change_when\",\n",
            "    \"124\": \"thank_you\",\n",
            "    \"125\": \"shopping_list_update\",\n",
            "    \"126\": \"pto_balance\",\n",
            "    \"127\": \"order_checks\",\n",
            "    \"128\": \"travel_alert\",\n",
            "    \"129\": \"fun_fact\",\n",
            "    \"130\": \"sync_device\",\n",
            "    \"131\": \"schedule_maintenance\",\n",
            "    \"132\": \"apr\",\n",
            "    \"133\": \"transfer\",\n",
            "    \"134\": \"ingredient_substitution\",\n",
            "    \"135\": \"calories\",\n",
            "    \"136\": \"current_location\",\n",
            "    \"137\": \"international_fees\",\n",
            "    \"138\": \"calculator\",\n",
            "    \"139\": \"definition\",\n",
            "    \"140\": \"next_holiday\",\n",
            "    \"141\": \"update_playlist\",\n",
            "    \"142\": \"mpg\",\n",
            "    \"143\": \"min_payment\",\n",
            "    \"144\": \"change_user_name\",\n",
            "    \"145\": \"restaurant_suggestion\",\n",
            "    \"146\": \"travel_notification\",\n",
            "    \"147\": \"cancel\",\n",
            "    \"148\": \"pto_used\",\n",
            "    \"149\": \"travel_suggestion\",\n",
            "    \"150\": \"change_volume\"\n",
            "  },\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"label2id\": {\n",
            "    \"accept_reservations\": 9,\n",
            "    \"account_blocked\": 2,\n",
            "    \"alarm\": 83,\n",
            "    \"application_status\": 103,\n",
            "    \"apr\": 132,\n",
            "    \"are_you_a_bot\": 117,\n",
            "    \"balance\": 35,\n",
            "    \"bill_balance\": 27,\n",
            "    \"bill_due\": 56,\n",
            "    \"book_flight\": 63,\n",
            "    \"book_hotel\": 116,\n",
            "    \"calculator\": 138,\n",
            "    \"calendar\": 60,\n",
            "    \"calendar_update\": 105,\n",
            "    \"calories\": 135,\n",
            "    \"cancel\": 147,\n",
            "    \"cancel_reservation\": 67,\n",
            "    \"car_rental\": 40,\n",
            "    \"card_declined\": 49,\n",
            "    \"carry_on\": 62,\n",
            "    \"change_accent\": 51,\n",
            "    \"change_ai_name\": 55,\n",
            "    \"change_language\": 29,\n",
            "    \"change_speed\": 119,\n",
            "    \"change_user_name\": 144,\n",
            "    \"change_volume\": 150,\n",
            "    \"confirm_reservation\": 85,\n",
            "    \"cook_time\": 86,\n",
            "    \"credit_limit\": 41,\n",
            "    \"credit_limit_change\": 109,\n",
            "    \"credit_score\": 69,\n",
            "    \"current_location\": 136,\n",
            "    \"damaged_card\": 87,\n",
            "    \"date\": 95,\n",
            "    \"definition\": 139,\n",
            "    \"direct_deposit\": 108,\n",
            "    \"directions\": 72,\n",
            "    \"distance\": 78,\n",
            "    \"do_you_have_pets\": 34,\n",
            "    \"exchange_rate\": 38,\n",
            "    \"expiration_date\": 44,\n",
            "    \"find_phone\": 81,\n",
            "    \"flight_status\": 80,\n",
            "    \"flip_coin\": 33,\n",
            "    \"food_last\": 54,\n",
            "    \"freeze_account\": 20,\n",
            "    \"fun_fact\": 129,\n",
            "    \"gas\": 110,\n",
            "    \"gas_type\": 8,\n",
            "    \"goodbye\": 114,\n",
            "    \"greeting\": 82,\n",
            "    \"how_busy\": 102,\n",
            "    \"how_old_are_you\": 99,\n",
            "    \"improve_credit_score\": 28,\n",
            "    \"income\": 93,\n",
            "    \"ingredient_substitution\": 134,\n",
            "    \"ingredients_list\": 112,\n",
            "    \"insurance\": 74,\n",
            "    \"insurance_change\": 64,\n",
            "    \"interest_rate\": 7,\n",
            "    \"international_fees\": 137,\n",
            "    \"international_visa\": 59,\n",
            "    \"jump_start\": 17,\n",
            "    \"last_maintenance\": 37,\n",
            "    \"lost_luggage\": 113,\n",
            "    \"make_call\": 25,\n",
            "    \"maybe\": 121,\n",
            "    \"meal_suggestion\": 46,\n",
            "    \"meaning_of_life\": 22,\n",
            "    \"measurement_conversion\": 31,\n",
            "    \"meeting_schedule\": 19,\n",
            "    \"min_payment\": 143,\n",
            "    \"mpg\": 142,\n",
            "    \"new_card\": 91,\n",
            "    \"next_holiday\": 140,\n",
            "    \"next_song\": 118,\n",
            "    \"no\": 30,\n",
            "    \"nutrition_info\": 1,\n",
            "    \"oil_change_how\": 3,\n",
            "    \"oil_change_when\": 123,\n",
            "    \"oos\": 42,\n",
            "    \"order\": 16,\n",
            "    \"order_checks\": 127,\n",
            "    \"order_status\": 84,\n",
            "    \"pay_bill\": 111,\n",
            "    \"payday\": 79,\n",
            "    \"pin_change\": 89,\n",
            "    \"play_music\": 106,\n",
            "    \"plug_type\": 120,\n",
            "    \"pto_balance\": 126,\n",
            "    \"pto_request\": 97,\n",
            "    \"pto_request_status\": 101,\n",
            "    \"pto_used\": 148,\n",
            "    \"recipe\": 104,\n",
            "    \"redeem_rewards\": 6,\n",
            "    \"reminder\": 76,\n",
            "    \"reminder_update\": 53,\n",
            "    \"repeat\": 13,\n",
            "    \"replacement_card_duration\": 90,\n",
            "    \"report_fraud\": 70,\n",
            "    \"report_lost_card\": 12,\n",
            "    \"reset_settings\": 88,\n",
            "    \"restaurant_reservation\": 23,\n",
            "    \"restaurant_reviews\": 0,\n",
            "    \"restaurant_suggestion\": 145,\n",
            "    \"rewards_balance\": 50,\n",
            "    \"roll_dice\": 92,\n",
            "    \"rollover_401k\": 100,\n",
            "    \"routing\": 45,\n",
            "    \"schedule_maintenance\": 131,\n",
            "    \"schedule_meeting\": 18,\n",
            "    \"share_location\": 58,\n",
            "    \"shopping_list\": 43,\n",
            "    \"shopping_list_update\": 125,\n",
            "    \"smart_home\": 10,\n",
            "    \"spelling\": 73,\n",
            "    \"spending_history\": 71,\n",
            "    \"sync_device\": 130,\n",
            "    \"taxes\": 94,\n",
            "    \"tell_joke\": 36,\n",
            "    \"text\": 26,\n",
            "    \"thank_you\": 124,\n",
            "    \"time\": 4,\n",
            "    \"timer\": 32,\n",
            "    \"timezone\": 66,\n",
            "    \"tire_change\": 47,\n",
            "    \"tire_pressure\": 98,\n",
            "    \"todo_list\": 48,\n",
            "    \"todo_list_update\": 65,\n",
            "    \"traffic\": 24,\n",
            "    \"transactions\": 68,\n",
            "    \"transfer\": 133,\n",
            "    \"translate\": 61,\n",
            "    \"travel_alert\": 128,\n",
            "    \"travel_notification\": 146,\n",
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            "storing https://huggingface.co/transformersbook/distilbert-base-uncased-finetuned-clinc/resolve/main/pytorch_model.bin in cache at /root/.cache/huggingface/transformers/299152d5c04a69678bc32e7081655a202e87ced1d401592c763fc404373adefc.6546e0b39d5da3515b1ee1d9d5a77edaa57fddce919158396e03eb3d01ce2d39\n",
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            "loading weights file https://huggingface.co/transformersbook/distilbert-base-uncased-finetuned-clinc/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/299152d5c04a69678bc32e7081655a202e87ced1d401592c763fc404373adefc.6546e0b39d5da3515b1ee1d9d5a77edaa57fddce919158396e03eb3d01ce2d39\n",
            "All model checkpoint weights were used when initializing DistilBertForSequenceClassification.\n",
            "\n",
            "All the weights of DistilBertForSequenceClassification were initialized from the model checkpoint at transformersbook/distilbert-base-uncased-finetuned-clinc.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use DistilBertForSequenceClassification for predictions without further training.\n",
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            "storing https://huggingface.co/transformersbook/distilbert-base-uncased-finetuned-clinc/resolve/main/tokenizer_config.json in cache at /root/.cache/huggingface/transformers/d5833481b51591772dc05742ee8ff81f90cd17e8a9dcabe1c13ce1030c94a572.42154c5fd30bfa7e34941d0d8ad26f8a3936990926fbe06b2da76dd749b1c6d4\n",
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            "storing https://huggingface.co/transformersbook/distilbert-base-uncased-finetuned-clinc/resolve/main/vocab.txt in cache at /root/.cache/huggingface/transformers/2c935eb35425fd20b0d3c39ca3f75cb695e488ea33011df00f5e48c01a8e55b0.d789d64ebfe299b0e416afc4a169632f903f693095b4629a7ea271d5a0cf2c99\n",
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          "text": [
            "storing https://huggingface.co/transformersbook/distilbert-base-uncased-finetuned-clinc/resolve/main/tokenizer.json in cache at /root/.cache/huggingface/transformers/b5e18c5d15cb77f9e3470dd4394e816170241bcb63077c9cc8a8556db639d4a3.59b3fc05e6327297545bda94ff9c1ef4b08d7fe92a7822f2260c164f54d0d152\n",
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        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "storing https://huggingface.co/transformersbook/distilbert-base-uncased-finetuned-clinc/resolve/main/special_tokens_map.json in cache at /root/.cache/huggingface/transformers/2595e78debd7a1082d8f01c336df4a7cb20b2d296adb5ac8edc4824781bf37f7.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d\n",
            "creating metadata file for /root/.cache/huggingface/transformers/2595e78debd7a1082d8f01c336df4a7cb20b2d296adb5ac8edc4824781bf37f7.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d\n",
            "loading file https://huggingface.co/transformersbook/distilbert-base-uncased-finetuned-clinc/resolve/main/vocab.txt from cache at /root/.cache/huggingface/transformers/2c935eb35425fd20b0d3c39ca3f75cb695e488ea33011df00f5e48c01a8e55b0.d789d64ebfe299b0e416afc4a169632f903f693095b4629a7ea271d5a0cf2c99\n",
            "loading file https://huggingface.co/transformersbook/distilbert-base-uncased-finetuned-clinc/resolve/main/tokenizer.json from cache at /root/.cache/huggingface/transformers/b5e18c5d15cb77f9e3470dd4394e816170241bcb63077c9cc8a8556db639d4a3.59b3fc05e6327297545bda94ff9c1ef4b08d7fe92a7822f2260c164f54d0d152\n",
            "loading file https://huggingface.co/transformersbook/distilbert-base-uncased-finetuned-clinc/resolve/main/added_tokens.json from cache at None\n",
            "loading file https://huggingface.co/transformersbook/distilbert-base-uncased-finetuned-clinc/resolve/main/special_tokens_map.json from cache at /root/.cache/huggingface/transformers/2595e78debd7a1082d8f01c336df4a7cb20b2d296adb5ac8edc4824781bf37f7.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d\n",
            "loading file https://huggingface.co/transformersbook/distilbert-base-uncased-finetuned-clinc/resolve/main/tokenizer_config.json from cache at /root/.cache/huggingface/transformers/d5833481b51591772dc05742ee8ff81f90cd17e8a9dcabe1c13ce1030c94a572.42154c5fd30bfa7e34941d0d8ad26f8a3936990926fbe06b2da76dd749b1c6d4\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model size (MB) - 255.89\n",
            "Average latency (ms) - 47.97 +\\- 11.77\n",
            "Accuracy on test set - 0.858\n"
          ]
        }
      ],
      "source": [
        "#@ INSPECTING PERFORMANCE BENCHMARK:\n",
        "finetuned_ckpt = \"transformersbook/distilbert-base-uncased-finetuned-clinc\"     # Initializing finetuned model checkpoint.\n",
        "pipe = pipeline(\"text-classification\", model=finetuned_ckpt)                    # Initializing model pipeline.\n",
        "optim_type = \"DistilBERT\"                                                       # Initialization.\n",
        "pb = PerformanceBenchmark(pipe, clinc[\"test\"], optim_type=optim_type)           # Initializing performance benchmark.\n",
        "perf_metrics.update(pb.run_benchmark())                                         # Getting metrics. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "W6Wu8YIN2EUg",
        "outputId": "fe738e53-bb99-4dae-c18c-537c16f7fd50"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@ FUNCTION FOR PLOTTING ACCURACY ACROSS LATENCY:\n",
        "def plot_metrics(perf_metrics, current_optim_type):          \n",
        "    df = pd.DataFrame.from_dict(perf_metrics, orient=\"index\") \n",
        "    for idx in df.index:\n",
        "        df_opt = df.loc[idx]\n",
        "        if idx == current_optim_type:\n",
        "            plt.scatter(df_opt[\"time_avg_ms\"], df_opt[\"accuracy\"] * 100,\n",
        "                        alpha=0.5, s=df_opt[\"size_mb\"], label=idx, \n",
        "                        marker='$\\u25CC$')\n",
        "        else:\n",
        "            plt.scatter(df_opt[\"time_avg_ms\"], df_opt[\"accuracy\"] * 100, \n",
        "                        s=df_opt[\"size_mb\"], label=idx, alpha=0.5)\n",
        "    legend = plt.legend(bbox_to_anchor=(1, 1))\n",
        "    for handle in legend.legendHandles:\n",
        "        handle.set_sizes([20])\n",
        "    plt.ylim(80, 90)\n",
        "    xlim = int(perf_metrics[\"BERT baseline\"][\"time_avg_ms\"] + 3)\n",
        "    plt.xlim(1, xlim)\n",
        "    plt.ylabel(\"Accuracy (%)\")\n",
        "    plt.xlabel(\"Average latency (ms)\")\n",
        "    plt.show()\n",
        "\n",
        "#@ IMPLEMENTATION OF FUNCTION:\n",
        "plot_metrics(perf_metrics, optim_type)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4cp_fAx9ggem"
      },
      "source": [
        "### **Hyperparameters with Optuna**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "6qv4rv1ZIMpe"
      },
      "outputs": [],
      "source": [
        "#@ DEFINING OBJECTIVE FUNCTION:\n",
        "def objective(trial):                                       # Defining function.\n",
        "    x = trial.suggest_float(\"x\", -2, 2)                     # Initialization. \n",
        "    y = trial.suggest_float(\"y\", -2, 2)                     # Initialization. \n",
        "    return (1 - x) ** 2 + 100 * (y - x ** 2) ** 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-fuLeRgdhjPG",
        "outputId": "f17603a2-8dac-444f-bea8-712b6b26e872"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[32m[I 2022-05-01 04:23:57,951]\u001b[0m A new study created in memory with name: no-name-9ca4e628-0266-4b82-9027-de0d614fa9b4\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:57,957]\u001b[0m Trial 0 finished with value: 176.8634238044208 and parameters: {'x': 0.7581363644695873, 'y': 1.9044508807957552}. Best is trial 0 with value: 176.8634238044208.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:57,960]\u001b[0m Trial 1 finished with value: 2.1367160069114792 and parameters: {'x': -0.4311760230546833, 'y': 0.1561720167427283}. Best is trial 1 with value: 2.1367160069114792.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:57,963]\u001b[0m Trial 2 finished with value: 1731.9550624549006 and parameters: {'x': -1.9401438349597404, 'y': -0.38711955442009893}. Best is trial 1 with value: 2.1367160069114792.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:57,966]\u001b[0m Trial 3 finished with value: 14.919633995607672 and parameters: {'x': 0.38156278811494415, 'y': -0.23568623991757276}. Best is trial 1 with value: 2.1367160069114792.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:57,971]\u001b[0m Trial 4 finished with value: 2.112892614955816 and parameters: {'x': 0.12143405409101282, 'y': -0.10105595069568851}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:57,975]\u001b[0m Trial 5 finished with value: 47.19799015188505 and parameters: {'x': 0.05964365511580905, 'y': 0.6840992092504741}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:57,984]\u001b[0m Trial 6 finished with value: 194.03175065272833 and parameters: {'x': -1.201690430523016, 'y': 0.06861701703912271}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:57,987]\u001b[0m Trial 7 finished with value: 59.126240023749915 and parameters: {'x': 0.5102921076908711, 'y': 1.0277729513889517}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:57,992]\u001b[0m Trial 8 finished with value: 26.468656418396485 and parameters: {'x': 1.0971070209674352, 'y': 0.6892584866909663}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:57,997]\u001b[0m Trial 9 finished with value: 1553.3531199209817 and parameters: {'x': -1.6069752505409038, 'y': -1.3502591815416132}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,011]\u001b[0m Trial 10 finished with value: 2484.957239310749 and parameters: {'x': 1.7308081799987285, 'y': -1.9887018630795197}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,018]\u001b[0m Trial 11 finished with value: 120.00728293070654 and parameters: {'x': -0.41969979491607234, 'y': -0.9100920898023532}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,025]\u001b[0m Trial 12 finished with value: 6.384895062713978 and parameters: {'x': -0.6439438313640666, 'y': 0.2227693187431426}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,037]\u001b[0m Trial 13 finished with value: 145.51149885095074 and parameters: {'x': -0.6588918791287905, 'y': -0.7606818822388776}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,047]\u001b[0m Trial 14 finished with value: 252.08218591400123 and parameters: {'x': -0.24063261818674175, 'y': 1.6407591249081261}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,053]\u001b[0m Trial 15 finished with value: 4.467846537485662 and parameters: {'x': -1.091633666900972, 'y': 1.2221460477146202}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,060]\u001b[0m Trial 16 finished with value: 56.87108601135489 and parameters: {'x': 1.1179318178543443, 'y': 0.495734560240662}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,068]\u001b[0m Trial 17 finished with value: 42.87878236148141 and parameters: {'x': 0.15438866025798537, 'y': -0.6255001395353529}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,079]\u001b[0m Trial 18 finished with value: 1836.4995983230044 and parameters: {'x': 1.7207429510149057, 'y': -1.3238775648657612}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,087]\u001b[0m Trial 19 finished with value: 88.03927211435833 and parameters: {'x': -1.0279159356852239, 'y': 0.14049529894958768}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,094]\u001b[0m Trial 20 finished with value: 134.77826821935287 and parameters: {'x': -0.13708891972315473, 'y': 1.1741517449189758}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,106]\u001b[0m Trial 21 finished with value: 24.215458333387478 and parameters: {'x': -0.9192225536168955, 'y': 1.2980930789142808}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,114]\u001b[0m Trial 22 finished with value: 160.5688174185508 and parameters: {'x': -1.29564643193714, 'y': 0.43251018506904454}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,121]\u001b[0m Trial 23 finished with value: 28.19767478742315 and parameters: {'x': -0.5405508345818089, 'y': -0.21598171239358183}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,133]\u001b[0m Trial 24 finished with value: 254.3570902523085 and parameters: {'x': -1.575367397940743, 'y': 0.907855588167888}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,143]\u001b[0m Trial 25 finished with value: 163.33346447908005 and parameters: {'x': -0.2570037492306453, 'y': 1.337874051823849}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,153]\u001b[0m Trial 26 finished with value: 407.27347960413266 and parameters: {'x': -0.8499015000771304, 'y': -1.2872727348554913}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,161]\u001b[0m Trial 27 finished with value: 218.73566954140483 and parameters: {'x': 0.20375171912018847, 'y': 1.5183412871040483}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,170]\u001b[0m Trial 28 finished with value: 217.46643620932545 and parameters: {'x': 0.6844635717691723, 'y': 1.9428270894729427}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,178]\u001b[0m Trial 29 finished with value: 829.8843738452867 and parameters: {'x': -1.5598551942835033, 'y': -0.4362271669718861}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,187]\u001b[0m Trial 30 finished with value: 5.401012656524911 and parameters: {'x': -0.3863286088121515, 'y': 0.33577340227570673}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,196]\u001b[0m Trial 31 finished with value: 6.048650838600819 and parameters: {'x': -0.37019949160383997, 'y': -0.06718759761787851}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,205]\u001b[0m Trial 32 finished with value: 15.396713953995235 and parameters: {'x': -0.08415118325507692, 'y': 0.3841932109821715}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,216]\u001b[0m Trial 33 finished with value: 11.267829081720947 and parameters: {'x': -0.723163059299384, 'y': 0.8110366443070323}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,224]\u001b[0m Trial 34 finished with value: 259.3372880277243 and parameters: {'x': -1.2054276595410058, 'y': -0.14216632667414064}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,233]\u001b[0m Trial 35 finished with value: 32.68514197467161 and parameters: {'x': 0.38157147103715783, 'y': -0.4227577508663358}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,242]\u001b[0m Trial 36 finished with value: 6.908917412594189 and parameters: {'x': 0.026174604512540667, 'y': 0.24482813176919505}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,258]\u001b[0m Trial 37 finished with value: 4.1252467669019675 and parameters: {'x': 0.8476412024128228, 'y': 0.5159608316634066}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,266]\u001b[0m Trial 38 finished with value: 46.28982205650056 and parameters: {'x': 1.1921970575533647, 'y': 0.7412391074915041}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,275]\u001b[0m Trial 39 finished with value: 5.216642926832936 and parameters: {'x': 0.878409814740511, 'y': 0.999679639881421}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,285]\u001b[0m Trial 40 finished with value: 829.6550860776779 and parameters: {'x': 1.3765937616707542, 'y': -0.9851168056045309}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,295]\u001b[0m Trial 41 finished with value: 37.23741776207904 and parameters: {'x': 0.6356818752653772, 'y': 1.0132276387513917}. Best is trial 4 with value: 2.112892614955816.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,304]\u001b[0m Trial 42 finished with value: 0.508357588509797 and parameters: {'x': 0.7777592058180293, 'y': 0.537162307175235}. Best is trial 42 with value: 0.508357588509797.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,313]\u001b[0m Trial 43 finished with value: 1.644229317072972 and parameters: {'x': 0.8756679717825123, 'y': 0.6391710892230986}. Best is trial 42 with value: 0.508357588509797.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,321]\u001b[0m Trial 44 finished with value: 10.806829414347952 and parameters: {'x': 0.9549358903750773, 'y': 0.5831960193431052}. Best is trial 42 with value: 0.508357588509797.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,330]\u001b[0m Trial 45 finished with value: 428.43132422518363 and parameters: {'x': 1.4550124900092278, 'y': 0.04770326237882866}. Best is trial 42 with value: 0.508357588509797.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,338]\u001b[0m Trial 46 finished with value: 16.412374581406986 and parameters: {'x': 0.446942347795664, 'y': 0.6010865269213002}. Best is trial 42 with value: 0.508357588509797.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,348]\u001b[0m Trial 47 finished with value: 37.0537959290673 and parameters: {'x': 0.8479891719161016, 'y': 0.11055717589881611}. Best is trial 42 with value: 0.508357588509797.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,358]\u001b[0m Trial 48 finished with value: 18.290658999424288 and parameters: {'x': 0.6094571207645176, 'y': -0.05445091495077528}. Best is trial 42 with value: 0.508357588509797.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,369]\u001b[0m Trial 49 finished with value: 1816.053365035114 and parameters: {'x': 1.9989852006855306, 'y': -0.2644048594406877}. Best is trial 42 with value: 0.508357588509797.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,378]\u001b[0m Trial 50 finished with value: 40.61608850252442 and parameters: {'x': 0.2295180198116984, 'y': -0.5799544371029238}. Best is trial 42 with value: 0.508357588509797.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,386]\u001b[0m Trial 51 finished with value: 0.5380385888911342 and parameters: {'x': 1.3159204924214278, 'y': 1.6654475859329478}. Best is trial 42 with value: 0.508357588509797.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,396]\u001b[0m Trial 52 finished with value: 245.4939592985594 and parameters: {'x': 1.3373016181590234, 'y': 0.221914041903591}. Best is trial 42 with value: 0.508357588509797.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,406]\u001b[0m Trial 53 finished with value: 41.809706675828735 and parameters: {'x': 1.0329757335398313, 'y': 1.7136347157195149}. Best is trial 42 with value: 0.508357588509797.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,415]\u001b[0m Trial 54 finished with value: 331.2572512177367 and parameters: {'x': 1.5385126467836938, 'y': 0.5477706171182023}. Best is trial 42 with value: 0.508357588509797.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,424]\u001b[0m Trial 55 finished with value: 40.4792150178062 and parameters: {'x': 1.2156638041882573, 'y': 0.8419713277643752}. Best is trial 42 with value: 0.508357588509797.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,431]\u001b[0m Trial 56 finished with value: 108.63732537850385 and parameters: {'x': 0.8382301174559952, 'y': 1.7447964913393932}. Best is trial 42 with value: 0.508357588509797.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,441]\u001b[0m Trial 57 finished with value: 134.9161695069394 and parameters: {'x': 0.5282615982906651, 'y': 1.439636174961212}. Best is trial 42 with value: 0.508357588509797.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,451]\u001b[0m Trial 58 finished with value: 2191.07915508835 and parameters: {'x': 1.7013797340267356, 'y': -1.7856779491439345}. Best is trial 42 with value: 0.508357588509797.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,461]\u001b[0m Trial 59 finished with value: 114.78521650321689 and parameters: {'x': 0.28160957689154276, 'y': 1.1482713607052282}. Best is trial 42 with value: 0.508357588509797.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,469]\u001b[0m Trial 60 finished with value: 0.6608518606547896 and parameters: {'x': 0.7773458403146923, 'y': 0.6824507601144236}. Best is trial 42 with value: 0.508357588509797.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,478]\u001b[0m Trial 61 finished with value: 6.119146916223407 and parameters: {'x': 0.9829005047724998, 'y': 0.7187302174002427}. Best is trial 42 with value: 0.508357588509797.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,488]\u001b[0m Trial 62 finished with value: 133.2828691132895 and parameters: {'x': 1.2237119123331703, 'y': 0.3432056144949426}. Best is trial 42 with value: 0.508357588509797.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,498]\u001b[0m Trial 63 finished with value: 0.14723052415071447 and parameters: {'x': 0.7115986654023692, 'y': 0.48106353293018395}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,506]\u001b[0m Trial 64 finished with value: 8.20285787925011 and parameters: {'x': 0.09454471636526984, 'y': 0.280655626100544}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,516]\u001b[0m Trial 65 finished with value: 10.942420836484883 and parameters: {'x': 0.6715243769513007, 'y': 0.12178659837184452}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,525]\u001b[0m Trial 66 finished with value: 72.4179388329334 and parameters: {'x': 0.7472153648634436, 'y': -0.2922809710684978}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,535]\u001b[0m Trial 67 finished with value: 2.1277138132521256 and parameters: {'x': 0.556876410010285, 'y': 0.4490845455753649}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,543]\u001b[0m Trial 68 finished with value: 6.056659860365428 and parameters: {'x': 0.4594814768029615, 'y': 0.45121695060929434}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,554]\u001b[0m Trial 69 finished with value: 6.144946867228761 and parameters: {'x': 1.0887869502048688, 'y': 0.9377260449643039}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,563]\u001b[0m Trial 70 finished with value: 65.69252931079505 and parameters: {'x': 0.5507526306637487, 'y': 1.1125917431258623}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,572]\u001b[0m Trial 71 finished with value: 27.077095379719594 and parameters: {'x': 0.3733532483129832, 'y': 0.6559621905689653}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,582]\u001b[0m Trial 72 finished with value: 1.1517289422683576 and parameters: {'x': -0.0720519130561747, 'y': 0.010124669820382654}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,592]\u001b[0m Trial 73 finished with value: 1.2982959086023207 and parameters: {'x': -0.13820809239069054, 'y': 0.013830568893131306}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,603]\u001b[0m Trial 74 finished with value: 4.124157513804663 and parameters: {'x': -0.149022434691163, 'y': -0.14524096156093236}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,620]\u001b[0m Trial 75 finished with value: 1.0855834337688595 and parameters: {'x': -0.041884130663377456, 'y': 0.0009739468786444539}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,629]\u001b[0m Trial 76 finished with value: 32.82199075018726 and parameters: {'x': 0.018906005716059465, 'y': -0.5640842811488426}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,639]\u001b[0m Trial 77 finished with value: 2.216904114095582 and parameters: {'x': -0.26908316932158727, 'y': -0.005461572549584971}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,650]\u001b[0m Trial 78 finished with value: 2.2932128082215972 and parameters: {'x': -0.4811016819441389, 'y': 0.19990718562875356}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,663]\u001b[0m Trial 79 finished with value: 59.445590112496504 and parameters: {'x': -0.04509500885352355, 'y': -0.761860192834329}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,674]\u001b[0m Trial 80 finished with value: 357.0711091774182 and parameters: {'x': 0.3337148320084738, 'y': 1.9998230899542175}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,684]\u001b[0m Trial 81 finished with value: 11.999330116949993 and parameters: {'x': 0.10141687369041219, 'y': -0.3242572679574325}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,693]\u001b[0m Trial 82 finished with value: 3.3862429795823625 and parameters: {'x': -0.17489231084315524, 'y': -0.11104145625675618}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,702]\u001b[0m Trial 83 finished with value: 8.856532350476666 and parameters: {'x': -0.6020363070833441, 'y': 0.11164875130739506}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,712]\u001b[0m Trial 84 finished with value: 60.61046337807846 and parameters: {'x': 0.17947272926770674, 'y': 0.8064016409194671}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,721]\u001b[0m Trial 85 finished with value: 27.762061342367417 and parameters: {'x': -0.3064626861894426, 'y': -0.41652372940044247}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,731]\u001b[0m Trial 86 finished with value: 76.57281462528006 and parameters: {'x': 0.9354000087852379, 'y': -6.19169546370972e-05}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,740]\u001b[0m Trial 87 finished with value: 7.07998436971394 and parameters: {'x': 0.7778721353464488, 'y': 0.33993145098773014}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,751]\u001b[0m Trial 88 finished with value: 6.802412674702918 and parameters: {'x': -0.07498133965105105, 'y': -0.23200834796295589}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,759]\u001b[0m Trial 89 finished with value: 276.6097467320636 and parameters: {'x': 1.3030858329689372, 'y': 0.035149993153894124}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,769]\u001b[0m Trial 90 finished with value: 45.06274839077502 and parameters: {'x': 1.1347199753811035, 'y': 0.6164366908872161}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,777]\u001b[0m Trial 91 finished with value: 1.0192903660109522 and parameters: {'x': 0.7280123473803695, 'y': 0.4327747655523693}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,786]\u001b[0m Trial 92 finished with value: 340.82428626272895 and parameters: {'x': 1.5311165002204228, 'y': 0.4989391811620012}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,795]\u001b[0m Trial 93 finished with value: 5.518405258358588 and parameters: {'x': 0.7294491683283063, 'y': 0.298746405098784}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,805]\u001b[0m Trial 94 finished with value: 77.98599433657427 and parameters: {'x': 1.033014537746679, 'y': 0.1840284148354715}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,814]\u001b[0m Trial 95 finished with value: 4.113534131442144 and parameters: {'x': 0.4560061915693033, 'y': 0.403328567187711}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,823]\u001b[0m Trial 96 finished with value: 1619.9542364100532 and parameters: {'x': -1.961065184419319, 'y': -0.16818189902533437}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,838]\u001b[0m Trial 97 finished with value: 146.53561063146734 and parameters: {'x': 0.30344797908777266, 'y': 1.300593919273074}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,849]\u001b[0m Trial 98 finished with value: 10.296674921537011 and parameters: {'x': 0.6266034021809781, 'y': 0.7113362305957847}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,859]\u001b[0m Trial 99 finished with value: 7.642853369260941 and parameters: {'x': -0.4269787095136902, 'y': -0.05447146837201659}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,870]\u001b[0m Trial 100 finished with value: 172.5582837252016 and parameters: {'x': 0.9080430384327229, 'y': -0.4890400709067216}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,879]\u001b[0m Trial 101 finished with value: 2.629806083408648 and parameters: {'x': 0.541698472434417, 'y': 0.4489931990262986}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,891]\u001b[0m Trial 102 finished with value: 6.117964018505808 and parameters: {'x': 0.7852228971467778, 'y': 0.8629859316987427}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,901]\u001b[0m Trial 103 finished with value: 30.970724594199236 and parameters: {'x': 0.11223192593235992, 'y': 0.5619828674446226}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,912]\u001b[0m Trial 104 finished with value: 5.528528780155666 and parameters: {'x': 0.22048340407027325, 'y': 0.2704435573277527}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,925]\u001b[0m Trial 105 finished with value: 9.53485261601423 and parameters: {'x': 0.692881497029969, 'y': 0.7873392374236583}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,936]\u001b[0m Trial 106 finished with value: 0.6405519880305804 and parameters: {'x': 0.572866939148585, 'y': 0.3958603047133673}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,946]\u001b[0m Trial 107 finished with value: 262.41462936292345 and parameters: {'x': 0.4378750567977341, 'y': 1.8106806574611936}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,958]\u001b[0m Trial 108 finished with value: 7.489709097183105 and parameters: {'x': 0.8164832084874224, 'y': 0.9397021622641542}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,968]\u001b[0m Trial 109 finished with value: 205.61774438031992 and parameters: {'x': -0.20952425991944937, 'y': 1.4727278908919672}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,979]\u001b[0m Trial 110 finished with value: 11.419490775407677 and parameters: {'x': 0.9957686552941074, 'y': 0.6536281092483271}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:58,992]\u001b[0m Trial 111 finished with value: 0.5167813347537433 and parameters: {'x': 0.5887460319849568, 'y': 0.40558387017748776}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,003]\u001b[0m Trial 112 finished with value: 3.735070837183899 and parameters: {'x': 0.5933911097634297, 'y': 0.16317545206551623}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,015]\u001b[0m Trial 113 finished with value: 2.011352003913646 and parameters: {'x': -0.32929066234638715, 'y': 0.059001748676179}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,026]\u001b[0m Trial 114 finished with value: 20.68378541244593 and parameters: {'x': 0.9006751443642591, 'y': 0.3565298902367506}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,037]\u001b[0m Trial 115 finished with value: 21.350750410507665 and parameters: {'x': -0.7154509221895009, 'y': 0.08282481996688983}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,049]\u001b[0m Trial 116 finished with value: 29.1501192438792 and parameters: {'x': 0.020515305170881543, 'y': 0.5313703007280928}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,058]\u001b[0m Trial 117 finished with value: 3.169443262728323 and parameters: {'x': -0.3769700383224759, 'y': 0.254951293429216}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,069]\u001b[0m Trial 118 finished with value: 257.83101129010714 and parameters: {'x': -0.08240370439232256, 'y': 1.6088497124410177}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,077]\u001b[0m Trial 119 finished with value: 1.613125387941284 and parameters: {'x': 0.717508969712513, 'y': 0.3909916527045727}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,088]\u001b[0m Trial 120 finished with value: 1.0289517055703459 and parameters: {'x': 0.7059031539114383, 'y': 0.40121894797811486}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,097]\u001b[0m Trial 121 finished with value: 1.3207972340298888 and parameters: {'x': 0.7163235182752457, 'y': 0.401749508642587}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,108]\u001b[0m Trial 122 finished with value: 0.3590823874197556 and parameters: {'x': 0.6565004212327455, 'y': 0.3818918431282302}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,117]\u001b[0m Trial 123 finished with value: 0.2310829766592275 and parameters: {'x': 0.6766520246286427, 'y': 0.4222870384254537}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,129]\u001b[0m Trial 124 finished with value: 1.1816596183149692 and parameters: {'x': 0.6266829562506339, 'y': 0.4948243287313685}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,140]\u001b[0m Trial 125 finished with value: 0.8933398937369494 and parameters: {'x': 0.631155997251767, 'y': 0.4853805333332253}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,151]\u001b[0m Trial 126 finished with value: 12.774963656066012 and parameters: {'x': 0.47517539577047496, 'y': 0.5793382985181288}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,161]\u001b[0m Trial 127 finished with value: 658.730998409602 and parameters: {'x': -1.810689286127414, 'y': 0.7274566822448794}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,172]\u001b[0m Trial 128 finished with value: 3.112534631646673 and parameters: {'x': 0.3789654655047653, 'y': 0.3087466119787289}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,182]\u001b[0m Trial 129 finished with value: 3.1443498974258612 and parameters: {'x': 0.8068957672079842, 'y': 0.47481221550006775}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,193]\u001b[0m Trial 130 finished with value: 29.231920310778523 and parameters: {'x': 1.084707517918877, 'y': 0.6359912415762752}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,202]\u001b[0m Trial 131 finished with value: 2.79215361061132 and parameters: {'x': 0.5989607646111609, 'y': 0.5209674723935525}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,214]\u001b[0m Trial 132 finished with value: 3.2863723847264175 and parameters: {'x': 0.6181985559708336, 'y': 0.20495207278018918}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,224]\u001b[0m Trial 133 finished with value: 0.3675254193526801 and parameters: {'x': 0.6876960145074746, 'y': 0.4209650885901772}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,235]\u001b[0m Trial 134 finished with value: 1.7706310036167878 and parameters: {'x': 0.6842761276537093, 'y': 0.33896860932494094}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,244]\u001b[0m Trial 135 finished with value: 3.120887571947434 and parameters: {'x': 0.5159192123149735, 'y': 0.4360712317153125}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,256]\u001b[0m Trial 136 finished with value: 31.24812350199753 and parameters: {'x': 0.9021190167043216, 'y': 0.2549042114960646}. Best is trial 63 with value: 0.14723052415071447.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,265]\u001b[0m Trial 137 finished with value: 0.08554825248031783 and parameters: {'x': 0.7830392064358538, 'y': 0.593535030724235}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,276]\u001b[0m Trial 138 finished with value: 892.4358310638447 and parameters: {'x': 1.9122092373519513, 'y': 0.6705707827853693}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,286]\u001b[0m Trial 139 finished with value: 2.3860231850651568 and parameters: {'x': 0.7817025902220757, 'y': 0.7639762186996191}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,297]\u001b[0m Trial 140 finished with value: 14.030608201673285 and parameters: {'x': 0.9791768416599975, 'y': 0.5842185405791671}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,306]\u001b[0m Trial 141 finished with value: 11.961216994747863 and parameters: {'x': 0.8515278050547027, 'y': 0.37956851876602093}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,317]\u001b[0m Trial 142 finished with value: 8.821120497012805 and parameters: {'x': 0.6734064101210049, 'y': 0.15827359963870358}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,325]\u001b[0m Trial 143 finished with value: 7.725734250968163 and parameters: {'x': 0.4203988001122074, 'y': 0.4485769562367798}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,338]\u001b[0m Trial 144 finished with value: 8.93129660263697 and parameters: {'x': 0.4978339012809305, 'y': 0.542442154205973}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,348]\u001b[0m Trial 145 finished with value: 0.20326242149268958 and parameters: {'x': 0.5534518950555667, 'y': 0.30009835487361897}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,359]\u001b[0m Trial 146 finished with value: 30.572261024806572 and parameters: {'x': 0.7221305558333246, 'y': 1.0736957698640979}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,369]\u001b[0m Trial 147 finished with value: 4.870966654217774 and parameters: {'x': 0.3018469135895359, 'y': 0.300480824470145}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,380]\u001b[0m Trial 148 finished with value: 1.694091680855245 and parameters: {'x': 0.5265996541969701, 'y': 0.3985500839814414}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,389]\u001b[0m Trial 149 finished with value: 1.7296104227283022 and parameters: {'x': 0.7893293425560696, 'y': 0.4932244677954074}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,400]\u001b[0m Trial 150 finished with value: 12.081739885897399 and parameters: {'x': 0.578760500581318, 'y': 0.6799897619924553}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,410]\u001b[0m Trial 151 finished with value: 2.6885493288812654 and parameters: {'x': 0.6569823319730237, 'y': 0.2712858890431884}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,423]\u001b[0m Trial 152 finished with value: 1.3872864807126015 and parameters: {'x': 0.8498540409566636, 'y': 0.6054296896287532}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,432]\u001b[0m Trial 153 finished with value: 0.36178543574383093 and parameters: {'x': 0.3987416866930096, 'y': 0.1606498539226266}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,442]\u001b[0m Trial 154 finished with value: 0.7053695177509887 and parameters: {'x': 0.3726875054576924, 'y': 0.19473937847794004}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,452]\u001b[0m Trial 155 finished with value: 0.4483104098707066 and parameters: {'x': 0.3510987042009735, 'y': 0.13977409304384575}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,463]\u001b[0m Trial 156 finished with value: 0.40602122104709837 and parameters: {'x': 0.3655286835894963, 'y': 0.13949965609483778}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,473]\u001b[0m Trial 157 finished with value: 2.3807415451822362 and parameters: {'x': 0.2492452970616591, 'y': 0.1969234006442563}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,484]\u001b[0m Trial 158 finished with value: 0.7801250024838156 and parameters: {'x': 0.401667625589345, 'y': 0.09636585927393027}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,493]\u001b[0m Trial 159 finished with value: 0.3977751686842734 and parameters: {'x': 0.36934351794240056, 'y': 0.13572492182044393}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,503]\u001b[0m Trial 160 finished with value: 0.46445721239741444 and parameters: {'x': 0.34776580699563775, 'y': 0.1011805479506766}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,513]\u001b[0m Trial 161 finished with value: 0.662603914777896 and parameters: {'x': 0.34446518993310044, 'y': 0.16691370459241217}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,524]\u001b[0m Trial 162 finished with value: 0.7604226244464687 and parameters: {'x': 0.30200390960982854, 'y': 0.14347720245422743}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,533]\u001b[0m Trial 163 finished with value: 1.653681323367295 and parameters: {'x': 0.19651385083211992, 'y': -0.061786057927908866}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,548]\u001b[0m Trial 164 finished with value: 1.5778202624198865 and parameters: {'x': 0.45773341378740745, 'y': 0.09621642661000923}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,558]\u001b[0m Trial 165 finished with value: 2.6681416085290923 and parameters: {'x': 0.3476079266490133, 'y': 0.27058193648270035}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,568]\u001b[0m Trial 166 finished with value: 0.5244499508307284 and parameters: {'x': 0.5260398436344594, 'y': 0.33147298270216646}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,577]\u001b[0m Trial 167 finished with value: 0.365895193227467 and parameters: {'x': 0.5596128884479601, 'y': 0.35463396790735646}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,589]\u001b[0m Trial 168 finished with value: 304.94507845481104 and parameters: {'x': 0.5254370194738336, 'y': -1.4695386596385795}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,600]\u001b[0m Trial 169 finished with value: 1.5964680426602267 and parameters: {'x': 0.4764977850016865, 'y': 0.34204637703050306}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,610]\u001b[0m Trial 170 finished with value: 0.7578887028471578 and parameters: {'x': 0.5637277405643433, 'y': 0.242452704724156}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,619]\u001b[0m Trial 171 finished with value: 2.2312187995469808 and parameters: {'x': 0.4354035013249187, 'y': 0.32786755335279727}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,632]\u001b[0m Trial 172 finished with value: 7.298418051009932 and parameters: {'x': 0.570756575789014, 'y': 0.05903908912362876}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,646]\u001b[0m Trial 173 finished with value: 7.663341847360064 and parameters: {'x': 0.25873602290300735, 'y': 0.3336627121840788}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,656]\u001b[0m Trial 174 finished with value: 2.8280091365399023 and parameters: {'x': 0.6191736995035284, 'y': 0.21957800956427448}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,666]\u001b[0m Trial 175 finished with value: 3.8336173745147386 and parameters: {'x': 0.49249090670262796, 'y': 0.43165181176472006}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,676]\u001b[0m Trial 176 finished with value: 0.8980012643310848 and parameters: {'x': 0.1633924913597312, 'y': -0.017810100017238095}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,685]\u001b[0m Trial 177 finished with value: 0.16450745300071978 and parameters: {'x': 0.7650253579049971, 'y': 0.5522040991203078}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,697]\u001b[0m Trial 178 finished with value: 0.39705326977519023 and parameters: {'x': 0.377678062098049, 'y': 0.1327570576382685}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,707]\u001b[0m Trial 179 finished with value: 9.439660755534778 and parameters: {'x': 0.40317135991202674, 'y': -0.13884058372465996}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,718]\u001b[0m Trial 180 finished with value: 0.8501396574639417 and parameters: {'x': 0.2946895210281169, 'y': 0.1462285104158444}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,728]\u001b[0m Trial 181 finished with value: 0.21160880769591592 and parameters: {'x': 0.5642854637079961, 'y': 0.3036662539675191}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,744]\u001b[0m Trial 182 finished with value: 2.7559353081275155 and parameters: {'x': 0.670817629817983, 'y': 0.28728260911366293}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,754]\u001b[0m Trial 183 finished with value: 1.0140298956477023 and parameters: {'x': 0.3966088199818566, 'y': 0.07667914282210744}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,766]\u001b[0m Trial 184 finished with value: 0.2981799593505158 and parameters: {'x': 0.5165193518144544, 'y': 0.24140988035291594}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,775]\u001b[0m Trial 185 finished with value: 6.8427675826086825 and parameters: {'x': 0.5121225134354053, 'y': 0.519266417012159}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,789]\u001b[0m Trial 186 finished with value: 14.096838182233723 and parameters: {'x': 0.7662914091574402, 'y': 0.21247304163106043}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,802]\u001b[0m Trial 187 finished with value: 1.0480486134385194 and parameters: {'x': 0.6471377215311777, 'y': 0.3226864072256238}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,814]\u001b[0m Trial 188 finished with value: 0.8278151254642496 and parameters: {'x': 0.5402726346088051, 'y': 0.37040986056322445}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,823]\u001b[0m Trial 189 finished with value: 19.032494770662527 and parameters: {'x': 0.7569538880023663, 'y': 0.13739425587821022}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,836]\u001b[0m Trial 190 finished with value: 12.103768167973856 and parameters: {'x': 0.44819176474467565, 'y': 0.5443765980056672}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,845]\u001b[0m Trial 191 finished with value: 1.419934842968307 and parameters: {'x': 0.356703898796334, 'y': 0.22754245540732349}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,856]\u001b[0m Trial 192 finished with value: 11.46901011428617 and parameters: {'x': 0.6075053118752449, 'y': 0.032685563917663585}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,866]\u001b[0m Trial 193 finished with value: 5.242268812149749 and parameters: {'x': 0.4808942352505404, 'y': 0.4542569788745735}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,879]\u001b[0m Trial 194 finished with value: 6.971783608876737 and parameters: {'x': 0.21522910280209467, 'y': 0.29843303196131676}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,891]\u001b[0m Trial 195 finished with value: 768.7625920842246 and parameters: {'x': 1.6504251491049513, 'y': -0.04799065491182153}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,904]\u001b[0m Trial 196 finished with value: 8.78210167842967 and parameters: {'x': 0.6613328885785902, 'y': 0.14295659943478717}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,913]\u001b[0m Trial 197 finished with value: 14.08540316386988 and parameters: {'x': 0.8714808318671081, 'y': 0.38439370347892976}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,925]\u001b[0m Trial 198 finished with value: 0.42628413004202415 and parameters: {'x': 0.5429651094912519, 'y': 0.2481845898922927}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,935]\u001b[0m Trial 199 finished with value: 7.45808301741011 and parameters: {'x': 0.577764239851085, 'y': 0.603622561477522}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,948]\u001b[0m Trial 200 finished with value: 8.079911211726042 and parameters: {'x': 0.7073171936770196, 'y': 0.21755660047236294}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,958]\u001b[0m Trial 201 finished with value: 1.4073925499391886 and parameters: {'x': 0.4083436670572056, 'y': 0.06391773808439771}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,970]\u001b[0m Trial 202 finished with value: 0.2538635097622107 and parameters: {'x': 0.5300938682851877, 'y': 0.2991796459199463}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,980]\u001b[0m Trial 203 finished with value: 3.517661460800482 and parameters: {'x': 0.5310609916847931, 'y': 0.4636230694703984}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:23:59,990]\u001b[0m Trial 204 finished with value: 13.578612532423271 and parameters: {'x': 0.795688772864135, 'y': 0.2651957742114489}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,000]\u001b[0m Trial 205 finished with value: 6.918190739338104 and parameters: {'x': 0.33138096088752744, 'y': 0.3641976827355088}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,011]\u001b[0m Trial 206 finished with value: 4.4970220341414056 and parameters: {'x': 0.598155518364613, 'y': 0.1495703553691912}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,023]\u001b[0m Trial 207 finished with value: 0.5234206361558738 and parameters: {'x': 0.4788351337333752, 'y': 0.27946354148561736}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,038]\u001b[0m Trial 208 finished with value: 0.3735767446989155 and parameters: {'x': 0.4617155195493564, 'y': 0.24213403826582253}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,050]\u001b[0m Trial 209 finished with value: 0.1914899618890643 and parameters: {'x': 0.7157335510520699, 'y': 0.5455435028447574}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,065]\u001b[0m Trial 210 finished with value: 0.8017435516814179 and parameters: {'x': 0.6949327479580157, 'y': 0.5671145107918079}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,075]\u001b[0m Trial 211 finished with value: 0.32749908754674545 and parameters: {'x': 0.7403711569619285, 'y': 0.49715023914475304}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,087]\u001b[0m Trial 212 finished with value: 11.787348128640664 and parameters: {'x': 0.9382919114535446, 'y': 0.5371200950216063}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,097]\u001b[0m Trial 213 finished with value: 3.0979514570199957 and parameters: {'x': 0.795150826240321, 'y': 0.45745098481611307}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,109]\u001b[0m Trial 214 finished with value: 7.1358311752505985 and parameters: {'x': 0.6883895061299884, 'y': 0.2085740610436116}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,120]\u001b[0m Trial 215 finished with value: 0.9923368977055169 and parameters: {'x': 0.8581021072099762, 'y': 0.6377389279337815}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,134]\u001b[0m Trial 216 finished with value: 9.62038308857682 and parameters: {'x': 0.40573548257077485, 'y': 0.4690425815300416}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,145]\u001b[0m Trial 217 finished with value: 18.874651257385477 and parameters: {'x': 0.7423411174881458, 'y': 0.11738538964803008}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,156]\u001b[0m Trial 218 finished with value: 5.012391577087522 and parameters: {'x': 0.26907090889477087, 'y': 0.2840151798920197}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,167]\u001b[0m Trial 219 finished with value: 0.23292031873522817 and parameters: {'x': 0.6405108974194483, 'y': 0.3780536035291677}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,178]\u001b[0m Trial 220 finished with value: 0.17467514664180955 and parameters: {'x': 0.6331159726156435, 'y': 0.38081802843469037}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,189]\u001b[0m Trial 221 finished with value: 0.5818810616304724 and parameters: {'x': 0.6795890928530157, 'y': 0.3926157681562349}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,200]\u001b[0m Trial 222 finished with value: 0.2900981782726891 and parameters: {'x': 0.6218826836794019, 'y': 0.34838113372615265}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,211]\u001b[0m Trial 223 finished with value: 0.19073676528974043 and parameters: {'x': 0.6154835550026839, 'y': 0.35811158577209057}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,223]\u001b[0m Trial 224 finished with value: 0.1924893034057084 and parameters: {'x': 0.611182086872446, 'y': 0.39386838866048646}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,233]\u001b[0m Trial 225 finished with value: 0.1578613458335196 and parameters: {'x': 0.6304805523310627, 'y': 0.38290547905198946}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,245]\u001b[0m Trial 226 finished with value: 0.2198520597902374 and parameters: {'x': 0.6105416855247027, 'y': 0.39887135487098396}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,255]\u001b[0m Trial 227 finished with value: 0.34932210656105356 and parameters: {'x': 0.6661924204171517, 'y': 0.4925867817022276}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,268]\u001b[0m Trial 228 finished with value: 1.2438451803271606 and parameters: {'x': 0.6316082489890447, 'y': 0.5041968621927954}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,279]\u001b[0m Trial 229 finished with value: 2.9897290377095915 and parameters: {'x': 0.7677253779774925, 'y': 0.4180611477395144}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,292]\u001b[0m Trial 230 finished with value: 0.7830220053973619 and parameters: {'x': 0.6735706200412102, 'y': 0.3714498352164776}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,302]\u001b[0m Trial 231 finished with value: 1.005760007231016 and parameters: {'x': 0.6442719697382455, 'y': 0.5088529735585364}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,314]\u001b[0m Trial 232 finished with value: 0.7147204404364236 and parameters: {'x': 0.5948673756756817, 'y': 0.42806881168390826}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,325]\u001b[0m Trial 233 finished with value: 2.9866918951399737 and parameters: {'x': 0.7216313696426586, 'y': 0.3501879804837107}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,340]\u001b[0m Trial 234 finished with value: 5.003893252838322 and parameters: {'x': 0.618597056984647, 'y': 0.6030806722270612}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,358]\u001b[0m Trial 235 finished with value: 3.2827904602225404 and parameters: {'x': 0.8135691408453221, 'y': 0.481671718579889}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,375]\u001b[0m Trial 236 finished with value: 4.298010551320625 and parameters: {'x': 0.7379399149067086, 'y': 0.33890184580449945}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,388]\u001b[0m Trial 237 finished with value: 6.304190211122473 and parameters: {'x': 0.5553675458675547, 'y': 0.5555462873383015}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,399]\u001b[0m Trial 238 finished with value: 0.14458897979528912 and parameters: {'x': 0.6365301354243522, 'y': 0.41634139543381715}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,410]\u001b[0m Trial 239 finished with value: 0.17087502069326094 and parameters: {'x': 0.6392263188206283, 'y': 0.42878883262196055}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,420]\u001b[0m Trial 240 finished with value: 0.1111801632164576 and parameters: {'x': 0.6665631534335387, 'y': 0.44428839083365274}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,432]\u001b[0m Trial 241 finished with value: 0.16818201223757676 and parameters: {'x': 0.6500897178175996, 'y': 0.4440046768070052}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,446]\u001b[0m Trial 242 finished with value: 0.8547176779849114 and parameters: {'x': 0.6211606378087586, 'y': 0.47017311859700955}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,457]\u001b[0m Trial 243 finished with value: 1.4557410044942976 and parameters: {'x': 0.7414438832649913, 'y': 0.4318878657664459}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,468]\u001b[0m Trial 244 finished with value: 1.7272708334541649 and parameters: {'x': 0.6587402765663258, 'y': 0.5608565456253178}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,482]\u001b[0m Trial 245 finished with value: 0.09371910024114542 and parameters: {'x': 0.8242560747824308, 'y': 0.7044646228463098}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,493]\u001b[0m Trial 246 finished with value: 1.1996589609217525 and parameters: {'x': 0.8889340674319728, 'y': 0.6812394096094606}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,505]\u001b[0m Trial 247 finished with value: 0.43321406777812754 and parameters: {'x': 0.7933322093092472, 'y': 0.6918661929244817}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,518]\u001b[0m Trial 248 finished with value: 0.40057362775243294 and parameters: {'x': 0.8288077257750012, 'y': 0.747853915655998}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,529]\u001b[0m Trial 249 finished with value: 1.1940835272269497 and parameters: {'x': 0.7080631488700341, 'y': 0.6066556735794225}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,543]\u001b[0m Trial 250 finished with value: 1.8449731258521973 and parameters: {'x': 0.6257671719935173, 'y': 0.5221572503957701}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,554]\u001b[0m Trial 251 finished with value: 2.654878817875163 and parameters: {'x': 0.7433551710502743, 'y': 0.39167283176953593}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,567]\u001b[0m Trial 252 finished with value: 3.5418698763965235 and parameters: {'x': 0.5553237095836331, 'y': 0.4912541266727146}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,580]\u001b[0m Trial 253 finished with value: 18.111417798835756 and parameters: {'x': 0.8629477859448302, 'y': 0.3193245056053508}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,592]\u001b[0m Trial 254 finished with value: 0.13446028717998587 and parameters: {'x': 0.6581980834226951, 'y': 0.4199462619138552}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,604]\u001b[0m Trial 255 finished with value: 21.018068734249983 and parameters: {'x': 0.9521729753684772, 'y': 0.44820364965551057}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,616]\u001b[0m Trial 256 finished with value: 1.2233377714358302 and parameters: {'x': 0.6682527242350685, 'y': 0.5520738599253338}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,628]\u001b[0m Trial 257 finished with value: 2.89410017969347 and parameters: {'x': 0.7638870711389665, 'y': 0.4150493981884031}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,642]\u001b[0m Trial 258 finished with value: 4.855509015102171 and parameters: {'x': 0.6256099008074989, 'y': 0.6085361089936684}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,662]\u001b[0m Trial 259 finished with value: 4.254954042594662 and parameters: {'x': 0.8398454236573842, 'y': 0.49968760518540317}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,676]\u001b[0m Trial 260 finished with value: 2.4859531056394135 and parameters: {'x': 0.7177461348385862, 'y': 0.3600374375926949}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,688]\u001b[0m Trial 261 finished with value: 25.59924063892301 and parameters: {'x': 0.5507283756465213, 'y': 0.8072600321597975}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,700]\u001b[0m Trial 262 finished with value: 0.13313939231546904 and parameters: {'x': 0.6463320847198636, 'y': 0.4087683057039839}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,713]\u001b[0m Trial 263 finished with value: 8.02704237494073 and parameters: {'x': 0.6002471237012487, 'y': 0.640782616521448}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,726]\u001b[0m Trial 264 finished with value: 2.472138419728878 and parameters: {'x': 0.781805172492239, 'y': 0.455510322312756}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,738]\u001b[0m Trial 265 finished with value: 1.87538441187058 and parameters: {'x': 0.6655946659772156, 'y': 0.575815261047051}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,749]\u001b[0m Trial 266 finished with value: 0.296479747638382 and parameters: {'x': 0.5353631365129464, 'y': 0.31500247669720954}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,761]\u001b[0m Trial 267 finished with value: 0.5101024017841103 and parameters: {'x': 0.5150295691638169, 'y': 0.31768694412217974}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,773]\u001b[0m Trial 268 finished with value: 2.429835937103958 and parameters: {'x': 0.4936672024533163, 'y': 0.3911340016210633}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,785]\u001b[0m Trial 269 finished with value: 0.19118798031906117 and parameters: {'x': 0.5699879512369224, 'y': 0.3328094067587348}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,798]\u001b[0m Trial 270 finished with value: 0.2021858940492091 and parameters: {'x': 0.5623649844994216, 'y': 0.3059289279765244}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,809]\u001b[0m Trial 271 finished with value: 0.21662392433541564 and parameters: {'x': 0.5858477022440106, 'y': 0.32198034618365295}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,822]\u001b[0m Trial 272 finished with value: 0.24100673533542383 and parameters: {'x': 0.5905314187539267, 'y': 0.32164558844188884}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,833]\u001b[0m Trial 273 finished with value: 0.31493267406047576 and parameters: {'x': 0.5853460111630041, 'y': 0.30481530699712023}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,843]\u001b[0m Trial 274 finished with value: 0.6671307255712347 and parameters: {'x': 0.7047004775189893, 'y': 0.42044969888845063}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,858]\u001b[0m Trial 275 finished with value: 0.5929571584388609 and parameters: {'x': 0.5794286059207288, 'y': 0.4002415294603987}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,873]\u001b[0m Trial 276 finished with value: 13.775826271791178 and parameters: {'x': 0.8221995886295822, 'y': 0.30528027544145925}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,886]\u001b[0m Trial 277 finished with value: 2.3033448511795025 and parameters: {'x': 0.9434151100478458, 'y': 0.738369846442013}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,902]\u001b[0m Trial 278 finished with value: 0.35504789915392937 and parameters: {'x': 0.4765721732843581, 'y': 0.2555940427539327}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,914]\u001b[0m Trial 279 finished with value: 0.3645914815574815 and parameters: {'x': 0.6943305886975051, 'y': 0.4300221623137206}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,927]\u001b[0m Trial 280 finished with value: 2.2372837698637045 and parameters: {'x': 0.6453626451108412, 'y': 0.5618035116746173}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,939]\u001b[0m Trial 281 finished with value: 0.19990403394780837 and parameters: {'x': 0.5850433460068232, 'y': 0.35892354216683925}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,951]\u001b[0m Trial 282 finished with value: 0.5297178634212538 and parameters: {'x': 0.7438139002517073, 'y': 0.4851352203657826}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,965]\u001b[0m Trial 283 finished with value: 0.14913465662269437 and parameters: {'x': 0.6138248881880994, 'y': 0.3769664560173812}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,977]\u001b[0m Trial 284 finished with value: 15.399391518084878 and parameters: {'x': 0.8871220112251048, 'y': 0.3947272560314337}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:00,989]\u001b[0m Trial 285 finished with value: 5.096828398108861 and parameters: {'x': 0.7988709324113019, 'y': 0.8630586253813743}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,003]\u001b[0m Trial 286 finished with value: 7.419391216617596 and parameters: {'x': 0.6252169893601808, 'y': 0.6606911857296479}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,014]\u001b[0m Trial 287 finished with value: 0.11940298137224667 and parameters: {'x': 0.7135299799778589, 'y': 0.5284480520189498}. Best is trial 137 with value: 0.08554825248031783.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,025]\u001b[0m Trial 288 finished with value: 0.06653490010711095 and parameters: {'x': 0.7432059426177829, 'y': 0.5499225596269273}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,041]\u001b[0m Trial 289 finished with value: 29.56237781145005 and parameters: {'x': 1.0585835946224615, 'y': 0.5769178332111017}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,056]\u001b[0m Trial 290 finished with value: 2.0543990546942132 and parameters: {'x': 0.8187436102707875, 'y': 0.5281600466174469}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,068]\u001b[0m Trial 291 finished with value: 1.6015628853551271 and parameters: {'x': 0.7442283786556498, 'y': 0.6778171464557652}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,080]\u001b[0m Trial 292 finished with value: 4.310525563997262 and parameters: {'x': 0.9175849174569068, 'y': 0.6345076684469814}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,091]\u001b[0m Trial 293 finished with value: 7.571177410576776 and parameters: {'x': 0.49027736545287215, 'y': 0.5107671658625867}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,105]\u001b[0m Trial 294 finished with value: 1.0357584598429141 and parameters: {'x': 0.7519117772592261, 'y': 0.4666692086669368}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,119]\u001b[0m Trial 295 finished with value: 16.86113565405091 and parameters: {'x': 0.5766024856909371, 'y': 0.7409048790053387}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,130]\u001b[0m Trial 296 finished with value: 0.4326669302404274 and parameters: {'x': 0.69178234408048, 'y': 0.5366720885361923}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,142]\u001b[0m Trial 297 finished with value: 14.551902725372246 and parameters: {'x': 1.0079450152803424, 'y': 0.6344844185158242}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,156]\u001b[0m Trial 298 finished with value: 3.8962812039770127 and parameters: {'x': 0.800141247962976, 'y': 0.4438504152741791}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,169]\u001b[0m Trial 299 finished with value: 5.386528982537213 and parameters: {'x': 0.5825563830581963, 'y': 0.5676758993113991}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,181]\u001b[0m Trial 300 finished with value: 1.8975386558367064 and parameters: {'x': 0.6967083089315128, 'y': 0.3510316075760057}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,192]\u001b[0m Trial 301 finished with value: 5.191967711894269 and parameters: {'x': 0.4695448393070488, 'y': 0.44207075490445125}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,206]\u001b[0m Trial 302 finished with value: 24.321349335797176 and parameters: {'x': 0.872795429426179, 'y': 0.2687691402044931}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,221]\u001b[0m Trial 303 finished with value: 572.0496636542588 and parameters: {'x': 0.6472466876074678, 'y': -1.972567551751876}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,233]\u001b[0m Trial 304 finished with value: 2.6806226195137848 and parameters: {'x': 0.7436275613578471, 'y': 0.3912755558202366}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,245]\u001b[0m Trial 305 finished with value: 5.260944038560403 and parameters: {'x': 0.5601245115780327, 'y': 0.5388495214056649}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,262]\u001b[0m Trial 306 finished with value: 6.972046497562755 and parameters: {'x': 0.46410897414083263, 'y': 0.4739482432593096}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,277]\u001b[0m Trial 307 finished with value: 3.7567513723827632 and parameters: {'x': 0.6563912352686815, 'y': 0.6216028125332056}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,289]\u001b[0m Trial 308 finished with value: 15.921876046151292 and parameters: {'x': 0.811573871622489, 'y': 0.2600750343496578}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,300]\u001b[0m Trial 309 finished with value: 0.4190048808212907 and parameters: {'x': 0.541666982932302, 'y': 0.33911260806696164}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,324]\u001b[0m Trial 310 finished with value: 1.2306237863673204 and parameters: {'x': 0.7123596815291207, 'y': 0.40031683511736815}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,339]\u001b[0m Trial 311 finished with value: 8.846474760943128 and parameters: {'x': 0.6398999519938128, 'y': 0.7047142679599888}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,352]\u001b[0m Trial 312 finished with value: 166.0536950411379 and parameters: {'x': 0.5858624303067568, 'y': -0.9447177938484271}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,364]\u001b[0m Trial 313 finished with value: 6.076937645832973 and parameters: {'x': 0.853853424056006, 'y': 0.48298481265051085}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,378]\u001b[0m Trial 314 finished with value: 36.34294075582735 and parameters: {'x': -1.065886305119626, 'y': 0.5697651844368861}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,390]\u001b[0m Trial 315 finished with value: 11.566165989305034 and parameters: {'x': 0.7535653501761442, 'y': 0.22866409854946132}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,401]\u001b[0m Trial 316 finished with value: 1.4818163910967064 and parameters: {'x': 0.49572982148016115, 'y': 0.3565419175646962}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,414]\u001b[0m Trial 317 finished with value: 0.09319477610188458 and parameters: {'x': 0.694741163653951, 'y': 0.4823214977020761}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,427]\u001b[0m Trial 318 finished with value: 10.39018757704416 and parameters: {'x': 0.6861706363712219, 'y': 0.7916369175138802}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,440]\u001b[0m Trial 319 finished with value: 2.757315961423648 and parameters: {'x': 0.8148482755884272, 'y': 0.4989615069533736}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,452]\u001b[0m Trial 320 finished with value: 6.632354306585327 and parameters: {'x': 0.9297891683897059, 'y': 0.6070700460146645}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,465]\u001b[0m Trial 321 finished with value: 1.007603241904989 and parameters: {'x': 0.7434159261180391, 'y': 0.4556225147261883}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,480]\u001b[0m Trial 322 finished with value: 0.29918778911454796 and parameters: {'x': 0.5753882136491982, 'y': 0.2965907872502298}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,493]\u001b[0m Trial 323 finished with value: 0.5345323396174939 and parameters: {'x': 0.6912385688222569, 'y': 0.5440828284258368}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,506]\u001b[0m Trial 324 finished with value: 1.3702315453501734 and parameters: {'x': 0.8715165997101396, 'y': 0.6431915569615491}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,519]\u001b[0m Trial 325 finished with value: 0.1409207575014073 and parameters: {'x': 0.6247292207749845, 'y': 0.3893243115927181}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,529]\u001b[0m Trial 326 finished with value: 2.122297482055431 and parameters: {'x': 0.769877755831179, 'y': 0.4488597087194883}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,543]\u001b[0m Trial 327 finished with value: 5.466056750339055 and parameters: {'x': -0.8240742650165985, 'y': 0.532851690845332}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,556]\u001b[0m Trial 328 finished with value: 0.8947910376668239 and parameters: {'x': 0.688369187020551, 'y': 0.38453935725105}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,569]\u001b[0m Trial 329 finished with value: 20.39638863725636 and parameters: {'x': 0.5150811604652875, 'y': 0.714321322803221}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,582]\u001b[0m Trial 330 finished with value: 4.1742777822894945 and parameters: {'x': 0.6450988732391433, 'y': 0.21494810867649786}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,595]\u001b[0m Trial 331 finished with value: 3.0218057757012424 and parameters: {'x': 0.789199558635426, 'y': 0.4502854014902416}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,608]\u001b[0m Trial 332 finished with value: 11.153121231032902 and parameters: {'x': 0.9717817099970558, 'y': 0.610408723041437}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,621]\u001b[0m Trial 333 finished with value: 2.536225087473035 and parameters: {'x': 0.4644688796825187, 'y': 0.36571238921788707}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,633]\u001b[0m Trial 334 finished with value: 1.4559805194515258 and parameters: {'x': 0.6135926077016196, 'y': 0.4908055486502343}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,649]\u001b[0m Trial 335 finished with value: 5.1938356326245305 and parameters: {'x': 0.715309089470048, 'y': 0.2855523721955293}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,662]\u001b[0m Trial 336 finished with value: 3.064570369299815 and parameters: {'x': 0.8373978739019651, 'y': 0.5269328493206371}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,676]\u001b[0m Trial 337 finished with value: 1.282896774700633 and parameters: {'x': 0.548991257541682, 'y': 0.40528980743433407}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,692]\u001b[0m Trial 338 finished with value: 29.499480080268782 and parameters: {'x': 0.6296966441188926, 'y': 0.9383882863624324}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,707]\u001b[0m Trial 339 finished with value: 16.79283865996379 and parameters: {'x': 0.4347400385970549, 'y': 0.5948722784089555}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,719]\u001b[0m Trial 340 finished with value: 1.872712355299271 and parameters: {'x': 0.7419663683616365, 'y': 0.6849064650842996}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,732]\u001b[0m Trial 341 finished with value: 19.477448331400222 and parameters: {'x': 0.9061696609383437, 'y': 0.3799105884616091}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,742]\u001b[0m Trial 342 finished with value: 4.95730941723991 and parameters: {'x': 0.6768915366503254, 'y': 0.2378889310189628}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,755]\u001b[0m Trial 343 finished with value: 3.2193243233321662 and parameters: {'x': 0.5365025656578717, 'y': 0.461169778801521}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,767]\u001b[0m Trial 344 finished with value: 12.901361745654434 and parameters: {'x': 0.8243078174668743, 'y': 0.3207286706138618}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,781]\u001b[0m Trial 345 finished with value: 231.32593208202482 and parameters: {'x': 0.6251223179113172, 'y': -1.129700297968214}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,794]\u001b[0m Trial 346 finished with value: 0.1306413879913857 and parameters: {'x': 0.7448233529405809, 'y': 0.5291637281744217}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,805]\u001b[0m Trial 347 finished with value: 0.21491376808368762 and parameters: {'x': 0.7726818854856803, 'y': 0.5566342959105285}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,819]\u001b[0m Trial 348 finished with value: 1.6738833460182565 and parameters: {'x': 0.7223835083228809, 'y': 0.6482029769012085}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,832]\u001b[0m Trial 349 finished with value: 4.722765794998636 and parameters: {'x': 0.8837114461712802, 'y': 0.5639380223750188}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,845]\u001b[0m Trial 350 finished with value: 4.360758235572339 and parameters: {'x': 1.0115324632267504, 'y': 0.8143768227885535}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,858]\u001b[0m Trial 351 finished with value: 2.3233753373657264 and parameters: {'x': 0.7988988969529797, 'y': 0.487145646100775}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,869]\u001b[0m Trial 352 finished with value: 5.148323132578218 and parameters: {'x': 0.6876160368095416, 'y': 0.6975543152767132}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,886]\u001b[0m Trial 353 finished with value: 69.53073829900687 and parameters: {'x': 1.1253592129017833, 'y': 0.4326766587390268}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,901]\u001b[0m Trial 354 finished with value: 1.335124583997205 and parameters: {'x': 0.6420609668207788, 'y': 0.5221060294768352}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,914]\u001b[0m Trial 355 finished with value: 0.3415967439976061 and parameters: {'x': 0.7414292120138779, 'y': 0.6021327218227316}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,926]\u001b[0m Trial 356 finished with value: 10.551040186395507 and parameters: {'x': 0.848308038332752, 'y': 0.3951572730365611}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,939]\u001b[0m Trial 357 finished with value: 1.5911311890683892 and parameters: {'x': 0.5868788930711939, 'y': 0.463609977400087}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,952]\u001b[0m Trial 358 finished with value: 1.517850982131754 and parameters: {'x': 0.5093514033541158, 'y': 0.37244836217389676}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,965]\u001b[0m Trial 359 finished with value: 7.3996539282718565 and parameters: {'x': 0.6953551908717723, 'y': 0.7538306145889015}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,978]\u001b[0m Trial 360 finished with value: 1.0203373898031607 and parameters: {'x': 0.7770744275338524, 'y': 0.5053235220571416}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:01,989]\u001b[0m Trial 361 finished with value: 4.442717841448347 and parameters: {'x': 0.6378563173028138, 'y': 0.6145038801723982}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,004]\u001b[0m Trial 362 finished with value: 1.5901848833137624 and parameters: {'x': 0.4472954427630651, 'y': 0.3134179331402267}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,023]\u001b[0m Trial 363 finished with value: 16.782435191345865 and parameters: {'x': 0.9033584781223127, 'y': 0.4065068408429036}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,037]\u001b[0m Trial 364 finished with value: 4.61821329606031 and parameters: {'x': 0.575810079855723, 'y': 0.5422294153686845}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,049]\u001b[0m Trial 365 finished with value: 9.714525836552484 and parameters: {'x': 0.7233594834595612, 'y': 0.21279772454614773}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,063]\u001b[0m Trial 366 finished with value: 4.522202564011749 and parameters: {'x': 0.8119488523038806, 'y': 0.4474393315060641}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,076]\u001b[0m Trial 367 finished with value: 382.53514006238726 and parameters: {'x': 0.6527434487711449, 'y': -1.5294682497934027}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,090]\u001b[0m Trial 368 finished with value: 747.953739990631 and parameters: {'x': -1.8386827099718592, 'y': 0.6606518663733499}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,105]\u001b[0m Trial 369 finished with value: 0.994127185254029 and parameters: {'x': 0.4910004142919615, 'y': 0.32681626596481833}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,118]\u001b[0m Trial 370 finished with value: 0.16948869899865615 and parameters: {'x': 0.7361672696695657, 'y': 0.5103382951603043}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,131]\u001b[0m Trial 371 finished with value: 12.24156099309647 and parameters: {'x': 0.9626017198660686, 'y': 0.576742637005373}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,144]\u001b[0m Trial 372 finished with value: 0.8730629823961427 and parameters: {'x': 0.7756599309418313, 'y': 0.5109436143788929}. Best is trial 288 with value: 0.06653490010711095.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,156]\u001b[0m Trial 373 finished with value: 0.03140490176980244 and parameters: {'x': 0.8579022697123486, 'y': 0.7465855143655542}. Best is trial 373 with value: 0.03140490176980244.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,170]\u001b[0m Trial 374 finished with value: 0.008741523367406493 and parameters: {'x': 0.9065572014659549, 'y': 0.8221616614387478}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,182]\u001b[0m Trial 375 finished with value: 1.3662506857364822 and parameters: {'x': 1.065435610517285, 'y': 1.0184496183313554}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,196]\u001b[0m Trial 376 finished with value: 0.4448688011041955 and parameters: {'x': 0.9731300948447047, 'y': 0.8803378412000474}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,209]\u001b[0m Trial 377 finished with value: 0.06773205406057095 and parameters: {'x': 0.9041888140215901, 'y': 0.841754987871735}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,223]\u001b[0m Trial 378 finished with value: 15.598031405518855 and parameters: {'x': 1.0129199387494439, 'y': 0.6310654841345823}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,234]\u001b[0m Trial 379 finished with value: 1.7168623351245873 and parameters: {'x': 0.8929854264112121, 'y': 0.928014327398881}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,249]\u001b[0m Trial 380 finished with value: 0.010094023083472542 and parameters: {'x': 0.9443167942884942, 'y': 0.8833715506911795}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,262]\u001b[0m Trial 381 finished with value: 3.188572071555573 and parameters: {'x': 1.0649087279216942, 'y': 0.9555828773670966}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,278]\u001b[0m Trial 382 finished with value: 2.0298647552281657 and parameters: {'x': 0.9798784780724864, 'y': 0.8177027190736351}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,292]\u001b[0m Trial 383 finished with value: 0.048151762651864535 and parameters: {'x': 0.9171287070173618, 'y': 0.861443557170334}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,306]\u001b[0m Trial 384 finished with value: 26.23958302607245 and parameters: {'x': 1.1884852471303642, 'y': 0.900598204940114}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,319]\u001b[0m Trial 385 finished with value: 3.484091473782627 and parameters: {'x': 0.9346356960150788, 'y': 1.0600866134604643}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,333]\u001b[0m Trial 386 finished with value: 11.055508089433399 and parameters: {'x': 1.0931561721273808, 'y': 0.8626226983566804}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,347]\u001b[0m Trial 387 finished with value: 0.010660212496835401 and parameters: {'x': 0.913818558837312, 'y': 0.8293784357746928}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,360]\u001b[0m Trial 388 finished with value: 0.9651116500958637 and parameters: {'x': 0.9465617900596855, 'y': 0.994073870971356}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,373]\u001b[0m Trial 389 finished with value: 5.500660470042315 and parameters: {'x': 1.019656338671294, 'y': 0.8051724172768231}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,386]\u001b[0m Trial 390 finished with value: 0.10210381213620426 and parameters: {'x': 0.906868509825691, 'y': 0.7918441175414718}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,401]\u001b[0m Trial 391 finished with value: 21.957538793561 and parameters: {'x': 1.1525075641208762, 'y': 0.859933208390125}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,418]\u001b[0m Trial 392 finished with value: 20.767260929802305 and parameters: {'x': 1.2912105762876642, 'y': 1.212445054770491}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,433]\u001b[0m Trial 393 finished with value: 0.06692961745771246 and parameters: {'x': 0.8958682227560469, 'y': 0.7788973493622615}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,446]\u001b[0m Trial 394 finished with value: 0.07063949394298515 and parameters: {'x': 0.9086105233804851, 'y': 0.8006156278975494}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,459]\u001b[0m Trial 395 finished with value: 5.844796190267158 and parameters: {'x': 1.0079987977374623, 'y': 0.7743027664833808}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,471]\u001b[0m Trial 396 finished with value: 0.27711340701672305 and parameters: {'x': 0.9153431285020039, 'y': 0.8898094304843602}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,486]\u001b[0m Trial 397 finished with value: 13.858780964936013 and parameters: {'x': 1.0789433731662754, 'y': 0.7919286775797885}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,505]\u001b[0m Trial 398 finished with value: 3.7396710865611524 and parameters: {'x': 0.8905593609462799, 'y': 0.9861683408977431}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,518]\u001b[0m Trial 399 finished with value: 2.2074850205193974 and parameters: {'x': 0.9919602577291124, 'y': 0.8354112526803654}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,530]\u001b[0m Trial 400 finished with value: 2.424656297482259 and parameters: {'x': 0.8864455055027126, 'y': 0.9410841105308393}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,548]\u001b[0m Trial 401 finished with value: 1.4466041136277705 and parameters: {'x': 0.9351475603792989, 'y': 0.7544010731428996}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,561]\u001b[0m Trial 402 finished with value: 0.02191553551120382 and parameters: {'x': 0.8542774585085159, 'y': 0.732398570397114}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,575]\u001b[0m Trial 403 finished with value: 0.09102022541440921 and parameters: {'x': 0.8754483643704781, 'y': 0.7389312805878774}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,588]\u001b[0m Trial 404 finished with value: 2.1365005301794313 and parameters: {'x': 1.0033725920883116, 'y': 0.8605892176121355}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,604]\u001b[0m Trial 405 finished with value: 12.472115612834807 and parameters: {'x': 0.8723327936064489, 'y': 1.1138924940012278}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,617]\u001b[0m Trial 406 finished with value: 25.309264944979805 and parameters: {'x': 1.1296534577644026, 'y': 0.7732008887042695}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,630]\u001b[0m Trial 407 finished with value: 3.909421934990347 and parameters: {'x': 0.963927547922715, 'y': 0.7314666438322478}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,642]\u001b[0m Trial 408 finished with value: 3.6613954665976878 and parameters: {'x': 1.0479862593404843, 'y': 0.9069876469693121}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,656]\u001b[0m Trial 409 finished with value: 0.309447350830802 and parameters: {'x': 0.8525651131772946, 'y': 0.7805059061361483}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,671]\u001b[0m Trial 410 finished with value: 0.03229728162503097 and parameters: {'x': 0.9262217907419242, 'y': 0.8414995979929554}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,685]\u001b[0m Trial 411 finished with value: 2.6854414580264723 and parameters: {'x': 0.9235900302153744, 'y': 1.0167134728067113}. Best is trial 374 with value: 0.008741523367406493.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,698]\u001b[0m Trial 412 finished with value: 0.004203700740006983 and parameters: {'x': 0.9358717054777038, 'y': 0.8768111636256667}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,714]\u001b[0m Trial 413 finished with value: 1.9059010268742504 and parameters: {'x': 1.004280347459062, 'y': 0.8705253055310708}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,731]\u001b[0m Trial 414 finished with value: 6.829658290425437 and parameters: {'x': 1.0497188449372246, 'y': 0.8406208031589381}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,749]\u001b[0m Trial 415 finished with value: 71.53593970945536 and parameters: {'x': 1.248778568927892, 'y': 0.7140246537511348}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,761]\u001b[0m Trial 416 finished with value: 1.2400666907362043 and parameters: {'x': 0.9271498953313352, 'y': 0.9707266635181502}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,777]\u001b[0m Trial 417 finished with value: 0.366666300472774 and parameters: {'x': 0.8511994873403304, 'y': 0.7832367943763404}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,792]\u001b[0m Trial 418 finished with value: 19.81245824132622 and parameters: {'x': 1.1375208831519499, 'y': 0.8490543749362429}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,806]\u001b[0m Trial 419 finished with value: 3.277396860008847 and parameters: {'x': 0.9514760342590762, 'y': 0.7243358647812334}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,818]\u001b[0m Trial 420 finished with value: 5.271003438949821 and parameters: {'x': 0.841269754708105, 'y': 0.936772092961838}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,833]\u001b[0m Trial 421 finished with value: 0.017753928924870162 and parameters: {'x': 0.877870153636883, 'y': 0.7653285029606725}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,847]\u001b[0m Trial 422 finished with value: 0.007473357682763065 and parameters: {'x': 1.0326792526373327, 'y': 1.0744298281907565}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,860]\u001b[0m Trial 423 finished with value: 82.18054727641695 and parameters: {'x': -1.4256336541790644, 'y': 1.1589506426893803}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,875]\u001b[0m Trial 424 finished with value: 0.4166775781779291 and parameters: {'x': 1.0953393660112045, 'y': 1.2636109444328623}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,889]\u001b[0m Trial 425 finished with value: 11.932038681947194 and parameters: {'x': 1.1971241496687903, 'y': 1.0882413177392496}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,902]\u001b[0m Trial 426 finished with value: 2.4261878277629982 and parameters: {'x': 1.0405700974787833, 'y': 0.9270767223043428}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,919]\u001b[0m Trial 427 finished with value: 0.8691545085835677 and parameters: {'x': 0.9519645490303198, 'y': 0.813131878448555}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,933]\u001b[0m Trial 428 finished with value: 1.4568458584375585 and parameters: {'x': 0.9165916409037727, 'y': 0.719728903482501}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,948]\u001b[0m Trial 429 finished with value: 0.006494902722852466 and parameters: {'x': 1.0253735821350645, 'y': 1.0437417450356514}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,960]\u001b[0m Trial 430 finished with value: 2.120084004341213 and parameters: {'x': 1.0890707995372824, 'y': 1.040742815073352}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,977]\u001b[0m Trial 431 finished with value: 0.005088929901148124 and parameters: {'x': 1.00933042263941, 'y': 1.02582029424527}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:02,992]\u001b[0m Trial 432 finished with value: 0.2142226570311356 and parameters: {'x': 1.0200917849862507, 'y': 0.9943466856554992}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,006]\u001b[0m Trial 433 finished with value: 8.740855029311827 and parameters: {'x': 1.1694032295588155, 'y': 1.072340270976388}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,021]\u001b[0m Trial 434 finished with value: 13.18308296539086 and parameters: {'x': 1.0014186458559435, 'y': 1.3659244313777632}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,036]\u001b[0m Trial 435 finished with value: 0.7623961853790262 and parameters: {'x': 1.0876137172580949, 'y': 1.0960289744046288}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,051]\u001b[0m Trial 436 finished with value: 0.046640221109012676 and parameters: {'x': 0.9518653501934164, 'y': 0.8849945509226498}. Best is trial 412 with value: 0.004203700740006983.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,066]\u001b[0m Trial 437 finished with value: 0.0024241819969246636 and parameters: {'x': 0.9577771304758945, 'y': 0.9148044217714888}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,080]\u001b[0m Trial 438 finished with value: 1.272195226500639 and parameters: {'x': 1.0189190231494774, 'y': 0.9254202116256958}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,093]\u001b[0m Trial 439 finished with value: 6.883491853303017 and parameters: {'x': 0.9534465403692949, 'y': 1.171383095596845}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,111]\u001b[0m Trial 440 finished with value: 9.84454472987443 and parameters: {'x': 1.147260316714041, 'y': 1.0027918260707456}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,125]\u001b[0m Trial 441 finished with value: 165.52208086742672 and parameters: {'x': 1.468395007471656, 'y': 0.8704829724628367}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,142]\u001b[0m Trial 442 finished with value: 2.0747225006899344 and parameters: {'x': 0.9029808566182037, 'y': 0.959086284497872}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,156]\u001b[0m Trial 443 finished with value: 46.64674244543063 and parameters: {'x': 1.249025731892378, 'y': 0.8775352163658452}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,170]\u001b[0m Trial 444 finished with value: 16.850821260003517 and parameters: {'x': 1.0988175299420062, 'y': 0.797021402828395}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,186]\u001b[0m Trial 445 finished with value: 0.32576165244832755 and parameters: {'x': 0.9824468207305588, 'y': 1.0222502908949882}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,199]\u001b[0m Trial 446 finished with value: 0.9283843322807912 and parameters: {'x': 0.893541632739271, 'y': 0.8941794265660632}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,215]\u001b[0m Trial 447 finished with value: 8.577267244261646 and parameters: {'x': 1.0478712465029902, 'y': 0.8052035567882012}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,230]\u001b[0m Trial 448 finished with value: 14.974812947402482 and parameters: {'x': 0.8680882643797722, 'y': 1.1403253729705822}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,247]\u001b[0m Trial 449 finished with value: 0.004454069568577615 and parameters: {'x': 0.9753450577254993, 'y': 0.9574997583024302}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,264]\u001b[0m Trial 450 finished with value: 0.44513569304705264 and parameters: {'x': 0.9793492051853863, 'y': 1.0258113889025084}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,278]\u001b[0m Trial 451 finished with value: 3.7438145552799793 and parameters: {'x': 1.0649183512752145, 'y': 0.9406706366417787}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,292]\u001b[0m Trial 452 finished with value: 109.47591168953053 and parameters: {'x': 1.3725173431649655, 'y': 0.8381598331648614}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,307]\u001b[0m Trial 453 finished with value: 2.3609026170425955 and parameters: {'x': 0.9674635493563516, 'y': 1.089603556722671}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,320]\u001b[0m Trial 454 finished with value: 46.97685753624231 and parameters: {'x': 1.198400511041568, 'y': 0.7510543427062817}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,337]\u001b[0m Trial 455 finished with value: 1.8809770352138253 and parameters: {'x': 0.8964325651514596, 'y': 0.9403484578161974}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,352]\u001b[0m Trial 456 finished with value: 4.948050315832011 and parameters: {'x': 1.0335948940057453, 'y': 0.8459016407325317}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,366]\u001b[0m Trial 457 finished with value: 0.049853845733460744 and parameters: {'x': 0.866125766333422, 'y': 0.7323044460005301}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,382]\u001b[0m Trial 458 finished with value: 2.3462984375297076 and parameters: {'x': 0.9416595847533872, 'y': 0.7336575968037506}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,397]\u001b[0m Trial 459 finished with value: 6.86298834286907 and parameters: {'x': 0.8546226312992437, 'y': 0.9919492163505959}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,414]\u001b[0m Trial 460 finished with value: 0.015331050739375197 and parameters: {'x': 1.0993862671161914, 'y': 1.2160348922455975}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,429]\u001b[0m Trial 461 finished with value: 0.5839164238470835 and parameters: {'x': 1.1472739800900666, 'y': 1.2412558010280643}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,444]\u001b[0m Trial 462 finished with value: 0.16537187311367937 and parameters: {'x': 1.125558636962233, 'y': 1.228203193830264}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,458]\u001b[0m Trial 463 finished with value: 10.23428570042471 and parameters: {'x': 1.03502994675767, 'y': 1.3911785129744585}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,472]\u001b[0m Trial 464 finished with value: 2.4803036549843536 and parameters: {'x': 0.9819097091413068, 'y': 1.1216260849681312}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,487]\u001b[0m Trial 465 finished with value: 7.33561910197999 and parameters: {'x': 1.0773025732477517, 'y': 0.8898476923921586}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,501]\u001b[0m Trial 466 finished with value: 27.76771660226767 and parameters: {'x': 1.2574999372297833, 'y': 1.0549847987711987}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,517]\u001b[0m Trial 467 finished with value: 34.776982098148174 and parameters: {'x': 0.8463957564966531, 'y': 1.3059058172801992}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,532]\u001b[0m Trial 468 finished with value: 0.16130665736531483 and parameters: {'x': 0.9621599209619912, 'y': 0.9657360586651238}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,547]\u001b[0m Trial 469 finished with value: 1.2136883777369252 and parameters: {'x': 0.9092374179121744, 'y': 0.7169196709045771}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,562]\u001b[0m Trial 470 finished with value: 33.83576104028616 and parameters: {'x': 1.191362702638458, 'y': 0.837974799695063}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,577]\u001b[0m Trial 471 finished with value: 4.364928876675444 and parameters: {'x': 1.044634372374348, 'y': 0.882384533603128}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,592]\u001b[0m Trial 472 finished with value: 0.5926606242308449 and parameters: {'x': 0.8329461269426545, 'y': 0.7689493420538748}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,605]\u001b[0m Trial 473 finished with value: 36.53298267083601 and parameters: {'x': 0.9656779084012339, 'y': 1.5369492814188488}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,622]\u001b[0m Trial 474 finished with value: 7.081084093858389 and parameters: {'x': 0.8710008927608753, 'y': 1.0244327605061996}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,637]\u001b[0m Trial 475 finished with value: 1.1575293898745775 and parameters: {'x': 1.1062344417293888, 'y': 1.1166918718776575}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,651]\u001b[0m Trial 476 finished with value: 2.758295503854837 and parameters: {'x': 1.0150054120568484, 'y': 1.1963103777112793}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,665]\u001b[0m Trial 477 finished with value: 0.39784551866543627 and parameters: {'x': 0.9292788253818306, 'y': 0.9262364066321269}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,679]\u001b[0m Trial 478 finished with value: 0.04684784193100634 and parameters: {'x': 0.855891859238004, 'y': 0.7164013590214442}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,695]\u001b[0m Trial 479 finished with value: 3.742097563880133 and parameters: {'x': 1.0014212617259715, 'y': 0.8093995759681729}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,715]\u001b[0m Trial 480 finished with value: 29.628712403693076 and parameters: {'x': 1.1176778145156416, 'y': 0.7050082878951144}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,732]\u001b[0m Trial 481 finished with value: 5.544768624183796 and parameters: {'x': 0.8409510483499826, 'y': 0.9421342325294285}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,746]\u001b[0m Trial 482 finished with value: 0.005982472743019463 and parameters: {'x': 0.9269396242413839, 'y': 0.856678062780354}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,766]\u001b[0m Trial 483 finished with value: 0.02460746496040688 and parameters: {'x': 0.9293774254782865, 'y': 0.8777495103712036}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,783]\u001b[0m Trial 484 finished with value: 6.7695055423238735 and parameters: {'x': 1.0646110928376469, 'y': 0.8732942809464065}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,797]\u001b[0m Trial 485 finished with value: 286.1711341212695 and parameters: {'x': 0.9505801931317084, 'y': -0.7880494232057694}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,813]\u001b[0m Trial 486 finished with value: 11.294205220496401 and parameters: {'x': 1.1712812273337345, 'y': 1.0362679515738857}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,828]\u001b[0m Trial 487 finished with value: 0.11516129376637263 and parameters: {'x': 1.0077874805832785, 'y': 0.9817091194777896}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,843]\u001b[0m Trial 488 finished with value: 0.0945701986240119 and parameters: {'x': 0.9281351375708455, 'y': 0.8315340576552172}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,858]\u001b[0m Trial 489 finished with value: 1.901381436841084 and parameters: {'x': 1.0293464648660318, 'y': 0.9216947880732833}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,873]\u001b[0m Trial 490 finished with value: 8.366980035500843 and parameters: {'x': 0.9188197067119972, 'y': 1.1333730399390987}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,888]\u001b[0m Trial 491 finished with value: 12.8572510393206 and parameters: {'x': 1.0842844981473003, 'y': 0.8172018536937543}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,901]\u001b[0m Trial 492 finished with value: 5.788079201074478 and parameters: {'x': 0.8704391454748664, 'y': 0.9978994654760596}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,917]\u001b[0m Trial 493 finished with value: 0.21146047405338786 and parameters: {'x': 0.9692036852035727, 'y': 0.8934741900387703}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,932]\u001b[0m Trial 494 finished with value: 13.918920633533334 and parameters: {'x': 0.834295434771125, 'y': 1.0687613974988301}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,948]\u001b[0m Trial 495 finished with value: 23.862887493848245 and parameters: {'x': 1.1257764307277789, 'y': 0.7790379757950794}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,963]\u001b[0m Trial 496 finished with value: 0.8340009095291232 and parameters: {'x': 0.980112649849173, 'y': 0.8693188128327207}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,978]\u001b[0m Trial 497 finished with value: 33.00130130528975 and parameters: {'x': 1.2374634830057238, 'y': 0.9573392834040328}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:03,993]\u001b[0m Trial 498 finished with value: 0.2075586937621096 and parameters: {'x': 0.9065697547178955, 'y': 0.7772784204361802}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,006]\u001b[0m Trial 499 finished with value: 698.9344299136941 and parameters: {'x': 1.954717122508051, 'y': 1.1789066423601047}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,023]\u001b[0m Trial 500 finished with value: 15.418362218651039 and parameters: {'x': 1.0552285648575626, 'y': 0.7208839419007993}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,040]\u001b[0m Trial 501 finished with value: 3.5864952993780803 and parameters: {'x': 0.8348017265580161, 'y': 0.8855524721612094}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,055]\u001b[0m Trial 502 finished with value: 0.7256117332551236 and parameters: {'x': 0.9495508685978196, 'y': 0.8166135280932708}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,069]\u001b[0m Trial 503 finished with value: 0.09705234197414898 and parameters: {'x': 1.0385402244560367, 'y': 1.0476518862080573}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,084]\u001b[0m Trial 504 finished with value: 0.03787657422955128 and parameters: {'x': 0.8268099734570657, 'y': 0.6924926761905559}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,100]\u001b[0m Trial 505 finished with value: 0.787996222994307 and parameters: {'x': 0.8887379924920775, 'y': 0.7017860993600401}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,114]\u001b[0m Trial 506 finished with value: 16.381634148906233 and parameters: {'x': 1.1628159415270625, 'y': 0.9477262118853433}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,130]\u001b[0m Trial 507 finished with value: 2.3452382730417205 and parameters: {'x': 0.9936669247227636, 'y': 0.8342335587904309}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,145]\u001b[0m Trial 508 finished with value: 1.0339059824617383 and parameters: {'x': 0.825251196692268, 'y': 0.781207837927303}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,159]\u001b[0m Trial 509 finished with value: 0.36192950757567655 and parameters: {'x': 0.9312745637658354, 'y': 0.9270390531981734}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,176]\u001b[0m Trial 510 finished with value: 0.031809041811516135 and parameters: {'x': 0.8346848936456759, 'y': 0.6900056232741942}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,191]\u001b[0m Trial 511 finished with value: 21.54512157453524 and parameters: {'x': -0.499598295062338, 'y': 0.6888742983324502}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,207]\u001b[0m Trial 512 finished with value: 13.96015019754804 and parameters: {'x': 0.835765241797657, 'y': 1.0717752519838692}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,222]\u001b[0m Trial 513 finished with value: 100.7998062776262 and parameters: {'x': 1.3085280477015877, 'y': 0.708728752305092}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,238]\u001b[0m Trial 514 finished with value: 1.9720085591193628 and parameters: {'x': 1.0657530202338257, 'y': 0.9955553008188477}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,252]\u001b[0m Trial 515 finished with value: 1.1466332038351645 and parameters: {'x': 0.9804958441868562, 'y': 0.8543089045626052}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,269]\u001b[0m Trial 516 finished with value: 0.032139365877290084 and parameters: {'x': 0.8207437207118261, 'y': 0.673876227997597}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,285]\u001b[0m Trial 517 finished with value: 1.1396272502188871 and parameters: {'x': 0.8153752708093228, 'y': 0.7699815401308528}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,300]\u001b[0m Trial 518 finished with value: 0.2099899525285323 and parameters: {'x': 0.8442348237131958, 'y': 0.669636363226223}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,315]\u001b[0m Trial 519 finished with value: 29.420556847174442 and parameters: {'x': 1.1025460199295438, 'y': 0.6732974720475733}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,331]\u001b[0m Trial 520 finished with value: 7.227753370218869 and parameters: {'x': 0.7984287194026496, 'y': 0.9055765097631489}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,346]\u001b[0m Trial 521 finished with value: 6.527166570638675 and parameters: {'x': 0.9961138399781985, 'y': 0.736759877319896}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,363]\u001b[0m Trial 522 finished with value: 6.055065023206508 and parameters: {'x': 0.9186945933653492, 'y': 1.0899358137079593}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,380]\u001b[0m Trial 523 finished with value: 0.6457802983710904 and parameters: {'x': 1.0314610154849038, 'y': 0.9836129782246075}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,396]\u001b[0m Trial 524 finished with value: 1.025681733573133 and parameters: {'x': 1.184508974281865, 'y': 1.3034804792305812}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,411]\u001b[0m Trial 525 finished with value: 27.12247254745159 and parameters: {'x': 0.8172242699604894, 'y': 1.1883270780093522}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,427]\u001b[0m Trial 526 finished with value: 0.1577787322373703 and parameters: {'x': 0.9026139688621455, 'y': 0.7762029267868482}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,444]\u001b[0m Trial 527 finished with value: 452.93738071910286 and parameters: {'x': 1.6770665804290794, 'y': 0.6853970280426758}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,460]\u001b[0m Trial 528 finished with value: 0.554215233662424 and parameters: {'x': 0.9756516029094104, 'y': 0.877490244758952}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,474]\u001b[0m Trial 529 finished with value: 5.6352246278191735 and parameters: {'x': 1.124907570786675, 'y': 1.0283596082834983}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,491]\u001b[0m Trial 530 finished with value: 7.592096545151969 and parameters: {'x': 0.812129221027269, 'y': 0.9344502360377445}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,507]\u001b[0m Trial 531 finished with value: 0.06262030612283129 and parameters: {'x': 0.8921105243060596, 'y': 0.7732823994885172}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,522]\u001b[0m Trial 532 finished with value: 16.38339521691782 and parameters: {'x': 1.0331676917138122, 'y': 0.6626849990144508}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,538]\u001b[0m Trial 533 finished with value: 6.115814123659771 and parameters: {'x': 0.9459205264658536, 'y': 1.1420082263874625}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,553]\u001b[0m Trial 534 finished with value: 582.6743535621839 and parameters: {'x': 1.8022863320256441, 'y': 0.8357047995885414}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,568]\u001b[0m Trial 535 finished with value: 0.020209679901838386 and parameters: {'x': 0.8715269009941006, 'y': 0.7534728079678413}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,585]\u001b[0m Trial 536 finished with value: 5.612552735013613 and parameters: {'x': 1.0638646944285228, 'y': 0.8949859177249143}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,601]\u001b[0m Trial 537 finished with value: 0.023633883742160664 and parameters: {'x': 0.8712451650815032, 'y': 0.7506680923046425}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,622]\u001b[0m Trial 538 finished with value: 34.5017604759713 and parameters: {'x': 0.8241883965502868, 'y': 1.2664053319542106}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,638]\u001b[0m Trial 539 finished with value: 0.08160569954325506 and parameters: {'x': 1.0044583133680303, 'y': 0.9803732711617078}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,654]\u001b[0m Trial 540 finished with value: 37.220538051011175 and parameters: {'x': 1.2046071969491692, 'y': 0.8413353239412448}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,670]\u001b[0m Trial 541 finished with value: 0.4099196669774436 and parameters: {'x': 0.8796432280789362, 'y': 0.7108886723517913}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,689]\u001b[0m Trial 542 finished with value: 2.6815840703020744 and parameters: {'x': 1.105152393407846, 'y': 1.057944339768539}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,702]\u001b[0m Trial 543 finished with value: 0.0073409642590622015 and parameters: {'x': 0.9622619376045742, 'y': 0.9336401122938718}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,719]\u001b[0m Trial 544 finished with value: 0.5379909659251982 and parameters: {'x': 0.9515295796847201, 'y': 0.9785960798641351}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,735]\u001b[0m Trial 545 finished with value: 1.033279194041905 and parameters: {'x': 1.019326596722368, 'y': 1.1406586780351389}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,751]\u001b[0m Trial 546 finished with value: 33.83913659864316 and parameters: {'x': -1.2077513568162699, 'y': 0.9204721977764084}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,767]\u001b[0m Trial 547 finished with value: 5.155109559160398 and parameters: {'x': 1.120399422462995, 'y': 1.028565654278453}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,782]\u001b[0m Trial 548 finished with value: 0.13110227797548146 and parameters: {'x': 0.9623540976664428, 'y': 0.8901135967871379}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,803]\u001b[0m Trial 549 finished with value: 9.622202219440295 and parameters: {'x': 1.0662575355167316, 'y': 0.8267791550880184}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,821]\u001b[0m Trial 550 finished with value: 69.723555405751 and parameters: {'x': 1.3360499963541783, 'y': 0.9506997670553504}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,836]\u001b[0m Trial 551 finished with value: 5.922414729742445 and parameters: {'x': 0.9279461635212842, 'y': 1.1043375093870194}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,853]\u001b[0m Trial 552 finished with value: 3.7640529193316254 and parameters: {'x': -0.9328791660338551, 'y': 0.887006013996797}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,869]\u001b[0m Trial 553 finished with value: 1.9829395840420587 and parameters: {'x': 0.8043171500898206, 'y': 0.7863767074106284}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,885]\u001b[0m Trial 554 finished with value: 168.49174257913518 and parameters: {'x': 0.9781892124047823, 'y': -0.34118772410879045}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,899]\u001b[0m Trial 555 finished with value: 16.459104382393264 and parameters: {'x': 1.1934533935541811, 'y': 1.0190942795842914}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,916]\u001b[0m Trial 556 finished with value: 35.59632158979611 and parameters: {'x': 0.7896227253342142, 'y': 1.2197595556100795}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,932]\u001b[0m Trial 557 finished with value: 44.442291559002115 and parameters: {'x': 0.8860596679589223, 'y': 1.4516548775490075}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,948]\u001b[0m Trial 558 finished with value: 18.446971853791506 and parameters: {'x': 1.0412489114975985, 'y': 0.6547197224776294}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,964]\u001b[0m Trial 559 finished with value: 0.5773863200714754 and parameters: {'x': 0.956988270741116, 'y': 0.8399624408232305}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,978]\u001b[0m Trial 560 finished with value: 4.611539184916242 and parameters: {'x': 0.8606889497185854, 'y': 0.9550780629110777}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:04,995]\u001b[0m Trial 561 finished with value: 16.968581690574602 and parameters: {'x': 1.0884567709512871, 'y': 0.7729037448046276}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,013]\u001b[0m Trial 562 finished with value: 16.04884702878338 and parameters: {'x': 0.8096793094190121, 'y': 1.0557383662762319}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,028]\u001b[0m Trial 563 finished with value: 1.1235242733654047 and parameters: {'x': 0.9935249772950756, 'y': 0.8810974303032204}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,046]\u001b[0m Trial 564 finished with value: 0.8159060975994784 and parameters: {'x': 0.9052354412463706, 'y': 0.7296221566704456}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,062]\u001b[0m Trial 565 finished with value: 31.43869456781622 and parameters: {'x': 1.1581365757028799, 'y': 1.9017594747047466}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,076]\u001b[0m Trial 566 finished with value: 8.690518760642295 and parameters: {'x': 0.7919142286997614, 'y': 0.9211896880266254}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,094]\u001b[0m Trial 567 finished with value: 4.951317750416628 and parameters: {'x': 1.0221650312770822, 'y': 0.82231682398016}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,114]\u001b[0m Trial 568 finished with value: 5.53302141603515 and parameters: {'x': 0.8686532138232395, 'y': 0.9894151587752879}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,130]\u001b[0m Trial 569 finished with value: 6.843568254696918 and parameters: {'x': 0.9624173391887082, 'y': 0.6646719869014166}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,147]\u001b[0m Trial 570 finished with value: 23.231473761158096 and parameters: {'x': 1.116639834591607, 'y': 0.7650352812210882}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,163]\u001b[0m Trial 571 finished with value: 9.544480475161025 and parameters: {'x': 0.9171614252863028, 'y': 1.150015425784724}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,178]\u001b[0m Trial 572 finished with value: 118.51846237214332 and parameters: {'x': 1.5564302899178188, 'y': 1.3352363237482956}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,195]\u001b[0m Trial 573 finished with value: 6.962229389894399 and parameters: {'x': 0.7949132482667514, 'y': 0.8949492096854363}. Best is trial 437 with value: 0.0024241819969246636.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,212]\u001b[0m Trial 574 finished with value: 0.0020023056898479247 and parameters: {'x': 1.0410472849928223, 'y': 1.0819978040465885}. Best is trial 574 with value: 0.0020023056898479247.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,228]\u001b[0m Trial 575 finished with value: 12.094620544795283 and parameters: {'x': 1.2471392480899839, 'y': 1.2084623327627382}. Best is trial 574 with value: 0.0020023056898479247.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,244]\u001b[0m Trial 576 finished with value: 0.3797217034853308 and parameters: {'x': 1.0749387593483128, 'y': 1.094329140463734}. Best is trial 574 with value: 0.0020023056898479247.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,260]\u001b[0m Trial 577 finished with value: 0.03665827383789852 and parameters: {'x': 1.0403368446484942, 'y': 1.0635841232861711}. Best is trial 574 with value: 0.0020023056898479247.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,277]\u001b[0m Trial 578 finished with value: 12.172538984013796 and parameters: {'x': 1.2291475354872252, 'y': 1.1626653238047633}. Best is trial 574 with value: 0.0020023056898479247.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,293]\u001b[0m Trial 579 finished with value: 2.6834436286111556 and parameters: {'x': 1.1146162506747297, 'y': 1.0789586539495213}. Best is trial 574 with value: 0.0020023056898479247.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,309]\u001b[0m Trial 580 finished with value: 0.39006984513046644 and parameters: {'x': 1.1609094236536226, 'y': 1.2873635274004622}. Best is trial 574 with value: 0.0020023056898479247.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,323]\u001b[0m Trial 581 finished with value: 0.08950265410900748 and parameters: {'x': 1.0343579874446358, 'y': 1.040177397954115}. Best is trial 574 with value: 0.0020023056898479247.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,343]\u001b[0m Trial 582 finished with value: 0.38703456175953577 and parameters: {'x': 1.0375642999975307, 'y': 1.1386382663979937}. Best is trial 574 with value: 0.0020023056898479247.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,360]\u001b[0m Trial 583 finished with value: 4.553366595233299 and parameters: {'x': 1.1218425706835717, 'y': 1.0454927055472527}. Best is trial 574 with value: 0.0020023056898479247.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,377]\u001b[0m Trial 584 finished with value: 49.898337326978954 and parameters: {'x': 1.300441567323663, 'y': 0.985399928249735}. Best is trial 574 with value: 0.0020023056898479247.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,394]\u001b[0m Trial 585 finished with value: 0.0018008451828139063 and parameters: {'x': 0.9987281860356447, 'y': 1.0016997199742996}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,411]\u001b[0m Trial 586 finished with value: 0.5888682604460799 and parameters: {'x': 1.0490869414451334, 'y': 1.1771640043768314}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,432]\u001b[0m Trial 587 finished with value: 1.2110276648725973 and parameters: {'x': 0.9930990369097561, 'y': 1.0962902354390287}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,448]\u001b[0m Trial 588 finished with value: 17.19002910976747 and parameters: {'x': 1.1904490663604093, 'y': 1.0029980213831675}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,463]\u001b[0m Trial 589 finished with value: 0.4079576115985057 and parameters: {'x': 1.0918952606105867, 'y': 1.2554422908189227}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,483]\u001b[0m Trial 590 finished with value: 0.07892392329246366 and parameters: {'x': 0.9993573519993182, 'y': 0.9706217885529108}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,499]\u001b[0m Trial 591 finished with value: 104.9894085166003 and parameters: {'x': 1.4464248472154322, 'y': 1.068474416228854}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,516]\u001b[0m Trial 592 finished with value: 5.337180576460911 and parameters: {'x': 0.9627609999260709, 'y': 1.157902115899483}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,532]\u001b[0m Trial 593 finished with value: 15.959912574323795 and parameters: {'x': 1.1712902791780342, 'y': 0.9727897089115204}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,547]\u001b[0m Trial 594 finished with value: 0.3876032825881002 and parameters: {'x': 1.0715277890133958, 'y': 1.21001734070361}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,567]\u001b[0m Trial 595 finished with value: 28.563995805589897 and parameters: {'x': 0.9078654586285597, 'y': 1.3585932216764385}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,585]\u001b[0m Trial 596 finished with value: 43.82069999196212 and parameters: {'x': -0.6153866365661246, 'y': 1.0206604152988992}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,602]\u001b[0m Trial 597 finished with value: 0.008927089256610144 and parameters: {'x': 0.9695898765691406, 'y': 0.931158963559455}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,617]\u001b[0m Trial 598 finished with value: 0.06402282154585422 and parameters: {'x': 1.0370324123766104, 'y': 1.1004664903948305}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,635]\u001b[0m Trial 599 finished with value: 32.01307782055921 and parameters: {'x': 1.2349490129505498, 'y': 0.9597860824097637}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,653]\u001b[0m Trial 600 finished with value: 0.05270728680083288 and parameters: {'x': 0.9788420953787571, 'y': 0.9352714824493578}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,672]\u001b[0m Trial 601 finished with value: 3.2523588354125326 and parameters: {'x': 1.1063459626623873, 'y': 1.04397224224703}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,689]\u001b[0m Trial 602 finished with value: 0.18954735812572412 and parameters: {'x': 0.9775315626257153, 'y': 0.9120889348448529}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,705]\u001b[0m Trial 603 finished with value: 6.63811404668678 and parameters: {'x': 0.9113087620282656, 'y': 1.0879763375287586}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,722]\u001b[0m Trial 604 finished with value: 23.19810521707052 and parameters: {'x': 1.1531748124633694, 'y': 0.8484116631199333}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,740]\u001b[0m Trial 605 finished with value: 1.6122053368804359 and parameters: {'x': 1.0579813356518972, 'y': 0.9924843117914675}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,757]\u001b[0m Trial 606 finished with value: 0.3438941296568641 and parameters: {'x': 0.9262654240141261, 'y': 0.9161447224473698}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,774]\u001b[0m Trial 607 finished with value: 4.598401471317645 and parameters: {'x': 1.0139538915579693, 'y': 1.2425367909080143}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,788]\u001b[0m Trial 608 finished with value: 0.8433045622387618 and parameters: {'x': 0.8643473046118314, 'y': 0.8379204287427295}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,806]\u001b[0m Trial 609 finished with value: 306.014524347049 and parameters: {'x': -1.6925946173576736, 'y': 1.1363961119354926}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,830]\u001b[0m Trial 610 finished with value: 4.050507137159678 and parameters: {'x': 1.0932097900905253, 'y': 0.9940648867079166}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,850]\u001b[0m Trial 611 finished with value: 0.9919181135032535 and parameters: {'x': 0.9596898066549779, 'y': 0.8214910484319434}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,865]\u001b[0m Trial 612 finished with value: 2.9970659439450142 and parameters: {'x': 0.8691332365303922, 'y': 0.9280176051926695}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,887]\u001b[0m Trial 613 finished with value: 286.9617066891524 and parameters: {'x': 1.0123355489514492, 'y': -0.6691706989021887}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,903]\u001b[0m Trial 614 finished with value: 8.488064291210037 and parameters: {'x': 1.160555023625414, 'y': 1.0559878704662105}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,920]\u001b[0m Trial 615 finished with value: 0.4961303790156945 and parameters: {'x': 0.9308580942014875, 'y': 0.7964004452819902}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,937]\u001b[0m Trial 616 finished with value: 43.52223451880143 and parameters: {'x': 1.25040081426771, 'y': 0.9042637421106208}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,954]\u001b[0m Trial 617 finished with value: 27.76959255582318 and parameters: {'x': 0.7795246971057767, 'y': 1.1341659536965465}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,971]\u001b[0m Trial 618 finished with value: 97.81270917683254 and parameters: {'x': -0.24079682405677783, 'y': 1.0391718406304487}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:05,988]\u001b[0m Trial 619 finished with value: 10.734972709583323 and parameters: {'x': 1.0532026472994187, 'y': 0.7816363323365466}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,007]\u001b[0m Trial 620 finished with value: 3.947809832820779 and parameters: {'x': 0.8687108471595748, 'y': 0.9529152632524437}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,024]\u001b[0m Trial 621 finished with value: 55.9065094512456 and parameters: {'x': 0.9734368037683285, 'y': 1.6952810488838472}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,043]\u001b[0m Trial 622 finished with value: 11.190017423705282 and parameters: {'x': 1.0958128358339843, 'y': 0.8664281803594756}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,060]\u001b[0m Trial 623 finished with value: 11.30074774734397 and parameters: {'x': 0.8138942223582718, 'y': 0.9980741051563473}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,079]\u001b[0m Trial 624 finished with value: 1.245701876308671 and parameters: {'x': 0.925999172631501, 'y': 0.7461090442715127}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,095]\u001b[0m Trial 625 finished with value: 2.14748820281289 and parameters: {'x': 1.0284397770295188, 'y': 1.2042038820703287}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,111]\u001b[0m Trial 626 finished with value: 18.827673597333252 and parameters: {'x': 1.1405963771205774, 'y': 0.8672792652109157}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,127]\u001b[0m Trial 627 finished with value: 8.378632320477603 and parameters: {'x': 0.8876795728347733, 'y': 1.0772156927128564}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,148]\u001b[0m Trial 628 finished with value: 0.049363262449083026 and parameters: {'x': 0.9799910556238911, 'y': 0.9382549060661909}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,165]\u001b[0m Trial 629 finished with value: 4.380540044073815 and parameters: {'x': 0.764836820244746, 'y': 0.7929474347985019}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,185]\u001b[0m Trial 630 finished with value: 20.93406398605496 and parameters: {'x': 1.0556407365624878, 'y': 0.6568736150390032}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,202]\u001b[0m Trial 631 finished with value: 6.327975174466033 and parameters: {'x': 0.8684426242705504, 'y': 1.0054030180131033}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,219]\u001b[0m Trial 632 finished with value: 0.14750886755246556 and parameters: {'x': 0.9621337243386718, 'y': 0.8874815425581692}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,239]\u001b[0m Trial 633 finished with value: 786.2851840153755 and parameters: {'x': 1.3183102689832324, 'y': -1.0659550773610003}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,261]\u001b[0m Trial 634 finished with value: 81.42192789663542 and parameters: {'x': 0.7938638606013092, 'y': 1.5323253428902932}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,283]\u001b[0m Trial 635 finished with value: 11.184371254389907 and parameters: {'x': 1.206827485596181, 'y': 1.122642321810329}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,300]\u001b[0m Trial 636 finished with value: 2.8678662746921324 and parameters: {'x': 1.011109278651876, 'y': 0.8529978604395659}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,322]\u001b[0m Trial 637 finished with value: 0.09497358308501352 and parameters: {'x': 0.9028870266718164, 'y': 0.7859573072132742}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,338]\u001b[0m Trial 638 finished with value: 10.28684979267477 and parameters: {'x': 1.1313621531483582, 'y': 0.9595182524108077}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,355]\u001b[0m Trial 639 finished with value: 31.13236263790675 and parameters: {'x': 0.8281858447511694, 'y': 1.2435910182436152}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,372]\u001b[0m Trial 640 finished with value: 2.0468446088057344 and parameters: {'x': 0.958703050284696, 'y': 1.0621199009943072}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,387]\u001b[0m Trial 641 finished with value: 13.838989308604125 and parameters: {'x': 1.0599885844744719, 'y': 0.7516162492872154}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,405]\u001b[0m Trial 642 finished with value: 3.1709651133740735 and parameters: {'x': 0.9114547335832441, 'y': 0.6528979714933284}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,422]\u001b[0m Trial 643 finished with value: 9.245545440100669 and parameters: {'x': 0.7889129390274167, 'y': 0.9257149243234677}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,441]\u001b[0m Trial 644 finished with value: 2.7222534738139914 and parameters: {'x': 1.00401965929864, 'y': 1.1730475159495648}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,459]\u001b[0m Trial 645 finished with value: 2.869297890191547 and parameters: {'x': 1.0734370054963809, 'y': 1.3214977613239636}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,475]\u001b[0m Trial 646 finished with value: 39.747035941987804 and parameters: {'x': 0.87997806593765, 'y': 1.4046996444374555}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,492]\u001b[0m Trial 647 finished with value: 2510.329756086337 and parameters: {'x': 1.8280823165245057, 'y': -1.6677497969274162}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,511]\u001b[0m Trial 648 finished with value: 10.210867213079755 and parameters: {'x': 1.1512688388174406, 'y': 1.0062337090860416}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,529]\u001b[0m Trial 649 finished with value: 3.8329568554185243 and parameters: {'x': 0.964365270252236, 'y': 0.7342534199213667}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,544]\u001b[0m Trial 650 finished with value: 0.6692721326107929 and parameters: {'x': 0.8540562574425141, 'y': 0.809908833457958}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,562]\u001b[0m Trial 651 finished with value: 6.68159155789772 and parameters: {'x': 0.7717118708429005, 'y': 0.8530169016621862}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,582]\u001b[0m Trial 652 finished with value: 2.001042061432879 and parameters: {'x': 1.0310220675308701, 'y': 0.9215823299618727}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,599]\u001b[0m Trial 653 finished with value: 7.060426058607486 and parameters: {'x': 0.922769306169297, 'y': 1.1171055545689326}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,616]\u001b[0m Trial 654 finished with value: 4.316974032830768 and parameters: {'x': 1.102273415116907, 'y': 1.0074852567219652}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,634]\u001b[0m Trial 655 finished with value: 509.5484892840015 and parameters: {'x': 0.9931722608183514, 'y': -1.270926832328915}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,651]\u001b[0m Trial 656 finished with value: 0.12185304527917726 and parameters: {'x': 0.8489939159640232, 'y': 0.6893184263569238}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,670]\u001b[0m Trial 657 finished with value: 61.34286309806733 and parameters: {'x': 1.274224386385579, 'y': 0.8409111511770593}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,688]\u001b[0m Trial 658 finished with value: 87.77901827298257 and parameters: {'x': 0.02292696569713859, 'y': 0.9323214740286708}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,706]\u001b[0m Trial 659 finished with value: 139.94510702965846 and parameters: {'x': 1.3944299312881332, 'y': 0.7621086005472557}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,722]\u001b[0m Trial 660 finished with value: 16.760534244105962 and parameters: {'x': 1.1941897536574984, 'y': 1.0171536644679695}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,738]\u001b[0m Trial 661 finished with value: 5.705528961128 and parameters: {'x': 0.9350962401523221, 'y': 0.6356306819270294}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,760]\u001b[0m Trial 662 finished with value: 0.5518880211432432 and parameters: {'x': 1.0774797061461214, 'y': 1.087078492071552}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,777]\u001b[0m Trial 663 finished with value: 2.757331911616882 and parameters: {'x': 0.8481551731752545, 'y': 0.8847236328018624}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,793]\u001b[0m Trial 664 finished with value: 4.043704995442814 and parameters: {'x': 1.0073252692738726, 'y': 0.8136158758343389}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,809]\u001b[0m Trial 665 finished with value: 34.90188083079634 and parameters: {'x': 0.7849212970429916, 'y': 1.2064879429251845}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,828]\u001b[0m Trial 666 finished with value: 0.7352391759933803 and parameters: {'x': 0.9316712950434717, 'y': 0.9534848109766375}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,852]\u001b[0m Trial 667 finished with value: 20.466381914078244 and parameters: {'x': 1.0890842586428011, 'y': 0.7337943864665805}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,873]\u001b[0m Trial 668 finished with value: 0.49216273887175654 and parameters: {'x': 0.8980702633482129, 'y': 0.8759400723993271}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,888]\u001b[0m Trial 669 finished with value: 0.1825844939822633 and parameters: {'x': 1.0081459992550923, 'y': 1.059080497225546}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,907]\u001b[0m Trial 670 finished with value: 33.030275684318624 and parameters: {'x': 0.7589538479602598, 'y': 1.1502249490572085}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,925]\u001b[0m Trial 671 finished with value: 0.3972075705794497 and parameters: {'x': 0.9540265779542308, 'y': 0.9730232158833209}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,942]\u001b[0m Trial 672 finished with value: 234.79376507142064 and parameters: {'x': 1.1547026375418823, 'y': -0.19888188191708112}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,958]\u001b[0m Trial 673 finished with value: 0.6394699675227048 and parameters: {'x': 0.8618436106092436, 'y': 0.6640100276298564}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,977]\u001b[0m Trial 674 finished with value: 4.887341011344756 and parameters: {'x': 1.0380570922561403, 'y': 0.8565219723803814}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:06,996]\u001b[0m Trial 675 finished with value: 2.2896124109673837 and parameters: {'x': 0.783761554328531, 'y': 0.7640437627630451}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,014]\u001b[0m Trial 676 finished with value: 3.6501377919529823 and parameters: {'x': 0.934077437750559, 'y': 1.0634402315100069}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,029]\u001b[0m Trial 677 finished with value: 9.47746779419456 and parameters: {'x': 1.1119371291722928, 'y': 0.9287527876649841}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,053]\u001b[0m Trial 678 finished with value: 28.871488105699264 and parameters: {'x': 0.8574459191381366, 'y': 1.27234632245578}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,071]\u001b[0m Trial 679 finished with value: 3.5969569833971318 and parameters: {'x': 0.9982842126214595, 'y': 0.8069149946528393}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,088]\u001b[0m Trial 680 finished with value: 24.558948939355417 and parameters: {'x': 1.2200291786981232, 'y': 0.9933900308032642}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,107]\u001b[0m Trial 681 finished with value: 2.9263580786226737 and parameters: {'x': 0.9392231358978622, 'y': 0.7111820851131456}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,123]\u001b[0m Trial 682 finished with value: 3.5098864709897035 and parameters: {'x': 1.0406748407155877, 'y': 0.8957013737629619}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,142]\u001b[0m Trial 683 finished with value: 27.774875842269143 and parameters: {'x': 0.7690191306763942, 'y': 1.1179027563478474}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,162]\u001b[0m Trial 684 finished with value: 0.3516775831455018 and parameters: {'x': 0.8681938765455537, 'y': 0.8115796983195179}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,179]\u001b[0m Trial 685 finished with value: 7.639179214232956 and parameters: {'x': 1.1389356186111124, 'y': 1.0211331125780967}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,195]\u001b[0m Trial 686 finished with value: 6.77046199987196 and parameters: {'x': 0.9517313157737715, 'y': 0.6456361572365975}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,214]\u001b[0m Trial 687 finished with value: 3.8395191870053877 and parameters: {'x': 1.0589148767910324, 'y': 0.925442394426392}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,231]\u001b[0m Trial 688 finished with value: 1.0036382570054505 and parameters: {'x': 0.8133253466925906, 'y': 0.759925292183544}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,249]\u001b[0m Trial 689 finished with value: 1.7205584816979405 and parameters: {'x': 0.9897911820512463, 'y': 0.8485204961574461}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,264]\u001b[0m Trial 690 finished with value: 10.132172223321813 and parameters: {'x': 0.9267031193451631, 'y': 1.1770050026622985}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,285]\u001b[0m Trial 691 finished with value: 4.67388275511238 and parameters: {'x': 0.8644049251396546, 'y': 0.962961875884531}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,304]\u001b[0m Trial 692 finished with value: 1.764434501125214 and parameters: {'x': 1.0934598792638726, 'y': 1.0631516867560833}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,322]\u001b[0m Trial 693 finished with value: 3.0130170577152957 and parameters: {'x': 0.756065880937561, 'y': 0.7434934995607757}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,342]\u001b[0m Trial 694 finished with value: 2.7899036753721123 and parameters: {'x': 1.0057488002508268, 'y': 0.8445015910553734}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,356]\u001b[0m Trial 695 finished with value: 362.62403188362777 and parameters: {'x': 1.1747983453678963, 'y': -0.5240375921001607}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,376]\u001b[0m Trial 696 finished with value: 1.6765537491085443 and parameters: {'x': 0.8850505453400352, 'y': 0.912285021499462}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,395]\u001b[0m Trial 697 finished with value: 18.784981625266145 and parameters: {'x': 1.0448517494475538, 'y': 0.658321939499591}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,413]\u001b[0m Trial 698 finished with value: 2.6851775337501573 and parameters: {'x': 0.9224707525811429, 'y': 1.0146338933903332}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,430]\u001b[0m Trial 699 finished with value: 19.867807983627852 and parameters: {'x': 0.8141742144198895, 'y': 1.1082253234725927}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,450]\u001b[0m Trial 700 finished with value: 54.17616871285387 and parameters: {'x': 1.2378383255776275, 'y': 0.7965834614282352}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,468]\u001b[0m Trial 701 finished with value: 0.26009097681626786 and parameters: {'x': 0.9723304205091617, 'y': 0.8945024472771804}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,486]\u001b[0m Trial 702 finished with value: 0.8933696287900555 and parameters: {'x': 1.098859060251919, 'y': 1.3014910489250033}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,503]\u001b[0m Trial 703 finished with value: 5.800327746561942 and parameters: {'x': 0.8562249583580701, 'y': 0.9735303406208082}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,522]\u001b[0m Trial 704 finished with value: 2.318480906757959 and parameters: {'x': 0.7511805879254283, 'y': 0.71449110961278}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,537]\u001b[0m Trial 705 finished with value: 2.2062215416004087 and parameters: {'x': 0.9892152186258898, 'y': 0.83001711434116}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,556]\u001b[0m Trial 706 finished with value: 76.97180066085369 and parameters: {'x': -1.425762149115015, 'y': 1.1896637979982754}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,576]\u001b[0m Trial 707 finished with value: 5.213415034147021 and parameters: {'x': 0.9038448327079015, 'y': 1.0450619644671342}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,593]\u001b[0m Trial 708 finished with value: 5.177256219486272 and parameters: {'x': 1.080365620890017, 'y': 0.9397959965531024}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,609]\u001b[0m Trial 709 finished with value: 0.16864518721925034 and parameters: {'x': 0.8138904167641449, 'y': 0.6258104513308883}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,630]\u001b[0m Trial 710 finished with value: 36.86606532186877 and parameters: {'x': -0.3862318343734028, 'y': 0.7403131408460047}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,651]\u001b[0m Trial 711 finished with value: 1.3563618884874464 and parameters: {'x': 1.0035256972443198, 'y': 1.1235262420553804}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,673]\u001b[0m Trial 712 finished with value: 22.81390599501149 and parameters: {'x': 1.1551827090440303, 'y': 0.8570602007774537}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,689]\u001b[0m Trial 713 finished with value: 1.8530393326402719 and parameters: {'x': 0.9228764866952307, 'y': 0.9876087466467263}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,709]\u001b[0m Trial 714 finished with value: 6.451667127313356 and parameters: {'x': 0.7367603338123826, 'y': 0.7954493582198867}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,727]\u001b[0m Trial 715 finished with value: 2.550237380109041 and parameters: {'x': 1.027976239839116, 'y': 0.8970650300485382}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,745]\u001b[0m Trial 716 finished with value: 0.33224177704735564 and parameters: {'x': 0.8697457294330395, 'y': 0.700308225851749}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,763]\u001b[0m Trial 717 finished with value: 4.473209650878992 and parameters: {'x': 0.9189825108757028, 'y': 1.0558732618297453}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,781]\u001b[0m Trial 718 finished with value: 0.10539680575500063 and parameters: {'x': 1.082916526404027, 'y': 1.2040963626150534}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,799]\u001b[0m Trial 719 finished with value: 9.224386881439216 and parameters: {'x': 0.814561223116778, 'y': 0.9666601031684884}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,816]\u001b[0m Trial 720 finished with value: 10.340645762462646 and parameters: {'x': 0.9774353222439951, 'y': 0.6338189813665174}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,834]\u001b[0m Trial 721 finished with value: 20.39541650342237 and parameters: {'x': 1.1319766630481682, 'y': 0.8299511986501678}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,856]\u001b[0m Trial 722 finished with value: 45.89786671187893 and parameters: {'x': 1.2569629731721605, 'y': 0.9029637686870172}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,878]\u001b[0m Trial 723 finished with value: 7.620400629586734 and parameters: {'x': -0.7386247932452098, 'y': 0.7599863710815956}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,900]\u001b[0m Trial 724 finished with value: 1.2612643515708193 and parameters: {'x': 0.9566056010817727, 'y': 1.0273164341589447}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,918]\u001b[0m Trial 725 finished with value: 13.742710384613291 and parameters: {'x': 0.8623668916723818, 'y': 1.1141326925440231}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,942]\u001b[0m Trial 726 finished with value: 9.8102290209523 and parameters: {'x': 0.7576386826169914, 'y': 0.886290131705744}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,962]\u001b[0m Trial 727 finished with value: 11.255503598074526 and parameters: {'x': 1.027103615091372, 'y': 0.7194605551042044}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,981]\u001b[0m Trial 728 finished with value: 0.7034667748502613 and parameters: {'x': 0.9422068894382617, 'y': 0.9714273984546749}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:07,999]\u001b[0m Trial 729 finished with value: 1.699369420655935 and parameters: {'x': 0.8359694958561491, 'y': 0.8281687550757039}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,018]\u001b[0m Trial 730 finished with value: 0.002678169220297488 and parameters: {'x': 1.0450106372559225, 'y': 1.0894933882981748}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,040]\u001b[0m Trial 731 finished with value: 0.47930962665049315 and parameters: {'x': 1.180965128307042, 'y': 1.3278533958573933}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,059]\u001b[0m Trial 732 finished with value: 11.047139765746714 and parameters: {'x': 0.9014106215631377, 'y': 1.1447672349755078}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,077]\u001b[0m Trial 733 finished with value: 0.1805804264599798 and parameters: {'x': 1.0921683588487296, 'y': 1.2343149038046621}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,093]\u001b[0m Trial 734 finished with value: 4.71420464361858 and parameters: {'x': 0.9914886775183669, 'y': 0.765929273490416}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,114]\u001b[0m Trial 735 finished with value: 0.3758154288377535 and parameters: {'x': 0.7595609928667485, 'y': 0.6333247907240326}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,133]\u001b[0m Trial 736 finished with value: 3.6535777337091324 and parameters: {'x': 0.8612985494152735, 'y': 0.9324746320894043}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,153]\u001b[0m Trial 737 finished with value: 82.99244798134264 and parameters: {'x': 1.3184748753135809, 'y': 0.8279309313012733}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,169]\u001b[0m Trial 738 finished with value: 12.538556315655944 and parameters: {'x': 1.0438511987921424, 'y': 1.4436964111336306}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,189]\u001b[0m Trial 739 finished with value: 2.0393335809993705 and parameters: {'x': 0.9386018009423367, 'y': 1.0236465281975322}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,206]\u001b[0m Trial 740 finished with value: 41.82588215850912 and parameters: {'x': 1.170328876403565, 'y': 0.7231646887670625}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,229]\u001b[0m Trial 741 finished with value: 5.7886725958221 and parameters: {'x': 0.7917820551270784, 'y': 0.8666127502214966}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,247]\u001b[0m Trial 742 finished with value: 2.6762879518463136 and parameters: {'x': 0.9657007823275295, 'y': 1.096135682419153}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,265]\u001b[0m Trial 743 finished with value: 82.51653559281745 and parameters: {'x': 0.11858543005201345, 'y': 0.9181622993135126}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,287]\u001b[0m Trial 744 finished with value: 5.138762894016804 and parameters: {'x': 1.0978053494698397, 'y': 0.9786992794497722}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,308]\u001b[0m Trial 745 finished with value: 0.12634899531834415 and parameters: {'x': 0.8766053224325838, 'y': 0.8017719702680498}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,327]\u001b[0m Trial 746 finished with value: 3.3291864717138933 and parameters: {'x': 1.031990810018885, 'y': 1.2474375690775203}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,346]\u001b[0m Trial 747 finished with value: 3.825297847955335 and parameters: {'x': 0.9144085373288185, 'y': 0.6407466592167376}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,367]\u001b[0m Trial 748 finished with value: 0.11589111827804273 and parameters: {'x': 0.8264042271165797, 'y': 0.7122280081168918}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,382]\u001b[0m Trial 749 finished with value: 29.677426242986844 and parameters: {'x': 0.7207583748641546, 'y': 1.0635464039503066}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,403]\u001b[0m Trial 750 finished with value: 1.5802215481438706 and parameters: {'x': 0.9963536163829504, 'y': 0.8670141944315766}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,424]\u001b[0m Trial 751 finished with value: 0.962302896648784 and parameters: {'x': 1.125565935067852, 'y': 1.1696085877118114}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,445]\u001b[0m Trial 752 finished with value: 0.3959044142815296 and parameters: {'x': 0.929662986942875, 'y': 0.9267998331564953}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,463]\u001b[0m Trial 753 finished with value: 87.25463375809314 and parameters: {'x': 0.8317674630789827, 'y': 1.625787494464566}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,482]\u001b[0m Trial 754 finished with value: 51.58111931471589 and parameters: {'x': 1.2281917665113968, 'y': 0.7906176532978249}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,502]\u001b[0m Trial 755 finished with value: 0.34675535732918616 and parameters: {'x': 1.0290676183184626, 'y': 1.0001660124874006}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,544]\u001b[0m Trial 756 finished with value: 5.133879674440014 and parameters: {'x': 0.7390124121480129, 'y': 0.7712118925780821}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,592]\u001b[0m Trial 757 finished with value: 9.026456524279736 and parameters: {'x': 0.9639174446642349, 'y': 0.6287178897690309}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,634]\u001b[0m Trial 758 finished with value: 4.186266212281417 and parameters: {'x': 1.0821336069249503, 'y': 0.9665743924357066}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,676]\u001b[0m Trial 759 finished with value: 9.023709120358731 and parameters: {'x': 0.9002954296434403, 'y': 1.1107612415943064}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,717]\u001b[0m Trial 760 finished with value: 3.131160483937664 and parameters: {'x': 0.8306990936549522, 'y': 0.86620006808029}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,758]\u001b[0m Trial 761 finished with value: 8.971154630598893 and parameters: {'x': 0.9943652074460286, 'y': 0.689243837797209}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,800]\u001b[0m Trial 762 finished with value: 1044.8215015561632 and parameters: {'x': 1.1411759379195126, 'y': -1.930056524838967}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,845]\u001b[0m Trial 763 finished with value: 3.354599505957314 and parameters: {'x': 0.9126254508532391, 'y': 1.015832342358807}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,887]\u001b[0m Trial 764 finished with value: 245.3653847872599 and parameters: {'x': 1.5732045253063074, 'y': 0.9096072770952798}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,935]\u001b[0m Trial 765 finished with value: 10.497107593495215 and parameters: {'x': 1.0501240966006664, 'y': 0.7788069924376195}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,966]\u001b[0m Trial 766 finished with value: 36.85919462438058 and parameters: {'x': 0.7712214939006251, 'y': 1.2014691263727348}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:08,987]\u001b[0m Trial 767 finished with value: 68.4866080969938 and parameters: {'x': -0.15896167946039508, 'y': 0.844679676598255}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,007]\u001b[0m Trial 768 finished with value: 9.360273469002601 and parameters: {'x': 0.8681275181821143, 'y': 1.0593066892281064}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,023]\u001b[0m Trial 769 finished with value: 0.058997655720048114 and parameters: {'x': 0.9607004936262459, 'y': 0.9469148365518766}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,046]\u001b[0m Trial 770 finished with value: 57.670739676762636 and parameters: {'x': 1.220700967216856, 'y': 0.7310190864250916}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,065]\u001b[0m Trial 771 finished with value: 0.01352638195940158 and parameters: {'x': 1.0636240043948448, 'y': 1.1410317137759836}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,084]\u001b[0m Trial 772 finished with value: 2.4426747529973714 and parameters: {'x': 1.155936398363556, 'y': 1.1806782309794623}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,104]\u001b[0m Trial 773 finished with value: 4.755997973029218 and parameters: {'x': 1.2587301294009738, 'y': 1.3678592404110956}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,123]\u001b[0m Trial 774 finished with value: 748.9796118931902 and parameters: {'x': -1.9894404225733158, 'y': 1.237500320061515}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,140]\u001b[0m Trial 775 finished with value: 0.07526538894925976 and parameters: {'x': 1.0910367938306769, 'y': 1.1644812369764699}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,162]\u001b[0m Trial 776 finished with value: 60.79961791705818 and parameters: {'x': 1.389966645156529, 'y': 1.1532419445724316}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,181]\u001b[0m Trial 777 finished with value: 1.3587319317461322 and parameters: {'x': 1.063996210929919, 'y': 1.2484767858551074}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,201]\u001b[0m Trial 778 finished with value: 10.936580852498722 and parameters: {'x': 1.1811472716267961, 'y': 1.0649003595363289}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,222]\u001b[0m Trial 779 finished with value: 7.352318970467752 and parameters: {'x': 1.0128603708854798, 'y': 1.2970346802578283}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,241]\u001b[0m Trial 780 finished with value: 4.5821698203998675 and parameters: {'x': 1.1487541883593892, 'y': 1.1060936365424205}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,263]\u001b[0m Trial 781 finished with value: 49.915296751093436 and parameters: {'x': 1.3169470867535569, 'y': 1.0285533322420843}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,282]\u001b[0m Trial 782 finished with value: 0.2389280397222626 and parameters: {'x': 1.0788895858059828, 'y': 1.1157632863556721}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,300]\u001b[0m Trial 783 finished with value: 0.044719504144785906 and parameters: {'x': 0.9951508658706412, 'y': 1.01146667201139}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,322]\u001b[0m Trial 784 finished with value: 14.136757536183996 and parameters: {'x': 0.9618096390420878, 'y': 1.3010471825372858}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,344]\u001b[0m Trial 785 finished with value: 5.91083005854684 and parameters: {'x': 0.8522034276751075, 'y': 0.9689230174362632}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,365]\u001b[0m Trial 786 finished with value: 0.15145035936281412 and parameters: {'x': 1.049018638283404, 'y': 1.1390467780784812}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,388]\u001b[0m Trial 787 finished with value: 3.4818653393693104 and parameters: {'x': 1.1206281481366775, 'y': 1.069600191187254}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,409]\u001b[0m Trial 788 finished with value: 4.898495700260684 and parameters: {'x': 0.9132360505093242, 'y': 0.6128447603121717}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,428]\u001b[0m Trial 789 finished with value: 12.51976187834083 and parameters: {'x': 0.7487491733367574, 'y': 0.9135649072270995}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,446]\u001b[0m Trial 790 finished with value: 209.68821228043203 and parameters: {'x': 1.020682152514263, 'y': -0.4062679717455523}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,465]\u001b[0m Trial 791 finished with value: 9.655963887793696 and parameters: {'x': 0.8160875582956207, 'y': 0.9761946490939253}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,487]\u001b[0m Trial 792 finished with value: 7.742191288875027 and parameters: {'x': 0.9592814250675781, 'y': 1.19843899147372}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,509]\u001b[0m Trial 793 finished with value: 0.4681669972937107 and parameters: {'x': 1.2041931841866016, 'y': 1.3847763903477288}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,528]\u001b[0m Trial 794 finished with value: 0.8143496545175987 and parameters: {'x': 0.8712803675030809, 'y': 0.8484480608902587}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,548]\u001b[0m Trial 795 finished with value: 28.31604908304615 and parameters: {'x': 1.1045141390916666, 'y': 0.6879258649182068}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,565]\u001b[0m Trial 796 finished with value: 0.4062343823027419 and parameters: {'x': 1.0079350906550042, 'y': 1.0796647260094643}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,588]\u001b[0m Trial 797 finished with value: 16.191076683912513 and parameters: {'x': 0.7241597695054951, 'y': 0.9258421591411677}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,608]\u001b[0m Trial 798 finished with value: 0.2205544574074091 and parameters: {'x': 0.9235905980936951, 'y': 0.8066821284864649}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,626]\u001b[0m Trial 799 finished with value: 9.395590040556069 and parameters: {'x': 0.8390872850125048, 'y': 1.0101670821064457}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,642]\u001b[0m Trial 800 finished with value: 5.400278239349929 and parameters: {'x': 1.0668053863263909, 'y': 0.9057847898180128}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,664]\u001b[0m Trial 801 finished with value: 2.8550923271983515 and parameters: {'x': 0.9664296216362621, 'y': 0.7650493805023754}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,685]\u001b[0m Trial 802 finished with value: 8.341428052208565 and parameters: {'x': 0.9122273432907804, 'y': 1.1208406271321911}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,705]\u001b[0m Trial 803 finished with value: 72.65926960089456 and parameters: {'x': 1.2074521077463614, 'y': 0.6057890025601967}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,725]\u001b[0m Trial 804 finished with value: 38.71659772055837 and parameters: {'x': 0.7884538512026733, 'y': 1.2435263900769304}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,742]\u001b[0m Trial 805 finished with value: 0.6712602775571299 and parameters: {'x': 1.0574794200286806, 'y': 1.0365341245162336}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,762]\u001b[0m Trial 806 finished with value: 0.34884966686924623 and parameters: {'x': 0.8803525106845966, 'y': 0.7171816162831794}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,786]\u001b[0m Trial 807 finished with value: 1.0487082774380887 and parameters: {'x': 0.9843073335607604, 'y': 0.8664664925967861}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,810]\u001b[0m Trial 808 finished with value: 9.8307197742105 and parameters: {'x': 0.8036476331228402, 'y': 0.9587738784490978}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,831]\u001b[0m Trial 809 finished with value: 23.239948037696163 and parameters: {'x': 1.1260041285824145, 'y': 0.7859717057730363}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,851]\u001b[0m Trial 810 finished with value: 1.3752169675653096 and parameters: {'x': 1.0184683619290127, 'y': 1.154532905937797}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,871]\u001b[0m Trial 811 finished with value: 0.2526988683823705 and parameters: {'x': 0.9074994803060519, 'y': 0.8729660871825721}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,892]\u001b[0m Trial 812 finished with value: 41.533185835727465 and parameters: {'x': 1.2853015684551305, 'y': 1.0081694853641712}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,913]\u001b[0m Trial 813 finished with value: 6.839169469058725 and parameters: {'x': 0.9723402050936608, 'y': 0.6839420441618924}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,931]\u001b[0m Trial 814 finished with value: 1.5693232748571169 and parameters: {'x': 1.0998403561360637, 'y': 1.0847746652189196}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,954]\u001b[0m Trial 815 finished with value: 9.68358679018078 and parameters: {'x': 0.7200503990019942, 'y': 0.8283953902529448}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:09,983]\u001b[0m Trial 816 finished with value: 3.02680879991563 and parameters: {'x': 0.883192508298633, 'y': 0.9536137067836465}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,006]\u001b[0m Trial 817 finished with value: 38.223901036571675 and parameters: {'x': 0.8104451381244828, 'y': 1.2747854793727373}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,026]\u001b[0m Trial 818 finished with value: 8.256131179830259 and parameters: {'x': 1.0291890953662182, 'y': 0.7719101777228448}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,048]\u001b[0m Trial 819 finished with value: 18.14224373232514 and parameters: {'x': 1.1597046198937293, 'y': 0.9192771876256033}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,071]\u001b[0m Trial 820 finished with value: 7.531398550193693 and parameters: {'x': 0.9453249564985544, 'y': 0.6192598064028404}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,088]\u001b[0m Trial 821 finished with value: 8.605782765863138 and parameters: {'x': 0.8578591926143608, 'y': 1.0289339770035282}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,110]\u001b[0m Trial 822 finished with value: 0.14376836619717728 and parameters: {'x': 1.0634214765412955, 'y': 1.1682478640860454}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,131]\u001b[0m Trial 823 finished with value: 3.3553486570316715 and parameters: {'x': 0.725147049089198, 'y': 0.7069405484868049}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,150]\u001b[0m Trial 824 finished with value: 112.76869811387931 and parameters: {'x': 0.9613961165988755, 'y': 1.9862015476199204}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,170]\u001b[0m Trial 825 finished with value: 369.55349761044306 and parameters: {'x': 1.020063766857881, 'y': -0.8818462926296379}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,189]\u001b[0m Trial 826 finished with value: 3.945013507784644 and parameters: {'x': 0.8052385541455983, 'y': 0.8460725168656982}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,206]\u001b[0m Trial 827 finished with value: 8.467096195995445 and parameters: {'x': 1.1734379241289479, 'y': 1.0864911510641326}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,227]\u001b[0m Trial 828 finished with value: 1.8575436860842856 and parameters: {'x': 0.9040162218319511, 'y': 0.9531986595781922}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,246]\u001b[0m Trial 829 finished with value: 9.843829493002858 and parameters: {'x': 1.0973134346189974, 'y': 0.8904989487320041}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,266]\u001b[0m Trial 830 finished with value: 1.3626843166519873 and parameters: {'x': 0.9423907483758696, 'y': 0.7715084922597231}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,284]\u001b[0m Trial 831 finished with value: 0.002162236180107021 and parameters: {'x': 1.0145265406853317, 'y': 1.0248468449186001}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,305]\u001b[0m Trial 832 finished with value: 4.019924636610534 and parameters: {'x': 1.115713564978864, 'y': 1.0446534505647618}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,325]\u001b[0m Trial 833 finished with value: 17.369398929564415 and parameters: {'x': 1.2328706011891142, 'y': 1.1038549085802514}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,344]\u001b[0m Trial 834 finished with value: 0.7885941040505557 and parameters: {'x': 1.040754901409621, 'y': 0.994461512637835}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,364]\u001b[0m Trial 835 finished with value: 1.4052940531529403 and parameters: {'x': 1.156211292146038, 'y': 1.2193131873011949}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,383]\u001b[0m Trial 836 finished with value: 0.776480766942657 and parameters: {'x': 1.0251322947952062, 'y': 1.138978520547604}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,403]\u001b[0m Trial 837 finished with value: 3.954286640859341 and parameters: {'x': 1.1032403927173793, 'y': 1.0185536631734335}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,425]\u001b[0m Trial 838 finished with value: 90.62264631455696 and parameters: {'x': 1.3827550938311373, 'y': 0.96082215812792}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,446]\u001b[0m Trial 839 finished with value: 0.7604441085353033 and parameters: {'x': 0.9896733701558558, 'y': 1.0666507115073178}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,471]\u001b[0m Trial 840 finished with value: 20.824160129869025 and parameters: {'x': 1.273793814377342, 'y': 1.1670378158980612}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,492]\u001b[0m Trial 841 finished with value: 3.359674796654388 and parameters: {'x': 1.0666200828378631, 'y': 1.3208514493820862}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,513]\u001b[0m Trial 842 finished with value: 87.01218727715097 and parameters: {'x': -0.06687668542506686, 'y': 0.9311545369313561}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,761]\u001b[0m Trial 843 finished with value: 0.35981320656079085 and parameters: {'x': 0.9765356483090707, 'y': 1.0135603934846324}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,787]\u001b[0m Trial 844 finished with value: 29.599138702435805 and parameters: {'x': 1.196792373775492, 'y': 0.888617113190091}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,808]\u001b[0m Trial 845 finished with value: 43.495744438639996 and parameters: {'x': 0.8987283872508575, 'y': 1.4671479913832703}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,831]\u001b[0m Trial 846 finished with value: 1.2784197782991504 and parameters: {'x': 0.9951164925638599, 'y': 1.1033230059751753}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,852]\u001b[0m Trial 847 finished with value: 0.22653731890470918 and parameters: {'x': 1.1208039358813753, 'y': 1.2101641161107037}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,873]\u001b[0m Trial 848 finished with value: 1.4082452207818112 and parameters: {'x': 0.9294847692502197, 'y': 0.9824017532110164}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,897]\u001b[0m Trial 849 finished with value: 5.513031767581379 and parameters: {'x': 1.0491506466707852, 'y': 0.8659700670429402}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,917]\u001b[0m Trial 850 finished with value: 8.826546159062518 and parameters: {'x': 0.8863095105991111, 'y': 1.0824219749968536}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,939]\u001b[0m Trial 851 finished with value: 1.077078554667239 and parameters: {'x': 1.018345873146449, 'y': 0.93326213868304}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,966]\u001b[0m Trial 852 finished with value: 23.710978231691097 and parameters: {'x': 1.1429273296162825, 'y': 0.8195534897445982}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:10,991]\u001b[0m Trial 853 finished with value: 2.2439157292004452 and parameters: {'x': 0.9391844527464506, 'y': 1.0317409876654118}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,019]\u001b[0m Trial 854 finished with value: 24.418154288102926 and parameters: {'x': 0.8702256778256647, 'y': 1.2512695811550183}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,042]\u001b[0m Trial 855 finished with value: 0.2664402488497707 and parameters: {'x': 1.0496817165962575, 'y': 1.1532099093058386}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,064]\u001b[0m Trial 856 finished with value: 0.012148271161167172 and parameters: {'x': 0.9584696348807401, 'y': 0.9084544866718841}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,089]\u001b[0m Trial 857 finished with value: 14.929412985945616 and parameters: {'x': 1.168250394806376, 'y': 0.9787894933512534}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,109]\u001b[0m Trial 858 finished with value: 9.324117892180938 and parameters: {'x': 1.100725768342878, 'y': 0.9064092059963896}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,132]\u001b[0m Trial 859 finished with value: 0.21558164755399659 and parameters: {'x': 1.001955182561328, 'y': 1.0503445469830044}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,154]\u001b[0m Trial 860 finished with value: 0.05562307749334587 and parameters: {'x': 0.9691599384019035, 'y': 0.9626530237734984}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,175]\u001b[0m Trial 861 finished with value: 39.933463004439055 and parameters: {'x': 1.2312695046246418, 'y': 0.8845186356383994}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,201]\u001b[0m Trial 862 finished with value: 0.07025384603291575 and parameters: {'x': 1.0659737436275014, 'y': 1.1106287714159946}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,222]\u001b[0m Trial 863 finished with value: 0.8419535912686574 and parameters: {'x': 0.9491181833179188, 'y': 0.9924421710806777}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,245]\u001b[0m Trial 864 finished with value: 9.0952101370858 and parameters: {'x': 1.0670689799893143, 'y': 0.8371281337189596}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,266]\u001b[0m Trial 865 finished with value: 0.08228873911525532 and parameters: {'x': 1.003035622010954, 'y': 1.034764866653923}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,287]\u001b[0m Trial 866 finished with value: 16.38871627989367 and parameters: {'x': 1.149057994688453, 'y': 0.9157789885263984}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,308]\u001b[0m Trial 867 finished with value: 10.591941504752342 and parameters: {'x': 0.9134410942954798, 'y': 1.1597121353599429}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,330]\u001b[0m Trial 868 finished with value: 82.99457169965696 and parameters: {'x': 0.9685325283907356, 'y': 1.8490633896776028}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,357]\u001b[0m Trial 869 finished with value: 10.954152292895957 and parameters: {'x': 0.865918923845844, 'y': 1.0805144571494976}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,379]\u001b[0m Trial 870 finished with value: 5.654465059161779 and parameters: {'x': 1.0850616970977478, 'y': 0.9397198836734907}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,397]\u001b[0m Trial 871 finished with value: 41.46893564638277 and parameters: {'x': 1.2142966016838759, 'y': 0.8309091159177315}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,417]\u001b[0m Trial 872 finished with value: 11.127257697158056 and parameters: {'x': 1.3069861622273016, 'y': 1.3760529692496006}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,439]\u001b[0m Trial 873 finished with value: 3.336861578041571 and parameters: {'x': 1.015031471921416, 'y': 1.2129534896363316}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,461]\u001b[0m Trial 874 finished with value: 7.313242009171218 and parameters: {'x': 0.8347987539219559, 'y': 0.9668139594563925}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,483]\u001b[0m Trial 875 finished with value: 0.004998112209663032 and parameters: {'x': 0.9317504012960383, 'y': 0.8663146181170713}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,504]\u001b[0m Trial 876 finished with value: 5.782723668053144 and parameters: {'x': 1.124708095395908, 'y': 1.024818938846488}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,524]\u001b[0m Trial 877 finished with value: 0.1296492404043623 and parameters: {'x': 0.967740502068814, 'y': 0.9006596429831463}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,546]\u001b[0m Trial 878 finished with value: 187.56119289621813 and parameters: {'x': 1.0569476861187943, 'y': -0.2523795691839369}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,566]\u001b[0m Trial 879 finished with value: 5.324737617230446 and parameters: {'x': 0.9306594114342226, 'y': 1.096776663698294}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,587]\u001b[0m Trial 880 finished with value: 11.708343009706113 and parameters: {'x': -1.1303690275905403, 'y': 1.0099679939995794}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,608]\u001b[0m Trial 881 finished with value: 3.70951055777222 and parameters: {'x': -0.9236474326051932, 'y': 0.8626593124778943}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,630]\u001b[0m Trial 882 finished with value: 1.0754149242706392 and parameters: {'x': 1.0169529185835513, 'y': 0.9305048823347681}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,652]\u001b[0m Trial 883 finished with value: 584.4133969110603 and parameters: {'x': 1.7981547399366187, 'y': 0.8172140619297883}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,678]\u001b[0m Trial 884 finished with value: 6.030916375666015 and parameters: {'x': 1.1116692368675172, 'y': 0.9904832721894841}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,699]\u001b[0m Trial 885 finished with value: 7.4026547783921846 and parameters: {'x': 1.18422162820111, 'y': 1.1309270533409812}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,717]\u001b[0m Trial 886 finished with value: 17.429249797751453 and parameters: {'x': 0.9236418637083633, 'y': 1.270527986860635}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,742]\u001b[0m Trial 887 finished with value: 50.70224803022244 and parameters: {'x': 1.0296969314816429, 'y': 1.7723246884217327}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,763]\u001b[0m Trial 888 finished with value: 0.9883637031494323 and parameters: {'x': 0.8934969668068472, 'y': 0.8971811925606318}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,785]\u001b[0m Trial 889 finished with value: 113.24799350830281 and parameters: {'x': 0.9749660304286677, 'y': -0.11361870031578908}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,805]\u001b[0m Trial 890 finished with value: 1.0553283706099434 and parameters: {'x': 1.0836180050981725, 'y': 1.0718396808682267}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,825]\u001b[0m Trial 891 finished with value: 13.212357039959732 and parameters: {'x': 0.7908013985356578, 'y': 0.9882524121939945}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,847]\u001b[0m Trial 892 finished with value: 1.0083535227300693 and parameters: {'x': 0.9593105776142579, 'y': 0.8199424485428699}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,865]\u001b[0m Trial 893 finished with value: 16.13370606341671 and parameters: {'x': 0.8753893892081981, 'y': 1.1677810932504242}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,886]\u001b[0m Trial 894 finished with value: 3.2577246366777812 and parameters: {'x': 1.041223810926215, 'y': 0.9037024281202168}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,912]\u001b[0m Trial 895 finished with value: 19.51410977357105 and parameters: {'x': 1.2289290789616951, 'y': 1.069112498882259}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,933]\u001b[0m Trial 896 finished with value: 350.20227933268893 and parameters: {'x': 1.113086419819195, 'y': -0.6323736823235477}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,955]\u001b[0m Trial 897 finished with value: 0.003084879246022172 and parameters: {'x': 0.9746672078331491, 'y': 0.9549189677480284}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,975]\u001b[0m Trial 898 finished with value: 8.677898108732562 and parameters: {'x': 1.1617849415957167, 'y': 1.055606124098246}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:11,994]\u001b[0m Trial 899 finished with value: 1.9735194802797482 and parameters: {'x': 1.0278353377271587, 'y': 1.1968999108965583}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,025]\u001b[0m Trial 900 finished with value: 0.11298434770376715 and parameters: {'x': 0.9634560702033141, 'y': 0.9616615018419231}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,054]\u001b[0m Trial 901 finished with value: 0.2918094936571033 and parameters: {'x': 1.117903976580101, 'y': 1.3024262948843428}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,077]\u001b[0m Trial 902 finished with value: 0.8035871896709158 and parameters: {'x': 1.0512962763025746, 'y': 1.0157277220080316}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,098]\u001b[0m Trial 903 finished with value: 26.785150379808332 and parameters: {'x': 1.2795534337913588, 'y': 1.1204688296772007}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,119]\u001b[0m Trial 904 finished with value: 4.530059050753011 and parameters: {'x': 0.8607441115165588, 'y': 0.9532637310755263}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,137]\u001b[0m Trial 905 finished with value: 0.7219984093798042 and parameters: {'x': 0.9757646314067625, 'y': 0.8671806955709288}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,160]\u001b[0m Trial 906 finished with value: 134.2999198508108 and parameters: {'x': 1.483660463068361, 'y': 1.0433796706943326}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,186]\u001b[0m Trial 907 finished with value: 2.98461091305833 and parameters: {'x': 0.7870540831917856, 'y': 0.7908969760180392}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,209]\u001b[0m Trial 908 finished with value: 7.995856224951333 and parameters: {'x': 0.9185064959756303, 'y': 1.1263061782556594}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,232]\u001b[0m Trial 909 finished with value: 12.17113837503276 and parameters: {'x': 1.1458019093228198, 'y': 0.9642952330221395}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,254]\u001b[0m Trial 910 finished with value: 4.053086150815497 and parameters: {'x': 1.0463376576092447, 'y': 0.8935530481538162}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,272]\u001b[0m Trial 911 finished with value: 19.505156098244903 and parameters: {'x': 0.8874916415143621, 'y': 1.2291445051118788}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,297]\u001b[0m Trial 912 finished with value: 0.22551245709267467 and parameters: {'x': 0.9910195774571896, 'y': 1.0295994626645946}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,319]\u001b[0m Trial 913 finished with value: 13.653069802872045 and parameters: {'x': 1.0864488917084545, 'y': 0.8109717304076085}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,341]\u001b[0m Trial 914 finished with value: 24.707650220118662 and parameters: {'x': 1.203079148888912, 'y': 0.9507465508584153}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,362]\u001b[0m Trial 915 finished with value: 16.47087027857089 and parameters: {'x': 0.8363363414974704, 'y': 1.1049715398379845}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,386]\u001b[0m Trial 916 finished with value: 0.6778032370462833 and parameters: {'x': 0.9812741910922907, 'y': 0.8805915304798566}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,404]\u001b[0m Trial 917 finished with value: 2.9782674564917517 and parameters: {'x': 0.7765723228376974, 'y': 0.7741887242994404}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,425]\u001b[0m Trial 918 finished with value: 56.98598584054096 and parameters: {'x': -0.5275086808506754, 'y': 1.0175400736391658}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,449]\u001b[0m Trial 919 finished with value: 0.26541139637616445 and parameters: {'x': 0.9324074301634715, 'y': 0.9204563718568948}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,469]\u001b[0m Trial 920 finished with value: 0.24328356729126985 and parameters: {'x': 1.0584958228783534, 'y': 1.1693890955092847}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,492]\u001b[0m Trial 921 finished with value: 9.454479631501606 and parameters: {'x': 0.8712489769135962, 'y': 1.0662864788593966}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,511]\u001b[0m Trial 922 finished with value: 0.8440484026446781 and parameters: {'x': 0.961158009027, 'y': 0.8320347568622427}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,533]\u001b[0m Trial 923 finished with value: 13.241037388856515 and parameters: {'x': 1.1528793515232247, 'y': 0.9655697273700157}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,556]\u001b[0m Trial 924 finished with value: 1080.7404260081646 and parameters: {'x': 1.3692482308779912, 'y': -1.4124135799031863}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,578]\u001b[0m Trial 925 finished with value: 5.423193491866836 and parameters: {'x': 1.0339501174027856, 'y': 1.301905607369402}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,601]\u001b[0m Trial 926 finished with value: 11.355626981603375 and parameters: {'x': 0.912672397728224, 'y': 1.1698388451519781}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,625]\u001b[0m Trial 927 finished with value: 2.4968553986338202 and parameters: {'x': 0.787185154191756, 'y': 0.7762352171804797}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,646]\u001b[0m Trial 928 finished with value: 4.330227750176725 and parameters: {'x': 1.0946154599907667, 'y': 0.9903062228794262}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,665]\u001b[0m Trial 929 finished with value: 68.2096474542368 and parameters: {'x': -0.2804307301593899, 'y': 0.8945466867127368}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,686]\u001b[0m Trial 930 finished with value: 1.025662801673996 and parameters: {'x': 0.9909145027802961, 'y': 1.0831824881946963}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,711]\u001b[0m Trial 931 finished with value: 11.505323857041704 and parameters: {'x': 0.714790049281089, 'y': 0.8489185887698503}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,733]\u001b[0m Trial 932 finished with value: 24.35236128299478 and parameters: {'x': 1.225512364550886, 'y': 1.0089149842888439}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,754]\u001b[0m Trial 933 finished with value: 3.139742445328676 and parameters: {'x': 0.8603071956210807, 'y': 0.9167701523775655}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,777]\u001b[0m Trial 934 finished with value: 3.3184179069718165 and parameters: {'x': 1.0109748132033543, 'y': 1.2042320190247784}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,799]\u001b[0m Trial 935 finished with value: 8.26736102836903 and parameters: {'x': 1.131445049180455, 'y': 1.567397480441541}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,823]\u001b[0m Trial 936 finished with value: 10.787277501595646 and parameters: {'x': 0.8815247478773778, 'y': 1.1053120396791274}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,843]\u001b[0m Trial 937 finished with value: 0.6803115284434083 and parameters: {'x': 0.9523571129411108, 'y': 0.8246407840334595}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,865]\u001b[0m Trial 938 finished with value: 16.78872416715915 and parameters: {'x': 1.067685357616372, 'y': 0.7302674752036942}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,883]\u001b[0m Trial 939 finished with value: 9.199217852479082 and parameters: {'x': 0.8032448750926955, 'y': 0.947865595605906}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,908]\u001b[0m Trial 940 finished with value: 3.666200005485273 and parameters: {'x': 0.9177681750710354, 'y': 1.0335949974356482}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,930]\u001b[0m Trial 941 finished with value: 6.442899940225147 and parameters: {'x': 1.0110919799079954, 'y': 0.7684807341656266}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,952]\u001b[0m Trial 942 finished with value: 0.08260830296306823 and parameters: {'x': 1.1818693778226779, 'y': 1.4190709743404444}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,970]\u001b[0m Trial 943 finished with value: 9.450409441772983 and parameters: {'x': 1.092595712339255, 'y': 0.8864896923342611}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:12,996]\u001b[0m Trial 944 finished with value: 6.246122349683014 and parameters: {'x': 0.9393775955452421, 'y': 1.13227916682505}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,021]\u001b[0m Trial 945 finished with value: 8.004072014045907 and parameters: {'x': 0.8329385967218571, 'y': 0.9762077125919186}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,043]\u001b[0m Trial 946 finished with value: 25.329783905393164 and parameters: {'x': 0.7376878435050398, 'y': 1.0467863412941134}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,073]\u001b[0m Trial 947 finished with value: 3.7041857875930453 and parameters: {'x': 1.0203608179463677, 'y': 1.233588042768629}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,096]\u001b[0m Trial 948 finished with value: 154.12955308313965 and parameters: {'x': -1.4395024469165543, 'y': 0.854881868434648}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,118]\u001b[0m Trial 949 finished with value: 2.0668332319376486 and parameters: {'x': 0.8977498371893553, 'y': 0.9493555420034746}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,142]\u001b[0m Trial 950 finished with value: 90.36871577701324 and parameters: {'x': 1.305767897351084, 'y': 0.7548970648681728}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,161]\u001b[0m Trial 951 finished with value: 1.3694840185511659 and parameters: {'x': 1.1080964063517718, 'y': 1.3444022863177512}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,187]\u001b[0m Trial 952 finished with value: 2.080532097106902 and parameters: {'x': 0.9863461289168629, 'y': 1.1171127204632987}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,206]\u001b[0m Trial 953 finished with value: 1.1155287052932217 and parameters: {'x': 0.8555095630216977, 'y': 0.8365221933716975}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,229]\u001b[0m Trial 954 finished with value: 256.42799235534363 and parameters: {'x': 1.6216175883055148, 'y': 1.0295136581996207}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,248]\u001b[0m Trial 955 finished with value: 4.132493677419712 and parameters: {'x': 1.059464145508053, 'y': 0.9192659071980017}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,273]\u001b[0m Trial 956 finished with value: 41.55238497579437 and parameters: {'x': 1.1754408367784293, 'y': 0.7372885551367985}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,296]\u001b[0m Trial 957 finished with value: 7.88524784506147 and parameters: {'x': 0.9509298567440242, 'y': 1.185031549644396}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,319]\u001b[0m Trial 958 finished with value: 15.35034069843699 and parameters: {'x': 0.7887704207053574, 'y': 1.0133840667037985}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,341]\u001b[0m Trial 959 finished with value: 3.9361735742674555 and parameters: {'x': 1.0327475485261175, 'y': 0.8681966046414666}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,363]\u001b[0m Trial 960 finished with value: 1.8736371090684623 and parameters: {'x': 0.9141934104713314, 'y': 0.9723612434580509}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,385]\u001b[0m Trial 961 finished with value: 15.136868130941343 and parameters: {'x': 0.8390381930781549, 'y': 1.0927132647496052}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,404]\u001b[0m Trial 962 finished with value: 55.78792104943899 and parameters: {'x': 1.2370510741517433, 'y': 0.7837585033832168}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,428]\u001b[0m Trial 963 finished with value: 0.08903935619762378 and parameters: {'x': 0.9857536159829035, 'y': 0.9419047562243892}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,451]\u001b[0m Trial 964 finished with value: 10.218723608746885 and parameters: {'x': 0.7171501608947848, 'y': 0.8327179141834007}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,474]\u001b[0m Trial 965 finished with value: 2.2971197100478684 and parameters: {'x': 1.1013916355458817, 'y': 1.0618405390366608}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,497]\u001b[0m Trial 966 finished with value: 0.8954916325138949 and parameters: {'x': 0.8999017175782501, 'y': 0.9039226212974721}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,517]\u001b[0m Trial 967 finished with value: 5.950984343994399 and parameters: {'x': 1.018959499905873, 'y': 1.282217489284186}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,539]\u001b[0m Trial 968 finished with value: 40.37692463623542 and parameters: {'x': 1.1490997945231547, 'y': 0.685176893174006}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,565]\u001b[0m Trial 969 finished with value: 6.444423619692454 and parameters: {'x': 0.945693096644945, 'y': 1.1481360315427844}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,588]\u001b[0m Trial 970 finished with value: 13.719175432977021 and parameters: {'x': 0.7916156263100538, 'y': 0.9964627008499156}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,612]\u001b[0m Trial 971 finished with value: 570.7178466342648 and parameters: {'x': -1.7946405636123441, 'y': 0.8481668804999966}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,633]\u001b[0m Trial 972 finished with value: 15.455588222892214 and parameters: {'x': 1.0770973300169324, 'y': 0.7670783038724747}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,653]\u001b[0m Trial 973 finished with value: 10.907691040001952 and parameters: {'x': 0.8608889146583696, 'y': 1.0711045579666234}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,683]\u001b[0m Trial 974 finished with value: 464.76183382849723 and parameters: {'x': 0.9881038004190364, 'y': -1.1794841113408883}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,708]\u001b[0m Trial 975 finished with value: 0.29020797669798304 and parameters: {'x': 0.9374151589685165, 'y': 0.9322533588203794}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,730]\u001b[0m Trial 976 finished with value: 712.8689996373406 and parameters: {'x': 1.9745585142288604, 'y': 1.2306998567610388}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,752]\u001b[0m Trial 977 finished with value: 10.781946653814565 and parameters: {'x': 1.06430418584051, 'y': 0.8044476244308723}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,774]\u001b[0m Trial 978 finished with value: 16.654161404640934 and parameters: {'x': 1.18345173353234, 'y': 0.9928754469094451}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,801]\u001b[0m Trial 979 finished with value: 3.5589817832724107 and parameters: {'x': 0.838467975141855, 'y': 0.8909883598791878}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,828]\u001b[0m Trial 980 finished with value: 3.2481641475268983 and parameters: {'x': 0.9837395177213168, 'y': 1.1479627424718384}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,849]\u001b[0m Trial 981 finished with value: 5.023272075667418 and parameters: {'x': 0.9123687160012702, 'y': 1.0563718666378676}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,872]\u001b[0m Trial 982 finished with value: 3.451027767281246 and parameters: {'x': 0.716575141694834, 'y': 0.697074544644638}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,895]\u001b[0m Trial 983 finished with value: 10.206274295309429 and parameters: {'x': 1.1259160050037027, 'y': 0.9484624863568148}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,917]\u001b[0m Trial 984 finished with value: 7.66220403381671 and parameters: {'x': 1.0518416624266138, 'y': 0.8296125681110506}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,942]\u001b[0m Trial 985 finished with value: 20.103510814350972 and parameters: {'x': 0.8074494381680033, 'y': 1.0999303418457398}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,965]\u001b[0m Trial 986 finished with value: 54.80956217422493 and parameters: {'x': 1.2823707398285313, 'y': 0.904678600972842}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:13,987]\u001b[0m Trial 987 finished with value: 0.01772159635850123 and parameters: {'x': 1.0014098403410014, 'y': 0.9895101662720246}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:14,009]\u001b[0m Trial 988 finished with value: 6.755326156505314 and parameters: {'x': 1.1385022712965465, 'y': 1.0366466103640788}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:14,034]\u001b[0m Trial 989 finished with value: 997.3859179944718 and parameters: {'x': 1.2123958027814914, 'y': -1.6881667237992382}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:14,055]\u001b[0m Trial 990 finished with value: 0.5845594912490321 and parameters: {'x': 1.0256804367310166, 'y': 1.128433708331733}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:14,083]\u001b[0m Trial 991 finished with value: 4.478945001408983 and parameters: {'x': 1.095118493016659, 'y': 0.9878631933062293}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:14,111]\u001b[0m Trial 992 finished with value: 5.417517171910179 and parameters: {'x': 1.005913305769852, 'y': 1.2446164326962377}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:14,135]\u001b[0m Trial 993 finished with value: 1.7704646033011027 and parameters: {'x': 1.0847116476819894, 'y': 1.0438104833281139}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:14,158]\u001b[0m Trial 994 finished with value: 8.061258239205744 and parameters: {'x': 0.9488667091724144, 'y': 1.1842255339799812}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:14,180]\u001b[0m Trial 995 finished with value: 19.887357412022652 and parameters: {'x': 1.197475741793732, 'y': 0.9884331609279742}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:14,204]\u001b[0m Trial 996 finished with value: 0.07876392013522829 and parameters: {'x': 1.0409796589245812, 'y': 1.1114027616058624}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:14,227]\u001b[0m Trial 997 finished with value: 0.0018254699129768193 and parameters: {'x': 0.9796838779197314, 'y': 0.9635391242025642}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:14,250]\u001b[0m Trial 998 finished with value: 8.74639401665548 and parameters: {'x': 1.142354044276804, 'y': 1.0095725366338573}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n",
            "\u001b[32m[I 2022-05-01 04:24:14,272]\u001b[0m Trial 999 finished with value: 28.379542755578207 and parameters: {'x': 1.272572273921863, 'y': 1.0874134344649167}. Best is trial 585 with value: 0.0018008451828139063.\u001b[0m\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'x': 0.9987281860356447, 'y': 1.0016997199742996}"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "#@ IMPLEMENTATION OF OPTUNA:\n",
        "study = optuna.create_study()                               # Initializing study.\n",
        "study.optimize(objective, n_trials=1000)                    # Optimization.\n",
        "study.best_params                                           # Finding best parameters."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MtV7MIN5i8sV",
        "outputId": "b1712df7-e741-486f-f82b-bc914d1654ac"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[32m[I 2022-04-30 04:23:49,919]\u001b[0m A new study created in memory with name: no-name-aa3cafb8-d1d4-40e8-90a7-8fa4f7fc3417\u001b[0m\n",
            "Trial:\n",
            "loading weights file https://huggingface.co/distilbert-base-uncased/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/9c169103d7e5a73936dd2b627e42851bec0831212b677c637033ee4bce9ab5ee.126183e36667471617ae2f0835fab707baa54b731f991507ebbb55ea85adb12a\n",
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_projector.weight', 'vocab_projector.bias', 'vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.bias', 'classifier.weight', 'pre_classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "***** Running training *****\n",
            "  Num examples = 15250\n",
            "  Num Epochs = 9\n",
            "  Instantaneous batch size per device = 48\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 48\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 2862\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2862' max='2862' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2862/2862 09:04, Epoch 9/9]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.205722</td>\n",
              "      <td>0.605161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.325300</td>\n",
              "      <td>0.098610</td>\n",
              "      <td>0.833548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.325300</td>\n",
              "      <td>0.064387</td>\n",
              "      <td>0.887097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.113300</td>\n",
              "      <td>0.049000</td>\n",
              "      <td>0.903548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.071600</td>\n",
              "      <td>0.040427</td>\n",
              "      <td>0.912258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.071600</td>\n",
              "      <td>0.035647</td>\n",
              "      <td>0.919032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.056200</td>\n",
              "      <td>0.032770</td>\n",
              "      <td>0.918710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.049200</td>\n",
              "      <td>0.031117</td>\n",
              "      <td>0.922258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.049200</td>\n",
              "      <td>0.030543</td>\n",
              "      <td>0.923226</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
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          "output_type": "stream",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "Saving model checkpoint to distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-500\n",
            "Configuration saved in distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-500/config.json\n",
            "Model weights saved in distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-500/pytorch_model.bin\n",
            "tokenizer config file saved in distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-500/tokenizer_config.json\n",
            "Special tokens file saved in distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-500/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "Saving model checkpoint to distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-1000\n",
            "Configuration saved in distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-1000/config.json\n",
            "Model weights saved in distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-1000/pytorch_model.bin\n",
            "tokenizer config file saved in distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-1000/tokenizer_config.json\n",
            "Special tokens file saved in distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-1000/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "Saving model checkpoint to distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-1500\n",
            "Configuration saved in distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-1500/config.json\n",
            "Model weights saved in distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-1500/pytorch_model.bin\n",
            "tokenizer config file saved in distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-1500/tokenizer_config.json\n",
            "Special tokens file saved in distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-1500/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "Saving model checkpoint to distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-2000\n",
            "Configuration saved in distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-2000/config.json\n",
            "Model weights saved in distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-2000/pytorch_model.bin\n",
            "tokenizer config file saved in distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-2000/tokenizer_config.json\n",
            "Special tokens file saved in distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-2000/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "Saving model checkpoint to distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-2500\n",
            "Configuration saved in distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-2500/config.json\n",
            "Model weights saved in distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-2500/pytorch_model.bin\n",
            "tokenizer config file saved in distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-2500/tokenizer_config.json\n",
            "Special tokens file saved in distilbert-base-uncased-finetuned-clinc/run-0/checkpoint-2500/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "\u001b[32m[I 2022-04-30 04:32:55,214]\u001b[0m Trial 0 finished with value: 0.9232258064516129 and parameters: {'num_train_epochs': 9, 'alpha': 0.4835192901842017, 'temperature': 10}. Best is trial 0 with value: 0.9232258064516129.\u001b[0m\n",
            "Trial:\n",
            "loading weights file https://huggingface.co/distilbert-base-uncased/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/9c169103d7e5a73936dd2b627e42851bec0831212b677c637033ee4bce9ab5ee.126183e36667471617ae2f0835fab707baa54b731f991507ebbb55ea85adb12a\n",
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_projector.weight', 'vocab_projector.bias', 'vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.bias', 'classifier.weight', 'pre_classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "***** Running training *****\n",
            "  Num examples = 15250\n",
            "  Num Epochs = 7\n",
            "  Instantaneous batch size per device = 48\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 48\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 2226\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2226' max='2226' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2226/2226 07:02, Epoch 7/7]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.227603</td>\n",
              "      <td>0.610645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.356400</td>\n",
              "      <td>0.108784</td>\n",
              "      <td>0.833548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.356400</td>\n",
              "      <td>0.070850</td>\n",
              "      <td>0.884839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.124900</td>\n",
              "      <td>0.054357</td>\n",
              "      <td>0.904194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.079800</td>\n",
              "      <td>0.045926</td>\n",
              "      <td>0.910968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.079800</td>\n",
              "      <td>0.041874</td>\n",
              "      <td>0.911935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.065000</td>\n",
              "      <td>0.040432</td>\n",
              "      <td>0.914194</td>\n",
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          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "Saving model checkpoint to distilbert-base-uncased-finetuned-clinc/run-1/checkpoint-500\n",
            "Configuration saved in distilbert-base-uncased-finetuned-clinc/run-1/checkpoint-500/config.json\n",
            "Model weights saved in distilbert-base-uncased-finetuned-clinc/run-1/checkpoint-500/pytorch_model.bin\n",
            "tokenizer config file saved in distilbert-base-uncased-finetuned-clinc/run-1/checkpoint-500/tokenizer_config.json\n",
            "Special tokens file saved in distilbert-base-uncased-finetuned-clinc/run-1/checkpoint-500/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "Saving model checkpoint to distilbert-base-uncased-finetuned-clinc/run-1/checkpoint-1000\n",
            "Configuration saved in distilbert-base-uncased-finetuned-clinc/run-1/checkpoint-1000/config.json\n",
            "Model weights saved in distilbert-base-uncased-finetuned-clinc/run-1/checkpoint-1000/pytorch_model.bin\n",
            "tokenizer config file saved in distilbert-base-uncased-finetuned-clinc/run-1/checkpoint-1000/tokenizer_config.json\n",
            "Special tokens file saved in distilbert-base-uncased-finetuned-clinc/run-1/checkpoint-1000/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "Saving model checkpoint to distilbert-base-uncased-finetuned-clinc/run-1/checkpoint-1500\n",
            "Configuration saved in distilbert-base-uncased-finetuned-clinc/run-1/checkpoint-1500/config.json\n",
            "Model weights saved in distilbert-base-uncased-finetuned-clinc/run-1/checkpoint-1500/pytorch_model.bin\n",
            "tokenizer config file saved in distilbert-base-uncased-finetuned-clinc/run-1/checkpoint-1500/tokenizer_config.json\n",
            "Special tokens file saved in distilbert-base-uncased-finetuned-clinc/run-1/checkpoint-1500/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "Saving model checkpoint to distilbert-base-uncased-finetuned-clinc/run-1/checkpoint-2000\n",
            "Configuration saved in distilbert-base-uncased-finetuned-clinc/run-1/checkpoint-2000/config.json\n",
            "Model weights saved in distilbert-base-uncased-finetuned-clinc/run-1/checkpoint-2000/pytorch_model.bin\n",
            "tokenizer config file saved in distilbert-base-uncased-finetuned-clinc/run-1/checkpoint-2000/tokenizer_config.json\n",
            "Special tokens file saved in distilbert-base-uncased-finetuned-clinc/run-1/checkpoint-2000/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "\u001b[32m[I 2022-04-30 04:39:58,994]\u001b[0m Trial 1 finished with value: 0.9141935483870968 and parameters: {'num_train_epochs': 7, 'alpha': 0.8749717280254017, 'temperature': 6}. Best is trial 0 with value: 0.9232258064516129.\u001b[0m\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "BestRun(run_id='0', objective=0.9232258064516129, hyperparameters={'num_train_epochs': 9, 'alpha': 0.4835192901842017, 'temperature': 10})\n"
          ]
        }
      ],
      "source": [
        "#@ DEFINING HYPERPARAMETER SPACE:\n",
        "def hp_space(trial):                                                                # Defining function. \n",
        "    return {\"num_train_epochs\": trial.suggest_int(\"num_train_epochs\", 5, 10),       # Training epochs.\n",
        "            \"alpha\": trial.suggest_float(\"alpha\", 0, 1),                            # Alpha parameter. \n",
        "            \"temperature\": trial.suggest_int(\"temperature\", 2, 20)}                 # Temperature parameter.\n",
        "\n",
        "#@ UPDATING DISTILLATION TRAINER:\n",
        "best_run = distilbert_trainer.hyperparameter_search(\n",
        "    n_trials=2, direction=\"maximize\", hp_space=hp_space)                           # Updating distillation trainer.\n",
        "print(best_run)                                                                     # Inspecting best parameters."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "HlVB2J5BkvZD",
        "outputId": "5e9d5382-5ab3-4154-9396-aca15a8187c8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "loading weights file https://huggingface.co/distilbert-base-uncased/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/9c169103d7e5a73936dd2b627e42851bec0831212b677c637033ee4bce9ab5ee.126183e36667471617ae2f0835fab707baa54b731f991507ebbb55ea85adb12a\n",
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_projector.weight', 'vocab_projector.bias', 'vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.bias', 'classifier.weight', 'pre_classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "loading weights file https://huggingface.co/distilbert-base-uncased/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/9c169103d7e5a73936dd2b627e42851bec0831212b677c637033ee4bce9ab5ee.126183e36667471617ae2f0835fab707baa54b731f991507ebbb55ea85adb12a\n",
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_projector.weight', 'vocab_projector.bias', 'vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.bias', 'classifier.weight', 'pre_classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "***** Running training *****\n",
            "  Num examples = 15250\n",
            "  Num Epochs = 9\n",
            "  Instantaneous batch size per device = 48\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 48\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 2862\n"
          ]
        },
        {
          "data": {
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2862' max='2862' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2862/2862 09:03, Epoch 9/9]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.685957</td>\n",
              "      <td>0.760968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.990800</td>\n",
              "      <td>0.899172</td>\n",
              "      <td>0.873548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.990800</td>\n",
              "      <td>0.521682</td>\n",
              "      <td>0.919677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.803300</td>\n",
              "      <td>0.356530</td>\n",
              "      <td>0.936129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.366300</td>\n",
              "      <td>0.283714</td>\n",
              "      <td>0.943226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.366300</td>\n",
              "      <td>0.249152</td>\n",
              "      <td>0.947097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.227100</td>\n",
              "      <td>0.231756</td>\n",
              "      <td>0.947742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.177600</td>\n",
              "      <td>0.224796</td>\n",
              "      <td>0.948387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.177600</td>\n",
              "      <td>0.222007</td>\n",
              "      <td>0.948387</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
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        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "Saving model checkpoint to distilbert-base-uncased-distilled-clinc/checkpoint-500\n",
            "Configuration saved in distilbert-base-uncased-distilled-clinc/checkpoint-500/config.json\n",
            "Model weights saved in distilbert-base-uncased-distilled-clinc/checkpoint-500/pytorch_model.bin\n",
            "tokenizer config file saved in distilbert-base-uncased-distilled-clinc/checkpoint-500/tokenizer_config.json\n",
            "Special tokens file saved in distilbert-base-uncased-distilled-clinc/checkpoint-500/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "Saving model checkpoint to distilbert-base-uncased-distilled-clinc/checkpoint-1000\n",
            "Configuration saved in distilbert-base-uncased-distilled-clinc/checkpoint-1000/config.json\n",
            "Model weights saved in distilbert-base-uncased-distilled-clinc/checkpoint-1000/pytorch_model.bin\n",
            "tokenizer config file saved in distilbert-base-uncased-distilled-clinc/checkpoint-1000/tokenizer_config.json\n",
            "Special tokens file saved in distilbert-base-uncased-distilled-clinc/checkpoint-1000/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "Saving model checkpoint to distilbert-base-uncased-distilled-clinc/checkpoint-1500\n",
            "Configuration saved in distilbert-base-uncased-distilled-clinc/checkpoint-1500/config.json\n",
            "Model weights saved in distilbert-base-uncased-distilled-clinc/checkpoint-1500/pytorch_model.bin\n",
            "tokenizer config file saved in distilbert-base-uncased-distilled-clinc/checkpoint-1500/tokenizer_config.json\n",
            "Special tokens file saved in distilbert-base-uncased-distilled-clinc/checkpoint-1500/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "Saving model checkpoint to distilbert-base-uncased-distilled-clinc/checkpoint-2000\n",
            "Configuration saved in distilbert-base-uncased-distilled-clinc/checkpoint-2000/config.json\n",
            "Model weights saved in distilbert-base-uncased-distilled-clinc/checkpoint-2000/pytorch_model.bin\n",
            "tokenizer config file saved in distilbert-base-uncased-distilled-clinc/checkpoint-2000/tokenizer_config.json\n",
            "Special tokens file saved in distilbert-base-uncased-distilled-clinc/checkpoint-2000/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "Saving model checkpoint to distilbert-base-uncased-distilled-clinc/checkpoint-2500\n",
            "Configuration saved in distilbert-base-uncased-distilled-clinc/checkpoint-2500/config.json\n",
            "Model weights saved in distilbert-base-uncased-distilled-clinc/checkpoint-2500/pytorch_model.bin\n",
            "tokenizer config file saved in distilbert-base-uncased-distilled-clinc/checkpoint-2500/tokenizer_config.json\n",
            "Special tokens file saved in distilbert-base-uncased-distilled-clinc/checkpoint-2500/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3100\n",
            "  Batch size = 48\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#@ UPDATING TRAINING ARGUMENTS:\n",
        "for k, v in best_run.hyperparameters.items():\n",
        "    setattr(student_training_args, k, v)                                    # Updating training arguments.\n",
        "\n",
        "#@ TRAINING DISTILLATION TRAINER:\n",
        "distilled_ckpt = \"distilbert-base-uncased-distilled-clinc\"                  # Initialization. \n",
        "student_training_args.output_dir = distilled_ckpt                           # Updating.\n",
        "\n",
        "#@ TRAINING DISTILLATION TRAINER:\n",
        "distil_trainer = DistillationTrainer(\n",
        "    model_init=student_init, teacher_model=teacher_model, \n",
        "    args=student_training_args, train_dataset=clinc_enc[\"train\"], \n",
        "    eval_dataset=clinc_enc[\"validation\"], \n",
        "    compute_metrics=compute_metrics, tokenizer=student_tokenizer)           # Initializing distillation trainer.\n",
        "distilbert_trainer.train();                                                 # Training distillation trainer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "BGZp3FLOnVH3"
      },
      "outputs": [],
      "source": [
        "#@ BENCHMARKING THE DISTILLED MODEL:\n",
        "distilled_ckpt = \"transformersbook/distilbert-base-uncased-distilled-clinc\"     # Initializing model checkpoint.\n",
        "pipe = pipeline(\"text-classification\", model=distilled_ckpt)                    # Initializing distilled model pipeline.\n",
        "optim_type = \"Distillation\"                                                     # Initialization.\n",
        "pb = PerformanceBenchmark(pipe, clinc[\"test\"], optim_type=optim_type)           # Inspecting performance benchmark.\n",
        "perf_metrics.update(pb.run_benchmark())                                         # Updating benchmark."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "H4gTjakpovOz",
        "outputId": "5b33c822-9c75-434a-d4f4-3fad27fc27ec"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@ VISUALIZING THE PERFORMANCE:\n",
        "plot_metrics(perf_metrics, optim_type)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R9jsvbXyDpd3"
      },
      "source": [
        "### **Models and Quantization**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "AyLfK_rN3JPM",
        "outputId": "d05ed08f-09fa-4593-de4d-b535fc20553f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#@ PLOTTING FREQUENCY DISTRIBUTION:\n",
        "state_dict = pipe.model.state_dict()                                                # Initializing model.\n",
        "weights = state_dict[\"distilbert.transformer.layer.0.attention.out_lin.weight\"]     # Initializing weights.\n",
        "plt.hist(weights.flatten().numpy(), bins=250, range=(-0.3, 0.3), edgecolor=\"C0\")    # Plotting histograms.\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O3zjLjj3EeU7",
        "outputId": "6ea9a963-f1a9-4f24-92ec-e42905503031"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ -5,  -8,   0,  ...,  -6,  -4,   8],\n",
              "        [  8,   3,   1,  ...,  -4,   7,   0],\n",
              "        [ -9,  -6,   5,  ...,   1,   5,  -3],\n",
              "        ...,\n",
              "        [  6,   0,  12,  ...,   0,   6,  -1],\n",
              "        [  0,  -2, -12,  ...,  12,  -7, -13],\n",
              "        [-13,  -1, -10,  ...,   8,   2,  -2]], dtype=torch.int8)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "#@ PROCESSING THE WEIGHTS:\n",
        "zero_point = 0                                                      # Initialization.\n",
        "scale = (weights.max() - weights.min()) / (127 -(-128))             # Initializing scaling factor.\n",
        "(weights / scale + zero_point).clamp(-128, 127).round().char()      # Quantization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KT0O1JOrz19Y",
        "outputId": "6306f2ea-515f-4b8d-ca54-16f32b087c68"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ -5,  -8,   0,  ...,  -6,  -4,   8],\n",
              "        [  8,   3,   1,  ...,  -4,   7,   0],\n",
              "        [ -9,  -6,   5,  ...,   1,   5,  -3],\n",
              "        ...,\n",
              "        [  6,   0,  12,  ...,   0,   6,  -1],\n",
              "        [  0,  -2, -12,  ...,  12,  -7, -13],\n",
              "        [-13,  -1, -10,  ...,   8,   2,  -2]], dtype=torch.int8)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "#@ INITIALIZING QUANTIZATION:\n",
        "dtype = torch.qint8                                                         # Initialization.\n",
        "quantized_weights = quantize_per_tensor(weights, scale, zero_point, dtype)  # Quantization of weights.\n",
        "quantized_weights.int_repr()                                                # Inspection."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NeiHQMvs08Ac",
        "outputId": "628a155b-9b06-49ab-deaf-e2799fe05aab"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "100 loops, best of 5: 10.5 ms per loop\n"
          ]
        }
      ],
      "source": [
        "#@ INSPECTING TIME:\n",
        "%%timeit\n",
        "weights @ weights"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TedqXYKQ1HHT",
        "outputId": "60e6eb07-9fc8-4f5d-b39b-5b892f5624de"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The slowest run took 11.22 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
            "1000 loops, best of 5: 375 µs per loop\n"
          ]
        }
      ],
      "source": [
        "#@ INSPECTING TIME:\n",
        "%%timeit\n",
        "q_fn = QFunctional()\n",
        "q_fn.mul(quantized_weights, quantized_weights)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SN95KODO7Hyt"
      },
      "source": [
        "**Benchmarking Quantized Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "9tE3iSVM3apY"
      },
      "outputs": [],
      "source": [
        "#@ IMPLEMENTATION OF DYNAMIC QUANTIZATION:\n",
        "from torch.quantization import quantize_dynamic\n",
        "model_ckpt = \"transformersbook/distilbert-base-uncased-distilled-clinc\"     # Initializing model checkpoint.\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)                       # Initializing pretrained tokenizer.\n",
        "model = (AutoModelForSequenceClassification.from_pretrained(\n",
        "    model_ckpt).to(\"cpu\"))                                                  # Initializing pretrained model.\n",
        "model_quantized = quantize_dynamic(model, {nn.Linear}, dtype=torch.qint8)   # Initializing quantized model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cWBCOCZ57MSg",
        "outputId": "6be671bc-ca98-41a4-b38a-93fdc37550cd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model size (MB) - 132.40\n",
            "Average latency (ms) - 21.79 +\\- 1.57\n",
            "Accuracy on test set - 0.876\n"
          ]
        }
      ],
      "source": [
        "#@ BENCHMARKING THE DISTILLED MODEL:\n",
        "pipe = pipeline(\"text-classification\", model=model_quantized, \n",
        "                tokenizer=tokenizer)                                            # Initializing distilled model pipeline.\n",
        "optim_type = \"Distillation + quantization\"                                      # Initialization.\n",
        "pb = PerformanceBenchmark(pipe, clinc[\"test\"], optim_type=optim_type)           # Inspecting performance benchmark.\n",
        "perf_metrics.update(pb.run_benchmark())                                         # Updating benchmark."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "Lu-6DtsC7uTE",
        "outputId": "0eec71cd-c031-4699-a5ca-0650a2be96e4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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8a4DBZpZF0NQzAlgAXGJmx7n7v4AzgOXVbEMa08bPoHgLLHsZClYG0/pdCpmHwOF9oMWhjRufiNRJlG38881sKrAIKAU+BCYCa4EXzawc2AxcGVUMcgDWzIf3/huy2sPga6F4K5SXQPPW8M4DQU354eOCXwAiklBUj1/25Q6vj4OSIhh2M7Q9Ys95X34I8x+F7sNgwE8aL04RqZbq8Uv8dhTAsJvAUiDrkD3nmUHXAdD6ruCsf+cWyGzbOHGKSJ2oZIPs6+0/wJz79036sTJaw4y7IO+lhotLROqFEr/sq2Rnzf3CpqQHy5XsaJiYRKTeKPHLvtIzoayk+mXKS4Ll0ls0TEwiUm/Uxi/7OvXmoH2/aFPVzT3FhTDidkjRR0gk0eiMX/bVogPMvg+mj4ctX+w5zx3WLoJ/3g4r/08XdkUSkBK/7MsMel8YnNW/8wB88ymsXQhr3oMNy4NbOVObwdHDGztSEakD/U6X/TvypODMv3gLLHp6zyd3c0fB4TnQon3jxigidaLEL1Vrf3Twt01XKFwX1uo5KriVU0QSlhK/1Kxlx+AlIgcFtfGLiCQZnfGLiDRhpWXlbC4qobS8nLSUFNplpZOWemDn7Er8IiJNzK7SMj5eV8jcTzfyxaYiynGMoN/aFIwjDsliaI/29OrcmuZpqbXevhK/iEgT4e6s+KqQKR98wfbiMlpmpNGhVXNSU6xymbJy55vtu5j83hpaZqTy4xOOIPvw1phZNVvek9r4RUSagPJy57Ul65g4+3NSU1Lo0i6TNpnpeyR9gNQUo01mOl3aZZKaksLE2Z/z2pJ1lJfHX2JfZ/wiIo3M3Zn20TpmrthAl3ZZ+yT7qrRsnkZmeipvrdiAmXFu305xnfnrjF9EpJGt+KqQmSsLapX0K6SmGF3bZfHWig2s+KowrnWU+EVEGtGu0jKmfPAFh7RoVuukXyE1xTikRTNe+GAtu0rLalxeiV9EpBF9vK4wuJDb/MBa3ls2T6OwuISP19V81q/ELyLSiOZ+upGWGfVzubVVRjpzP91Y43JK/CIijaS0rJwvNhUd8Nl+hZbN0/hiUxGlZeXVLqfELyLSSDYXlVCO17ltf2+pKUY5zuai6nvQU+IXEWkkpeXl1E/K/5aF262OEr+ISCNJS0kh/seu4uPhdqujxC8i0kjaZaWTglFWi6duq1NW7qRgtMtKr3Y5JX4RkUaSlprCEYdksX1Xab1sb/uuUo44JKvG6p1K/CIijWhoj/ZsL66fxL+tuIShPWruElWJX0SkEfXq3JqWGakHfNa/fVcprTPS6dW55q5R40r8ZtbOzHqb2dFmpi8LEZF60jwtKK28acfuOrf1l5U7m3bs5kcndI2rPn+VSdzM2pjZr81sKfAe8DjwArDazP5qZqfVKUIREdlD9uGtOe34DqzdXFTr5F9W7qzdXMR3szuSfXjNZ/tQfVnmqcAzwCnuviV2hpkNBH5iZke7+5O1ilJERPYQlFTujJnx1ooNHNKiWVxP827fVcqmHbsZ0fMwzsmJryQzVJP43f2MauYtBBbGtQcREalRSkpQT79HxxZM+eAL1m4uolVGOi2bp+3TA9f2XaVsKy6hdUY6Vw/rXuseuOIuEGFmHYDrgUzgMXf/pBbHJCIiNTAzenZqw61nt6yxz93zczs3SJ+7fwSeCPf9HHBCrfcmIiI1ap6WSv8j29H/yHaUlpWzuaiE0vJy0lJSaJeVXuN9+jWp7uLuG2Y2LGZSMyA/fDWPZ+NmdoOZLTOzPDN73swyLHCPmf3LzJab2dgDOQARkYNZWmoKHVo1p1ObTDq0an7ASR+qP+P/EfAbM/sF8Bvgt8DvCZp6rq1pw2bWBRgL9HL3nWb2AnApQQ2hI4Bsdy83s44HeAwiIlIL1V3c3QrcZGZHA/cA64Dr9r7DJ47tZ5pZCZAVbuNu4DJ3Lw/3s6GuwYuISO1V19RzjJlNAH4G/DvwCjDFzMaaWY1XE9z9S2ACsAZYD2x19zeBY4Afm9kCM/s/Mzu2iv1fHS6zoKCgoPZHJiIi+1VdY9HzwEvATOBZd5/j7t8DtgBv1rRhM2sHnA90BzoDLczscoLrA8XuPojgYvFT+1vf3Se6+yB3H9ShQ4faHJOIiFSjusTfHPic4GJuVsVEd38GODeObZ8OfO7uBe5eQvAlcjKwNhwGeBnoW/uwRUSkrqq7uHst8DCwG7gmdoa774xj22uAwWaWBewERgALgELgNIIvlVOBf9U+bBERqavqLu7OBebWdcPuPt/MpgKLgFLgQ2AiwV1Bk83sBmA7wTUEERFpIFUmfjN7jaAw2xthU03svKOBMUC+u++3jR7A3ccD4/eavAs4p64Bi4jIgamuqecq4Ebgv8xsE1AAZADdgFXAw+7+auQRiohIvaquqecr4GbgZjPrBnQiaKv/l7sXNUh0IiJS7+Kq1ePu+QR394iISIJTb1oiIklGiV9EJMnUmPjN7AfqZ1dE5OART0L/MfCJmf3BzLKjDkhERKJVY+J398uB/gS3cE4ys3lhAbVWkUcnIiL1Lq4mHHcvJOh8/X8Jbuv8IbDIzH4ZYWwiIhKBeNr4zzOzl4FZQDpwort/H+hHUK5ZREQSSDz38V8EPODus2MnunuRmf00mrBERCQq8ST+Owg6UgHAzDKBw9w9391nRBWYiIhEI542/r8C5THjZeE0ERFJQPEk/jR3310xEg43iy4kERGJUjyJv8DMzqsYMbPzgW+iC0lERKIUTxv/NQQdpzwMGPAFMDrSqEREJDI1Jn53X0XQhWLLcHx75FGJiEhk4irLbGbnAL2BDDMDwN3vijAuERGJSDwPcD1GUK/nlwRNPZcAR0Ucl4iIRCSei7snu/toYLO73wkMAY6LNiwREYlKPIm/OPxbZGadgRKCej0iIpKA4mnjf83M2gL3AYsAB56INCoREYlMtYk/7IBlhrtvAV40s2lAhrtvbZDoRESk3lXb1OPu5cAjMeO7lPRFRBJbPG38M8zsIqu4j1NERBJaPIn/5wRF2XaZWaGZbTOzwojjEhGRiMTz5K66WBQROYjUmPjNbNj+pu/dMYuIiCSGeG7nvClmOAM4EVgIfDeSiEREJFLxNPX8IHbczI4AHowsIhERiVQ8F3f3thboWd+BiIhIw4injf9PBE/rQvBFkUvwBK+IiCSgeNr4F8QMlwLPu/vciOIREZGIxZP4pwLF7l4GYGapZpbl7kXRhiYiIlGI68ldIDNmPBOYHk04IiIStXgSf0Zsd4vhcFY8GzezG8xsmZnlmdnzZpYRM+8hM1M3jiIiDSyexL/DzAZUjJjZQGBnTSuZWRdgLDDI3fsAqcCl4bxBQLs6RSwiIgcknjb+XwF/NbN1BF0vHk7QFWO82880sxKCXwnrzCyVoLb/ZcAPax+yiIgciHge4PrAzLKB48NJK929JI71vjSzCcAagl8Ib7r7m2Z2PfA3d19fXcFPM7sauBrgyCOPrPlIREQkLvF0tv5vQAt3z3P3PKClmV0bx3rtgPOB7kBnoIWZjSborP1PNa3v7hPdfZC7D+rQoUNNi4uISJziaeO/KuyBCwB33wxcFcd6pwOfu3tB+AvhJeBOoAfwqZnlA1lm9mntwxYRkbqKJ/GnxnbCErbRN4tjvTXAYDPLCtcfAdzv7oe7ezd37wYUuXuPugQuIiJ1E8/F3deBKWb2eDj+83Batdx9vplNJSjvUAp8CEysa6AiIlI/4kn8txBcZP1FOP5P4Il4Nu7u44Hx1cxvGc92RESk/tTY1OPu5e7+mLtf7O4XAx8Tx8VZERFpmuI548fM+gMjgR8BnxNcqBURkQRUZeI3s+MIkv1I4BtgCmDufloDxSYiIhGo7ox/BTAHONfdP4Wg9k6DRCUiIpGpro3/QmA9MNPMnjCzEQQlG0REJIFVmfjd/RV3vxTIBmYS1OzpaGaPmtmZDRWgiIjUr3ju6tnh7s+Fna53Jbgf/5bIIxMRkUjUqrN1d98c1tAZEVVAIiISrVolfhERSXxK/CIiSUaJX0QkySjxi4gkGSV+EZEko8QvIpJklPhFRJKMEr+ISJJR4hcRSTJK/CIiSUaJX0QkySjxi4gkGSV+EZEko8QvIpJklPhFRJKMEr+ISJJR4hcRSTJK/CIiSUaJX0QkySjxi4gkGSV+EZEko8QvIpJklPhFRJKMEr+ISJJR4hcRSTJK/CIiSSbSxG9mN5jZMjPLM7PnzSzDzCab2cpw2lNmlh5lDCIisqfIEr+ZdQHGAoPcvQ+QClwKTAaygRwgE/hZVDGIiMi+0hpg+5lmVgJkAevc/c2KmWb2PtA14hhERCRGZGf87v4lMAFYA6wHtu6V9NOBnwCv7299M7vazBaY2YKCgoKowhQRSTpRNvW0A84HugOdgRZmdnnMIv8NzHb3Oftb390nuvsgdx/UoUOHqMIUEUk6UV7cPR343N0L3L0EeAk4GcDMxgMdgBsj3L+IiOxHlG38a4DBZpYF7ARGAAvM7GfA94AR7l4e4f5FRGQ/Ikv87j7fzKYCi4BS4ENgIrADWA3MMzOAl9z9rqjiEBGRPUV6V4+7jwfGN+Q+RUSkenpyV0QkySjxi4gkGSV+EZEko8QvIpJklPhFRJKMEr+ISJJR4hcRSTJK/CIiSUaJX0QkySjxi4gkGSV+EZEko8QvIpJklPhFRJKMEr+ISJJR4hcRSTJK/CIiSUaJX0QkySjxi4gkGSV+EZEko8QvIpJklPhFRJKMEr+ISJJR4hcRSTJK/CIiSUaJX0QkySjxi4gkGSV+EZEko8QvIpJklPhFRJKMEr+ISJJR4hcRSTJK/CIiSUaJX0QkySjxi4gkGSV+EZEkE2niN7MbzGyZmeWZ2fNmlmFm3c1svpl9amZTzKxZlDGIiMieIkv8ZtYFGAsMcvc+QCpwKfCfwAPu3gPYDPw0qhhERGRfUTf1pAGZZpYGZAHrge8CU8P5TwMXRByDiIjESItqw+7+pZlNANYAO4E3gYXAFncvDRdbC3TZ3/pmdjVwdTi63cxWRhVrPToU+Kaxg6iDRI0bEjf2RI0bEjf2RI0b6h77UfubGFniN7N2wPlAd2AL8FfgrHjXd/eJwMRooouGmS1w90GNHUdtJWrckLixJ2rckLixJ2rcUP+xR9nUczrwubsXuHsJ8BIwFGgbNv0AdAW+jDAGERHZS5SJfw0w2MyyzMyAEcDHwEzg4nCZK4BXI4xBRET2Elnid/f5BBdxFwFLw31NBG4BbjSzT4H2wJNRxdAIEqppKkaixg2JG3uixg2JG3uixg31HLu5e31uT0REmjg9uSsikmSU+EVEkowSfx2Y2VNmtsHM8mKmHWJm/zSzT8K/7RozxqqY2RFmNtPMPg7LaVwfTm/S8YflPt43syVh3HeG0xOiBIiZpZrZh2Y2LRxPlLjzzWypmS02swXhtCb9WalgZm3NbKqZrTCz5WY2pKnHbmbHh+91xavQzH5V33Er8dfNJPZ9JmEcMMPdjwVmhONNUSnw7+7eCxgM/JuZ9aLpx78L+K679wNygbPMbDCJUwLkemB5zHiixA1wmrvnxtxH3tQ/KxX+C3jd3bOBfgTvf5OO3d1Xhu91LjAQKAJepr7jdne96vACugF5MeMrgU7hcCdgZWPHGOdxvAqckUjxE5T/WAScRPA0Y1o4fQjwRmPHt594u4b/Wb8LTAMsEeIOY8sHDt1rWpP/rABtgM8Jb2BJpNhjYj0TmBtF3Drjrz+Hufv6cPgr4LDGDCYeZtYN6A/MJwHiD5tLFgMbgH8Cq4izBEgjexC4GSgPx9uTGHEDOPCmmS0My6hAAnxWCCoGFAB/DpvY/sfMWpAYsVe4FHg+HK7XuJX4I+DB13KTvk/WzFoCLwK/cvfC2HlNNX53L/PgJ3BX4EQgu5FDqpGZnQtscPeFjR1LHX3H3QcA3ydoFhwWO7OpflYIytEMAB519/7ADvZqHmnCsRNe8zmPoNTNHuojbiX++vO1mXUCCP9uaOR4qmRm6QRJf7K7vxROTpj43X0LwRPgQ2j6JUCGAueZWT7wvwTNPf9F048bCIothn83ELQ1n0hifFbWAms9eJAUgodJB5AYsUPwRbvI3b8Ox+s1biX++vM3ghIU0IRLUTyibxkAAAWqSURBVITlM54Elrv7/TGzmnT8ZtbBzNqGw5kE1yWW08RLgLj7re7e1d27Efx0f8vdR9HE4wYwsxZm1qpimKDNOY8m/lkBcPevgC/M7PhwUkXJmCYfe2gk3zbzQH3H3dgXMBLxFf6DrAdKCM4sfkrQbjsD+ASYDhzS2HFWEft3CH4mfgQsDl9nN/X4gb7Ah2HcecDt4fSjgfeBTwl+Fjdv7FirOYbhwLREiTuMcUn4WgbcFk5v0p+VmPhzgQXhZ+YVoF0ixA60ADYCbWKm1WvcKtkgIpJk1NQjIpJklPhFRJKMEr+ISJJR4hcRSTJK/CIiSUaJXxqcmV1gZm5mifDkbb6ZHVrDMr9uqHj2s+9MM3vbzFIPcDs5ZjapnsKSJk6JXxrDSOCd8O8BO9CkVw8aLfEDVwIvuXvZgWzE3ZcCXc3syPoJS5oyJX5pUGGNoO8QPPR2aTjtLDP7a8wyw2Pq1p9pZvPMbJGZ/TVcv+JM/D/NbBFwiZldZWYfhPX6XzSzrHC5Y8zsvbCm/N1mtj1mPzeF63xUUd+/hthfCYuVLasoWGZm9wKZYe30yeG0yy3oO2CxmT1e8cVkZtvN7J4wxvfM7LBw+mFm9nI4fYmZnWxmd5nZr2L2fY+FfSfsZRThU5zh+/a2mb1qZp+Z2b1mNiqMZamZHRMud4mZ5YX7mh2zrdcq/k3kINfYT6nplVwvgkT1ZDj8LkHN8TRgDdAinP4ocDlwKDA7ZvotfPvEbj5wc8x228cM3w38MhyeBowMh68BtofDZxJ0YG0EJ0DTgGH7iTefsCwx4dOSQCbB08Ptw/HtMcv3JEig6eH4fwOjw2EHfhAO/wH4TTg8haBYHkAqQUnhbgS1WgjjWxV7jOH0ZsBXMePDgS0EZXubE9T/uTOcdz3wYDi8FOgSDreNWX8o8Fpjf0b0iv6lM35paCMJipUR/h3pQXni14EfhIXLziE4ix0M9ALmhuWYrwCOitnWlJjhPmY2x8yWEny59A6nD+HbCofPxSx/Zvj6kKC2fzZwbA2xjzWzJcB7wBFVLD+C4MvsgzDmEQSlDwB2E3zBACwkSO4QFG57FCorkG5193xgo5n1r4jT3Tfuta9DCRJ9rA/cfb277yL4sngznL40Zn9zgUlmdhXBF02FDUDn6t4AOTik1byISP0ws0MIklyOmTlB0nEzu4ngS+A6YBOwwN23hQXl/unuVV0L2BEzPAm4wN2XmNkYgrPfasMBfu/uj8cZ+3DgdGCIuxeZ2Swgo4rtPu3ut+5nXom7V9RIKaPm/3//A4wBDgee2s/8nfuJYVfMcHnMeHnF/tz9GjM7ieALdqGZDQy/VDLCbcpBTmf80pAuBp5196PcvZu7H0HQS9IpwNsEZXOv4ttfBO8BQ82sB1RWizyuim23AtaHJadHxUx/D7goHI5tv34DuDLmmkEXM+tYTextgM1h0s8m+DVSoSTcLwSFtC6u2JYFfaUeRfVmAL8Il081szbh9JcJuvg8IYx3D+6+GUg1s/19AVXJzI5x9/nufjtBZyVHhLOOI2jCkoOcEr80pJEEySzWiwTNPWUEzSDfD//i7gUEZ7zPm9lHwDyq7nzltwQ9ic0FVsRM/xVwY7h+D2BruO03CZp+5oXNQ1MJvjyq8jqQZmbLgXsJvlAqTAQ+MrPJ7v4x8BuCXqs+IugprFM124Wg/f20MI6FBM1buPtugvLNL3jVd+28SXCxvDbuCy/25hFcZ1kSTj8N+HsttyUJSNU55aAW3t2z093dzC4l+JI5v7HjioeZpRBcf7jE3T+pYpkBwA3u/pMD3Fdzgl9d3/Fvu4SUg5Ta+OVgNxB4OLxesIXgvvcmz8x6EfzyebmqpA/g7ovMbKaZpVbzqyAeRwLjlPSTg874RUSSjNr4RUSSjBK/iEiSUeIXEUkySvwiIklGiV9EJMn8P8KszmKRIYXfAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@ FUNCTION FOR PLOTTING ACCURACY ACROSS LATENCY:\n",
        "def plot_metrics(perf_metrics, current_optim_type):          \n",
        "    df = pd.DataFrame.from_dict(perf_metrics, orient=\"index\") \n",
        "    for idx in df.index:\n",
        "        df_opt = df.loc[idx]\n",
        "        if idx == current_optim_type:\n",
        "            plt.scatter(df_opt[\"time_avg_ms\"], df_opt[\"accuracy\"] * 100,\n",
        "                        alpha=0.5, s=df_opt[\"size_mb\"], label=idx, \n",
        "                        marker='$\\u25CC$')\n",
        "        else:\n",
        "            plt.scatter(df_opt[\"time_avg_ms\"], df_opt[\"accuracy\"] * 100, \n",
        "                        s=df_opt[\"size_mb\"], label=idx, alpha=0.5)\n",
        "    legend = plt.legend(bbox_to_anchor=(1, 1))\n",
        "    for handle in legend.legendHandles:\n",
        "        handle.set_sizes([20])\n",
        "    plt.ylim(80, 90)\n",
        "    xlim = int(perf_metrics[\"BERT baseline\"][\"time_avg_ms\"] + 3)\n",
        "    plt.xlim(1, xlim)\n",
        "    plt.ylabel(\"Accuracy (%)\")\n",
        "    plt.xlabel(\"Average latency (ms)\")\n",
        "    plt.show()\n",
        "    \n",
        "#@ VISUALIZING THE PERFORMANCE:\n",
        "plot_metrics(perf_metrics, optim_type)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zqBN7uJCPjqu"
      },
      "source": [
        "### **Optimization with ONNX & ONNX Runtime**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "Nx04WPlj92Pl"
      },
      "outputs": [],
      "source": [
        "#@ INITIALIZING OPENMP ENVIRONMENT:\n",
        "os.environ[\"OMP_NUM_THREADS\"] = f\"{cpu_count()}\"        # Initialization. \n",
        "os.environ[\"OMP_WAIT_POLICY\"] = \"ACTIVE\"                # Initialization."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ww3UhXW6RVQR",
        "outputId": "15a12896-ec4a-4bee-fbf2-9bc82f7a6ca5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ONNX opset version set to: 12\n",
            "Loading pipeline (model: transformersbook/distilbert-base-uncased-distilled-clinc, tokenizer: PreTrainedTokenizerFast(name_or_path='transformersbook/distilbert-base-uncased-distilled-clinc', vocab_size=30522, model_max_len=512, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}))\n",
            "Creating folder onnx\n",
            "Using framework PyTorch: 1.11.0+cu113\n",
            "Found input input_ids with shape: {0: 'batch', 1: 'sequence'}\n",
            "Found input attention_mask with shape: {0: 'batch', 1: 'sequence'}\n",
            "Found output output_0 with shape: {0: 'batch'}\n",
            "Ensuring inputs are in correct order\n",
            "head_mask is not present in the generated input list.\n",
            "Generated inputs order: ['input_ids', 'attention_mask']\n"
          ]
        }
      ],
      "source": [
        "#@ CONVERTING DISTILLED MODEL INTO ONNX FORMAT:\n",
        "model_ckpt = \"transformersbook/distilbert-base-uncased-distilled-clinc\"     # Initializing model checkpoint.\n",
        "onnx_model_path = Path(\"onnx/model.onnx\")                                   # Initializing ONNX model path.\n",
        "convert(framework=\"pt\", model=model_ckpt, tokenizer=tokenizer, \n",
        "        output=onnx_model_path, opset=12, \n",
        "        pipeline_name=\"text-classification\")                                # Converting into ONNX format."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "IVInwor3WD_m"
      },
      "outputs": [],
      "source": [
        "#@ CREATING INFERENCE SESSION:                                                  # Defining function.\n",
        "def create_model_for_provider(model_path, provider=\"CPUExecutionProvider\"):\n",
        "    options = SessionOptions()                                                  # Initialization.\n",
        "    options.intra_op_num_threads = 1                                            # Initialization.\n",
        "    options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL\n",
        "    session = InferenceSession(str(model_path), options, providers=[provider])\n",
        "    session.disable_fallback()\n",
        "    return session\n",
        "\n",
        "#@ IMPLEMENTATION OF INFERENCE SESSION FUNCTION:\n",
        "onnx_model = create_model_for_provider(onnx_model_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EoCq0eFVY7pq",
        "outputId": "a469dabe-154c-467a-e342-01966f1ef5f5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 151)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "#@ INSPECTING INPUTS: SAMPLE:\n",
        "inputs = clinc_enc[\"test\"][:1]                              # Initialization.\n",
        "del inputs[\"labels\"]                                        # Dropping label column.\n",
        "logits_onnx = onnx_model.run(None, inputs)[0]               # Initializing logits. \n",
        "logits_onnx.shape                                           # Inspecting shape."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CrUfTMEvZjwq",
        "outputId": "f761588a-4df5-4eeb-a1c5-7c6d20d8ab5a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "61\n",
            "61\n"
          ]
        }
      ],
      "source": [
        "#@ INSPECTING PREDICTIONS:\n",
        "print(np.argmax(logits_onnx))\n",
        "print(clinc_enc[\"test\"][0][\"labels\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "18GrQqsEZ0KF",
        "outputId": "011bb49f-ca61-45a9-8589-80b34bb4da55"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'car_rental', 'score': 0.7848334}]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "#@ INITIALIZING ONNX PIPELINE:\n",
        "class OnnxPipeline:                                             # Defining class.\n",
        "    def __init__(self, model, tokenizer):                       # Initializing constructor function.\n",
        "        self.model = model                                      # Initializing model.\n",
        "        self.tokenizer = tokenizer                              # Initializing tokenizer.\n",
        "    \n",
        "    def __call__(self, query):\n",
        "        model_inputs =self.tokenizer(query,return_tensors=\"pt\") # Initializing tokenization.\n",
        "        inputs_onnx = {k:v.cpu().detach().numpy() for k,v in \n",
        "                       model_inputs.items()}                    # Initializing inputs.\n",
        "        logits = self.model.run(None, inputs_onnx)[0][0, :]     # Initializing logits.\n",
        "        probs = softmax(logits)                                 # Implementation of softmax laer.\n",
        "        pred_idx = np.argmax(probs).item()                      # Initialization.\n",
        "        return [{\"label\": intents.int2str(pred_idx), \n",
        "                 \"score\": probs[pred_idx]}]\n",
        "\n",
        "#@ IMPLEMENTATION OF ONNX PIPELINE:\n",
        "pipe = OnnxPipeline(onnx_model, tokenizer)                      # Initializing ONNX pipeline.\n",
        "pipe(query)                                                     # Inspection."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "q5Ukd0BWTuIK"
      },
      "outputs": [],
      "source": [
        "#@ INITIALIZING ONNX PERFORMANCE BENCHMARK:\n",
        "class OnnxPerformanceBenchmark(PerformanceBenchmark):                       # Defining performance benchmark class.\n",
        "    def __init__(self, *args, model_path, **kwargs):                        # Initializing constructor function.\n",
        "        super().__init__(*args, **kwargs)\n",
        "        self.model_path = model_path                                        # Initializing model path.\n",
        "    \n",
        "    def compute_size(self):                                                 # Defining compute size function.\n",
        "        size_mb = Path(self.model_path).stat().st_size / (1024 * 1024)      # Initializing model size.\n",
        "        print(f\"Model size (MB) - {size_mb:.2f}\")                           # Inspection.\n",
        "        return {\"size_mb\": size_mb}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DiH8Dvi3y_zj",
        "outputId": "9cce9f01-3055-4e2d-d9c4-60c55784e2fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model size (MB) - 255.90\n",
            "Average latency (ms) - 28.01 +\\- 1.37\n",
            "Accuracy on test set - 0.868\n"
          ]
        }
      ],
      "source": [
        "#@ INSPECTING PERFORMANCE OF DISTILLED MODEL:\n",
        "optim_type = \"Distillation + ORT\"                                           # Initialization.\n",
        "pb = OnnxPerformanceBenchmark(pipe, clinc[\"test\"], optim_type, \n",
        "                              model_path=\"onnx/model.onnx\")                 # Implementation of performance benchmark.\n",
        "perf_metrics.update(pb.run_benchmark())                                     # Updating perf metrics."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "8Sq7zqGdy_zj",
        "outputId": "0cd96796-558f-4f19-9b62-34dc2c9ced6d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#@ FUNCTION FOR PLOTTING ACCURACY ACROSS LATENCY:\n",
        "def plot_metrics(perf_metrics, current_optim_type):          \n",
        "    df = pd.DataFrame.from_dict(perf_metrics, orient=\"index\") \n",
        "    for idx in df.index:\n",
        "        df_opt = df.loc[idx]\n",
        "        if idx == current_optim_type:\n",
        "            plt.scatter(df_opt[\"time_avg_ms\"], df_opt[\"accuracy\"] * 100,\n",
        "                        alpha=0.5, s=df_opt[\"size_mb\"], label=idx, \n",
        "                        marker='$\\u25CC$')\n",
        "        else:\n",
        "            plt.scatter(df_opt[\"time_avg_ms\"], df_opt[\"accuracy\"] * 100, \n",
        "                        s=df_opt[\"size_mb\"], label=idx, alpha=0.5)\n",
        "    legend = plt.legend(bbox_to_anchor=(1, 1))\n",
        "    for handle in legend.legendHandles:\n",
        "        handle.set_sizes([20])\n",
        "    plt.ylim(80, 90)\n",
        "    xlim = int(perf_metrics[\"BERT baseline\"][\"time_avg_ms\"] + 3)\n",
        "    plt.xlim(1, xlim)\n",
        "    plt.ylabel(\"Accuracy (%)\")\n",
        "    plt.xlabel(\"Average latency (ms)\")\n",
        "    plt.show()\n",
        "    \n",
        "#@ VISUALIZING THE PERFORMANCE:\n",
        "plot_metrics(perf_metrics, optim_type)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vSNIv6LMy_zj",
        "outputId": "434a6bcb-d94d-4b29-ce6c-507d18029025"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ignore MatMul due to non constant B: /[MatMul_47]\n",
            "Ignore MatMul due to non constant B: /[MatMul_60]\n",
            "Ignore MatMul due to non constant B: /[MatMul_129]\n",
            "Ignore MatMul due to non constant B: /[MatMul_142]\n",
            "Ignore MatMul due to non constant B: /[MatMul_211]\n",
            "Ignore MatMul due to non constant B: /[MatMul_224]\n",
            "Ignore MatMul due to non constant B: /[MatMul_293]\n",
            "Ignore MatMul due to non constant B: /[MatMul_306]\n",
            "Ignore MatMul due to non constant B: /[MatMul_375]\n",
            "Ignore MatMul due to non constant B: /[MatMul_388]\n",
            "Ignore MatMul due to non constant B: /[MatMul_457]\n",
            "Ignore MatMul due to non constant B: /[MatMul_470]\n"
          ]
        }
      ],
      "source": [
        "#@ INITIALIZING MODEL QUANTIZATION:\n",
        "from onnxruntime.quantization import quantize_dynamic, QuantType\n",
        "model_input = \"onnx/model.onnx\"                                         # Initializing model input.\n",
        "model_output = \"onnx/model.quant.onnx\"                                  # Initializing model output.\n",
        "quantize_dynamic(model_input, model_output, weight_type=QuantType.QInt8)  # Initializing model quantization."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@ INSPECTING PERFORMANCE BENCHMARK:\n",
        "onnx_quantized_model = create_model_for_provider(model_output)          # Initializing model.\n",
        "pipe = OnnxPipeline(onnx_quantized_model, tokenizer)                    # Initializing model pipeline.\n",
        "optim_type = \"Distillation + ORT (quantized)\"                           # Initialization.\n",
        "pb = OnnxPerformanceBenchmark(pipe, clinc[\"test\"], optim_type, \n",
        "                              model_path=model_output)                  # Implementation of performance benchmark.\n",
        "perf_metrics.update(pb.run_benchmark())                                 # Updating perf metrics."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RsmtiyC10plR",
        "outputId": "588773ba-71fc-4508-b500-fe28eeebf618"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model size (MB) - 64.22\n",
            "Average latency (ms) - 16.71 +\\- 2.21\n",
            "Accuracy on test set - 0.878\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@ VISUALIZING THE PERFORMANCE:\n",
        "plot_metrics(perf_metrics, optim_type)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "pbH0-ThF1jbm",
        "outputId": "5c9e72e3-9fbf-4f33-8ffc-aa970fe6ba1c"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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