{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "be6721bc", "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "execution": { "iopub.execute_input": "2024-05-31T20:46:17.146884Z", "iopub.status.busy": "2024-05-31T20:46:17.146092Z", "iopub.status.idle": "2024-05-31T20:46:51.422939Z", "shell.execute_reply": "2024-05-31T20:46:51.422021Z" }, "papermill": { "duration": 34.284276, "end_time": "2024-05-31T20:46:51.425358", "exception": false, "start_time": "2024-05-31T20:46:17.141082", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting yfinance\r\n", " Downloading yfinance-0.2.40-py2.py3-none-any.whl.metadata (11 kB)\r\n", "Requirement already satisfied: pandas>=1.3.0 in /opt/conda/lib/python3.10/site-packages (from yfinance) (2.2.1)\r\n", "Requirement already satisfied: numpy>=1.16.5 in /opt/conda/lib/python3.10/site-packages (from yfinance) (1.26.4)\r\n", "Requirement already satisfied: requests>=2.31 in /opt/conda/lib/python3.10/site-packages (from yfinance) (2.31.0)\r\n", "Collecting multitasking>=0.0.7 (from yfinance)\r\n", " Downloading multitasking-0.0.11-py3-none-any.whl.metadata (5.5 kB)\r\n", "Requirement already satisfied: lxml>=4.9.1 in /opt/conda/lib/python3.10/site-packages (from yfinance) (5.2.2)\r\n", "Requirement already satisfied: platformdirs>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from yfinance) (4.2.2)\r\n", "Requirement already satisfied: pytz>=2022.5 in /opt/conda/lib/python3.10/site-packages (from yfinance) (2023.3.post1)\r\n", "Requirement already satisfied: frozendict>=2.3.4 in /opt/conda/lib/python3.10/site-packages (from yfinance) (2.4.4)\r\n", "Collecting peewee>=3.16.2 (from yfinance)\r\n", " Downloading peewee-3.17.5.tar.gz (3.0 MB)\r\n", "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m3.0/3.0 MB\u001B[0m \u001B[31m72.7 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\r\n", "\u001B[?25h Installing build dependencies ... \u001B[?25l-\b \b\\\b \b|\b \b/\b \b-\b \bdone\r\n", "\u001B[?25h Getting requirements to build wheel ... \u001B[?25l-\b \bdone\r\n", "\u001B[?25h Preparing metadata (pyproject.toml) ... \u001B[?25l-\b \bdone\r\n", "\u001B[?25hRequirement already satisfied: beautifulsoup4>=4.11.1 in /opt/conda/lib/python3.10/site-packages (from yfinance) (4.12.2)\r\n", "Requirement already satisfied: html5lib>=1.1 in /opt/conda/lib/python3.10/site-packages (from yfinance) (1.1)\r\n", "Requirement already satisfied: soupsieve>1.2 in /opt/conda/lib/python3.10/site-packages (from beautifulsoup4>=4.11.1->yfinance) (2.5)\r\n", "Requirement already satisfied: six>=1.9 in /opt/conda/lib/python3.10/site-packages (from html5lib>=1.1->yfinance) (1.16.0)\r\n", "Requirement already satisfied: webencodings in /opt/conda/lib/python3.10/site-packages (from html5lib>=1.1->yfinance) (0.5.1)\r\n", "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.3.0->yfinance) (2.9.0.post0)\r\n", "Requirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.3.0->yfinance) (2023.4)\r\n", "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests>=2.31->yfinance) (3.3.2)\r\n", "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests>=2.31->yfinance) (3.6)\r\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests>=2.31->yfinance) (1.26.18)\r\n", "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests>=2.31->yfinance) (2024.2.2)\r\n", "Downloading yfinance-0.2.40-py2.py3-none-any.whl (73 kB)\r\n", "\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m73.5/73.5 kB\u001B[0m \u001B[31m4.5 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optuna\n", "!pip install yfinance" ] }, { "cell_type": "code", "execution_count": 2, "id": "b52d3551", "metadata": { "execution": { "iopub.execute_input": "2024-05-31T20:46:51.437729Z", "iopub.status.busy": "2024-05-31T20:46:51.437418Z", "iopub.status.idle": "2024-05-31T20:47:04.269718Z", "shell.execute_reply": "2024-05-31T20:47:04.268783Z" }, "papermill": { "duration": 12.840939, "end_time": "2024-05-31T20:47:04.271983", "exception": false, "start_time": "2024-05-31T20:46:51.431044", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting optuna_integration\r\n", " Downloading optuna_integration-3.6.0-py3-none-any.whl.metadata (10 kB)\r\n", "Requirement already satisfied: optuna in /opt/conda/lib/python3.10/site-packages (from optuna_integration) (3.6.1)\r\n", "Requirement already satisfied: alembic>=1.5.0 in /opt/conda/lib/python3.10/site-packages (from optuna->optuna_integration) (1.13.1)\r\n", "Requirement already satisfied: colorlog in /opt/conda/lib/python3.10/site-packages (from optuna->optuna_integration) (6.8.2)\r\n", "Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from optuna->optuna_integration) (1.26.4)\r\n", "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from optuna->optuna_integration) (21.3)\r\n", "Requirement already satisfied: sqlalchemy>=1.3.0 in /opt/conda/lib/python3.10/site-packages (from optuna->optuna_integration) (2.0.25)\r\n", "Requirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from optuna->optuna_integration) (4.66.4)\r\n", "Requirement already satisfied: PyYAML in /opt/conda/lib/python3.10/site-packages (from optuna->optuna_integration) (6.0.1)\r\n", "Requirement already satisfied: Mako in /opt/conda/lib/python3.10/site-packages (from alembic>=1.5.0->optuna->optuna_integration) (1.3.5)\r\n", "Requirement already satisfied: typing-extensions>=4 in 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'/opt/conda/lib/python3.10/site-packages/aiohttp-3.9.1.dist-info/METADATA'\u001B[0m\u001B[33m\r\n", "\u001B[0mInstalling collected packages: optuna_integration\r\n", "Successfully installed optuna_integration-3.6.0\r\n" ] } ], "source": [ "!pip install optuna_integration" ] }, { "cell_type": "code", "execution_count": 3, "id": "3674102e", "metadata": { "execution": { "iopub.execute_input": "2024-05-31T20:47:04.286204Z", "iopub.status.busy": "2024-05-31T20:47:04.285916Z", "iopub.status.idle": "2024-05-31T20:47:18.233001Z", "shell.execute_reply": "2024-05-31T20:47:18.232174Z" }, "papermill": { "duration": 13.956798, "end_time": "2024-05-31T20:47:18.235087", "exception": false, "start_time": "2024-05-31T20:47:04.278289", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2024-05-31 20:47:07.091531: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "2024-05-31 20:47:07.091665: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "2024-05-31 20:47:07.224419: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Device mapping:\n", "/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n", "\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import os\n", "from datetime import datetime\n", "import yfinance as yf\n", "import tensorflow as tf\n", "from tensorflow.keras.models import Model\n", "from tensorflow.keras.layers import LSTM, Dense, Input\n", "from tensorflow.keras.optimizers import Adam\n", "from tensorflow.keras.callbacks import EarlyStopping\n", "import optuna\n", "from optuna_integration import TFKerasPruningCallback\n", "from sklearn.metrics import roc_auc_score\n", "from functools import partial\n", "sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(log_device_placement=True))\n", "from typing import Union, Optional, Tuple, Any\n", "tf.keras.utils.set_random_seed(\n", " 21052003\n", ")\n", "def get_stock(ticker, start, end):\n", "\n", " def convert_colnames(value):\n", " value = value.lower()\n", " value = value.replace(\" \", \"_\")\n", " return value\n", "\n", " def check_object(obj):\n", " if obj is None:\n", " raise ValueError('Return of yfinance download is None')\n", " elif isinstance(obj, pd.DataFrame):\n", " if obj.empty:\n", " raise ValueError('DataFrame is empty')\n", " else:\n", " raise ValueError('Return of yfinance download is niether a Dataframe nor None')\n", "\n", " try:\n", " df = yf.download(ticker, start=start, end=end, progress=False)\n", " df.columns = map(convert_colnames, df.columns)\n", " df.index.names = ['date']\n", " check_object(df)\n", " return df\n", " except Exception as e:\n", " print('An error occured while downloading data\\n' + str(e))\n", "\n", "def add_sentiment(stock_data: pd.DataFrame, file_name) -> pd.DataFrame:\n", " file_path = os.path.join('/kaggle', 'input', 'sentiments', file_name)\n", " sentiment_data = pd.read_csv(file_path, index_col='date', parse_dates=['date'])\n", " merged_df = pd.merge(stock_data, sentiment_data, left_index=True, right_index=True, how='left')\n", " return merged_df\n", "\n", "\n", "def calc_rsi(over: pd.Series, fn_roll: callable, scale_down: bool = True) -> pd.Series:\n", " # Get the difference in price from previous step\n", " delta = over.diff()\n", " # Get rid of the first row, which is NaN since it did not have a previous row to calculate the differences\n", " delta = delta[1:]\n", "\n", " # Make the positive gains (up) and negative gains (down) Series\n", " up, down = delta.clip(lower=0), delta.clip(upper=0).abs()\n", "\n", " roll_up, roll_down = fn_roll(up), fn_roll(down)\n", " rs = roll_up / roll_down\n", " rsi = 100.0 - (100.0 / (1.0 + rs))\n", "\n", " # Avoid division-by-zero if `roll_down` is zero\n", " # This prevents inf and/or nan values.\n", " rsi[:] = np.select([roll_down == 0, roll_up == 0, True], [100, 0, rsi])\n", " # rsi = rsi.case_when([((roll_down == 0), 100), ((roll_up == 0), 0)])\n", " # This alternative to np.select works only for pd.__version__ >= 2.2.0.\n", " rsi.name = 'rsi'\n", "\n", " # Assert range\n", " valid_rsi = rsi[13:]\n", " assert ((0 <= valid_rsi) & (valid_rsi <= 100)).all()\n", " # Note: rsi[:length - 1] is excluded from above assertion because it is NaN for SMA.\n", " rsi = rsi.reindex(over.index)\n", " if scale_down:\n", " rsi = rsi / 100\n", " return rsi\n", "\n", "\n", "def scale_stock_data(data: pd.DataFrame, scale_volume: float, scale_price: float) -> pd.DataFrame:\n", " data['volume'] = data['volume'] / scale_volume\n", " data['open'] = data['open'] / scale_price\n", " data['high'] = data['high'] / scale_price\n", " data['low'] = data['low'] / scale_price\n", " data['close'] = data['close'] / scale_price\n", " data['adj_close'] = data['adj_close'] / scale_price\n", " return data\n", "\n", "\n", "def calculate_indicators(prices_dataframe: pd.DataFrame, use_regular_close=False) -> pd.DataFrame:\n", " if use_regular_close:\n", " col = 'close'\n", " prices_dataframe = prices_dataframe.drop(columns=['adj_close'])\n", " else:\n", " col = 'adj_close'\n", " prices_dataframe['return'] = prices_dataframe[col].pct_change(1).iloc[1:]\n", " prices_dataframe['log1p_return'] = np.log1p(prices_dataframe['return'])\n", "\n", " prices_dataframe['rsi_ema'] = calc_rsi(prices_dataframe[col], lambda s: s.ewm(span=14).mean())\n", " # prices_dataframe['rsi_sma'] = calc_rsi(prices_dataframe[col], lambda s: s.rolling(14).mean())\n", "\n", " # prices_dataframe['smstd_20'] = prices_dataframe[col].rolling(window=20).std()\n", " # prices_dataframe['sma_20'] = prices_dataframe[col].rolling(window=20).mean()\n", " prices_dataframe['ewma_20'] = prices_dataframe[col].ewm(span=20).mean()\n", " prices_dataframe['ewma_60'] = prices_dataframe[col].ewm(span=60).mean()\n", " prices_dataframe['ewmstd_20'] = prices_dataframe[col].ewm(span=20).std()\n", " prices_dataframe['macd'] = prices_dataframe[col].ewm(span=12).mean() - prices_dataframe[col].ewm(span=26).mean()\n", " # prices_dataframe['upper_band_sma'] = prices_dataframe.ewma_20 + (2 * prices_dataframe.ewmstd_20)\n", " # prices_dataframe['lower_band_sma'] = prices_dataframe.sma_20 - (2 * prices_dataframe.ewmstd_20)\n", "\n", " # prices_dataframe['upper_band_ewma'] = prices_dataframe.ewma_20 + (2 * prices_dataframe.ewmstd_20)\n", " # prices_dataframe['lower_band_ewma'] = prices_dataframe.ewma_20 - (2 * prices_dataframe.ewmstd_20)\n", "\n", " return prices_dataframe\n", "\n", "\n", "def get_dataset(ticker: str,\n", " scale_price: float,\n", " scale_vol: float,\n", " sentiment: str,\n", " use_regular_close=False) -> Union[pd.DataFrame, None]:\n", " start = datetime(2007, 1, 1)\n", " end = datetime(2016, 8, 16)\n", " try:\n", " if sentiment == 'nyt_and_reu':\n", " sentiments = ['reuters.csv', 'nytimes.csv']\n", " else:\n", " sentiments = None\n", "\n", " data = get_stock(ticker, start, end)\n", " data = scale_stock_data(data, scale_vol, scale_price)\n", " data = calculate_indicators(data, use_regular_close)\n", " data = data.dropna()\n", " data['clabel'] = data['return'].apply(lambda x: 1 if x > 0 else 0)\n", " if sentiments is None:\n", " return data\n", " for sent in sentiments:\n", " data = add_sentiment(data, sent)\n", " return data\n", " except Exception as e:\n", " print(f'Failed to get {ticker} due to error: {e}')\n", " return None\n", "\n", " \n", "def get_sequences(data: pd.DataFrame,\n", " target_col: str,\n", " time_steps: int = 10) -> tuple[np.ndarray, np.ndarray]:\n", "\n", " feature_sequences = []\n", " targets = []\n", "\n", " for i in range(time_steps, len(data)):\n", " features_sequence = data.iloc[i - time_steps:i, :]\n", " target = data[target_col].iloc[i]\n", " feature_sequences.append(features_sequence)\n", " targets.append(target)\n", "\n", " # (batch_dim, sequence_size, features)\n", " feature_sequences = np.array(feature_sequences)\n", " targets = np.array(targets)\n", " targets = targets.reshape(targets.shape[0], 1)\n", "\n", " return feature_sequences, targets\n", "\n", "\n", "def get_train_val_test(feature_sequences, target, n: int = 252):\n", "\n", " opt_sequences = feature_sequences[:-n]\n", " test_sequences = feature_sequences[-n:]\n", " opt_target = target[:-n]\n", " test_target = target[-n:]\n", " \n", " train_sequences = opt_sequences[:-n]\n", " val_sequences = opt_sequences[-n:]\n", " train_target = opt_target[:-n]\n", " val_target = opt_target[-n:]\n", " \n", " \n", " return train_sequences, val_sequences, test_sequences, train_target, val_target, test_target\n", "\n", "def get_tt(feature_sequences, target, n: int = 252):\n", "\n", " opt_sequences = feature_sequences[:-n]\n", " test_sequences = feature_sequences[-n:]\n", " opt_target = target[:-n]\n", " test_target = target[-n:]\n", " \n", " return opt_sequences, test_sequences, opt_target, test_target\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "cac08fa6", "metadata": { "execution": { "iopub.execute_input": "2024-05-31T20:47:18.249671Z", "iopub.status.busy": "2024-05-31T20:47:18.248797Z", "iopub.status.idle": "2024-05-31T20:47:20.568103Z", "shell.execute_reply": "2024-05-31T20:47:20.567135Z" }, "papermill": { "duration": 2.328755, "end_time": "2024-05-31T20:47:20.570303", "exception": false, "start_time": "2024-05-31T20:47:18.241548", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_23/1332710684.py:136: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " data['clabel'] = data['return'].apply(lambda x: 1 if x > 0 else 0)\n" ] } ], "source": [ "tickers = ['XOM', 'AAPL', 'JPM', 'PG']\n", "df = get_dataset(tickers[1],\n", " 100,\n", " 1e7,\n", " 'nyt_and_reu')\n", "df = df.drop(columns=['nyt_vader_comp', 'reu_finbert_sent', 'reu_vader_comp', 'nyt_vader_sent', 'reu_vader_sent', 'close'])\n", "prefixes = ['nyt_', 'reu_']\n", "prefixed_cols = [col for col in df.columns if any(col.startswith(prefix) for prefix in prefixes)]\n", "\n", "# Extract base column names\n", "base_names = set(col.split('_', 1)[1] for col in prefixed_cols)\n", "\n", "# Average values of columns with matching base names\n", "for base_name in base_names:\n", " matching_cols = [col for col in prefixed_cols if col.endswith(base_name)]\n", " df[base_name] = df[matching_cols].mean(axis=1)\n", "\n", "# Drop the original prefixed columns\n", "df.drop(columns=prefixed_cols, inplace=True)" ] }, { "cell_type": "code", "execution_count": 5, "id": "db4e4852", "metadata": { "execution": { "iopub.execute_input": "2024-05-31T20:47:20.584456Z", "iopub.status.busy": "2024-05-31T20:47:20.584139Z", "iopub.status.idle": "2024-05-31T22:34:10.969804Z", "shell.execute_reply": "2024-05-31T22:34:10.968961Z" }, "papermill": { "duration": 6410.395245, "end_time": "2024-05-31T22:34:10.972137", "exception": false, "start_time": "2024-05-31T20:47:20.576892", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[I 2024-05-31 20:47:20,597] A new study created in memory with name: no-name-ec9d3986-eba9-4614-8693-e5fadc833048\n", "[I 2024-05-31 20:47:46,713] Trial 0 finished with value: 0.5 and parameters: {'lstm_units_1': 128, 'lstm_units_2': 64, 'dense_units': 16, 'eta': 0.08994154633501211, 'sequence_length': 24}. Best is trial 0 with value: 0.5.\n", "[I 2024-05-31 20:48:10,115] Trial 1 finished with value: 0.5658575296401978 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 32, 'dense_units': 256, 'eta': 0.0002879735177979203, 'sequence_length': 46}. Best is trial 1 with value: 0.5658575296401978.\n", "[I 2024-05-31 20:48:33,490] Trial 2 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 16, 'dense_units': 32, 'eta': 0.04662958353861651, 'sequence_length': 48}. Best is trial 1 with value: 0.5658575296401978.\n", "[I 2024-05-31 20:49:04,881] Trial 3 finished with value: 0.49255985021591187 and parameters: {'lstm_units_1': 256, 'lstm_units_2': 256, 'dense_units': 16, 'eta': 0.004998660098348866, 'sequence_length': 29}. Best is trial 1 with value: 0.5658575296401978.\n", "[I 2024-05-31 20:49:27,325] Trial 4 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 4, 'dense_units': 32, 'eta': 0.06153276097200958, 'sequence_length': 36}. Best is trial 1 with value: 0.5658575296401978.\n", "[I 2024-05-31 20:49:51,306] Trial 5 finished with value: 0.45598989725112915 and parameters: {'lstm_units_1': 128, 'lstm_units_2': 8, 'dense_units': 8, 'eta': 0.00027438995665091933, 'sequence_length': 42}. Best is trial 1 with value: 0.5658575296401978.\n", "[I 2024-05-31 20:49:54,470] Trial 6 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:49:57,983] Trial 7 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:50:19,768] Trial 8 finished with value: 0.4880201816558838 and parameters: {'lstm_units_1': 8, 'lstm_units_2': 32, 'dense_units': 32, 'eta': 0.004232477906799194, 'sequence_length': 32}. Best is trial 1 with value: 0.5658575296401978.\n", "[I 2024-05-31 20:50:51,014] Trial 9 finished with value: 0.5 and parameters: {'lstm_units_1': 256, 'lstm_units_2': 8, 'dense_units': 256, 'eta': 0.010142832164259812, 'sequence_length': 50}. Best is trial 1 with value: 0.5658575296401978.\n", "[I 2024-05-31 20:50:54,115] Trial 10 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:50:57,667] Trial 11 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:51:01,137] Trial 12 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:51:04,319] Trial 13 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:51:07,821] Trial 14 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:51:42,891] Trial 15 finished with value: 0.5076923370361328 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.027328600972558872, 'sequence_length': 58}. Best is trial 1 with value: 0.5658575296401978.\n", "[I 2024-05-31 20:52:16,189] Trial 16 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.029988935891552555, 'sequence_length': 53}. Best is trial 1 with value: 0.5658575296401978.\n", "[I 2024-05-31 20:52:19,915] Trial 17 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:52:23,485] Trial 18 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:52:26,546] Trial 19 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:52:30,220] Trial 20 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:52:50,381] Trial 21 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 64, 'dense_units': 64, 'eta': 0.08472662725637228, 'sequence_length': 23}. Best is trial 1 with value: 0.5658575296401978.\n", "[I 2024-05-31 20:53:27,649] Trial 22 finished with value: 0.5 and parameters: {'lstm_units_1': 128, 'lstm_units_2': 256, 'dense_units': 256, 'eta': 0.038773091319189615, 'sequence_length': 55}. Best is trial 1 with value: 0.5658575296401978.\n", "[I 2024-05-31 20:54:01,457] Trial 23 finished with value: 0.5 and parameters: {'lstm_units_1': 128, 'lstm_units_2': 256, 'dense_units': 16, 'eta': 0.08487405203056914, 'sequence_length': 47}. Best is trial 1 with value: 0.5658575296401978.\n", "[I 2024-05-31 20:54:05,238] Trial 24 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:54:08,858] Trial 25 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:54:11,940] Trial 26 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:54:15,682] Trial 27 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:54:39,499] Trial 28 finished with value: 0.5 and parameters: {'lstm_units_1': 16, 'lstm_units_2': 8, 'dense_units': 8, 'eta': 0.09436378318112384, 'sequence_length': 56}. Best is trial 1 with value: 0.5658575296401978.\n", "[I 2024-05-31 20:54:43,205] Trial 29 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:54:46,427] Trial 30 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:54:50,190] Trial 31 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:54:53,269] Trial 32 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:54:57,175] Trial 33 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:55:00,254] Trial 34 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:55:03,411] Trial 35 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:55:07,398] Trial 36 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:55:10,666] Trial 37 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:55:14,534] Trial 38 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:55:36,620] Trial 39 finished with value: 0.5067465305328369 and parameters: {'lstm_units_1': 128, 'lstm_units_2': 4, 'dense_units': 8, 'eta': 0.0002269129042842351, 'sequence_length': 34}. Best is trial 1 with value: 0.5658575296401978.\n", "[I 2024-05-31 20:55:40,518] Trial 40 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:55:43,608] Trial 41 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:55:46,692] Trial 42 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:55:50,613] Trial 43 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:55:53,857] Trial 44 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:55:57,798] Trial 45 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:56:00,844] Trial 46 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:56:21,024] Trial 47 finished with value: 0.5038461685180664 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 128, 'dense_units': 64, 'eta': 0.03164018802642687, 'sequence_length': 13}. Best is trial 1 with value: 0.5658575296401978.\n", "[I 2024-05-31 20:56:25,023] Trial 48 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:56:28,102] Trial 49 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:56:48,214] Trial 50 finished with value: 0.5 and parameters: {'lstm_units_1': 256, 'lstm_units_2': 128, 'dense_units': 64, 'eta': 0.03017408695467507, 'sequence_length': 10}. Best is trial 1 with value: 0.5658575296401978.\n", "[I 2024-05-31 20:57:10,031] Trial 51 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 32, 'dense_units': 64, 'eta': 0.05832464587288706, 'sequence_length': 25}. Best is trial 1 with value: 0.5658575296401978.\n", "[I 2024-05-31 20:57:34,808] Trial 52 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 16, 'eta': 0.03641044498431581, 'sequence_length': 30}. Best is trial 1 with value: 0.5658575296401978.\n", "[I 2024-05-31 20:57:56,552] Trial 53 finished with value: 0.5783417820930481 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 64, 'dense_units': 256, 'eta': 0.014400049536893974, 'sequence_length': 20}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 20:57:59,627] Trial 54 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:58:02,740] Trial 55 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:58:23,579] Trial 56 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 64, 'dense_units': 256, 'eta': 0.012945282439933037, 'sequence_length': 14}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 20:58:46,033] Trial 57 finished with value: 0.4734552204608917 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 64, 'dense_units': 256, 'eta': 0.0012590185705216, 'sequence_length': 34}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 20:58:49,169] Trial 58 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:58:53,371] Trial 59 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:59:13,156] Trial 60 finished with value: 0.5 and parameters: {'lstm_units_1': 32, 'lstm_units_2': 32, 'dense_units': 256, 'eta': 0.07222084401065092, 'sequence_length': 16}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 20:59:16,300] Trial 61 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:59:20,624] Trial 62 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:59:23,797] Trial 63 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:59:26,975] Trial 64 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:59:31,232] Trial 65 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:59:34,527] Trial 66 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:59:37,668] Trial 67 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 20:59:40,803] Trial 68 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:00:05,549] Trial 69 finished with value: 0.5 and parameters: {'lstm_units_1': 128, 'lstm_units_2': 128, 'dense_units': 64, 'eta': 0.0793713522986502, 'sequence_length': 25}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:00:08,735] Trial 70 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:00:11,799] Trial 71 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:00:16,127] Trial 72 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:00:19,198] Trial 73 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:00:22,269] Trial 74 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:00:25,516] Trial 75 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:00:29,896] Trial 76 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:00:33,078] Trial 77 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:00:36,287] Trial 78 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:00:39,359] Trial 79 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:00:43,842] Trial 80 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:00:47,067] Trial 81 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:00:50,261] Trial 82 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:00:54,844] Trial 83 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:00:58,010] Trial 84 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:01:01,124] Trial 85 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:01:04,266] Trial 86 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:01:08,963] Trial 87 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:01:12,093] Trial 88 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:01:35,131] Trial 89 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 256, 'dense_units': 32, 'eta': 0.023736602630639456, 'sequence_length': 20}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:01:38,221] Trial 90 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:01:42,981] Trial 91 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:02:16,957] Trial 92 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.03561088667983097, 'sequence_length': 54}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:02:20,165] Trial 93 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:02:47,067] Trial 94 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 64, 'dense_units': 64, 'eta': 0.048336994551331496, 'sequence_length': 56}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:02:50,148] Trial 95 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:02:55,077] Trial 96 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:02:58,213] Trial 97 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:03:01,412] Trial 98 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:03:25,115] Trial 99 finished with value: 0.5 and parameters: {'lstm_units_1': 8, 'lstm_units_2': 64, 'dense_units': 128, 'eta': 0.02903977452160755, 'sequence_length': 51}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:03:29,848] Trial 100 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:03:49,852] Trial 101 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 64, 'dense_units': 64, 'eta': 0.08704955150836953, 'sequence_length': 19}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:04:10,298] Trial 102 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 64, 'dense_units': 64, 'eta': 0.05373092326799685, 'sequence_length': 22}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:04:13,408] Trial 103 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:04:18,172] Trial 104 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:04:21,348] Trial 105 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:04:24,449] Trial 106 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:04:27,617] Trial 107 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:04:30,782] Trial 108 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:04:35,618] Trial 109 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:04:38,796] Trial 110 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:05:15,928] Trial 111 finished with value: 0.5 and parameters: {'lstm_units_1': 128, 'lstm_units_2': 256, 'dense_units': 256, 'eta': 0.0391441059007103, 'sequence_length': 55}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:05:53,852] Trial 112 finished with value: 0.5 and parameters: {'lstm_units_1': 128, 'lstm_units_2': 256, 'dense_units': 256, 'eta': 0.02610341092091221, 'sequence_length': 58}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:05:57,125] Trial 113 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:06:02,266] Trial 114 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:06:05,559] Trial 115 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:06:08,761] Trial 116 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:06:12,148] Trial 117 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:06:15,455] Trial 118 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:06:20,537] Trial 119 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:06:23,958] Trial 120 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:06:27,502] Trial 121 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:06:31,062] Trial 122 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:06:34,658] Trial 123 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:06:39,640] Trial 124 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:06:42,751] Trial 125 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:07:05,085] Trial 126 finished with value: 0.5 and parameters: {'lstm_units_1': 8, 'lstm_units_2': 256, 'dense_units': 256, 'eta': 0.06542146651601827, 'sequence_length': 21}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:07:08,256] Trial 127 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:07:11,393] Trial 128 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:07:39,298] Trial 129 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.017057269769447988, 'sequence_length': 35}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:07:42,401] Trial 130 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:07:45,629] Trial 131 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:07:48,891] Trial 132 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:07:52,018] Trial 133 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:07:55,169] Trial 134 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:08:00,447] Trial 135 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:08:03,555] Trial 136 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:08:06,616] Trial 137 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:08:09,670] Trial 138 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:08:12,899] Trial 139 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:08:16,017] Trial 140 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:08:21,413] Trial 141 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:08:24,553] Trial 142 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:08:27,646] Trial 143 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:08:30,724] Trial 144 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:08:33,917] Trial 145 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:08:37,024] Trial 146 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:09:18,076] Trial 147 finished with value: 0.5 and parameters: {'lstm_units_1': 256, 'lstm_units_2': 128, 'dense_units': 64, 'eta': 0.08616387625451401, 'sequence_length': 59}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:09:49,403] Trial 148 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 8, 'eta': 0.032225050907791486, 'sequence_length': 55}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:09:52,528] Trial 149 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:09:55,758] Trial 150 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:10:16,549] Trial 151 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 32, 'dense_units': 64, 'eta': 0.07537442059552331, 'sequence_length': 25}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:10:19,643] Trial 152 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:10:25,112] Trial 153 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:10:28,282] Trial 154 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:10:31,372] Trial 155 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:10:34,596] Trial 156 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:10:37,675] Trial 157 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:10:40,809] Trial 158 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:11:10,407] Trial 159 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 128, 'dense_units': 256, 'eta': 0.08982336897221367, 'sequence_length': 49}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:11:13,632] Trial 160 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:11:16,790] Trial 161 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:11:41,294] Trial 162 finished with value: 0.5178751945495605 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 16, 'eta': 0.02613892456757272, 'sequence_length': 29}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:11:44,528] Trial 163 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:11:47,688] Trial 164 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:11:50,820] Trial 165 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:12:24,102] Trial 166 finished with value: 0.5 and parameters: {'lstm_units_1': 256, 'lstm_units_2': 256, 'dense_units': 256, 'eta': 0.07588965229314383, 'sequence_length': 26}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:12:27,214] Trial 167 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:12:30,364] Trial 168 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:12:56,336] Trial 169 finished with value: 0.5044136047363281 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 64, 'dense_units': 64, 'eta': 0.017074671649059438, 'sequence_length': 54}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:12:59,470] Trial 170 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:13:02,602] Trial 171 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:13:08,434] Trial 172 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:13:11,622] Trial 173 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:13:14,925] Trial 174 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:13:18,016] Trial 175 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:13:21,173] Trial 176 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:13:24,381] Trial 177 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:13:27,569] Trial 178 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:13:33,405] Trial 179 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:13:58,857] Trial 180 finished with value: 0.504256010055542 and parameters: {'lstm_units_1': 16, 'lstm_units_2': 256, 'dense_units': 32, 'eta': 0.03623975709340365, 'sequence_length': 30}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:14:02,027] Trial 181 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:14:27,087] Trial 182 finished with value: 0.5 and parameters: {'lstm_units_1': 16, 'lstm_units_2': 256, 'dense_units': 32, 'eta': 0.09946921435581023, 'sequence_length': 30}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:14:30,227] Trial 183 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:14:53,682] Trial 184 finished with value: 0.5 and parameters: {'lstm_units_1': 16, 'lstm_units_2': 256, 'dense_units': 32, 'eta': 0.030593732261576, 'sequence_length': 25}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:14:56,843] Trial 185 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:14:59,966] Trial 186 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:15:06,102] Trial 187 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:15:09,294] Trial 188 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:15:12,490] Trial 189 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:15:15,690] Trial 190 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:15:40,302] Trial 191 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 16, 'eta': 0.034010247224397126, 'sequence_length': 30}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:15:43,564] Trial 192 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:15:46,736] Trial 193 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:15:52,778] Trial 194 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:15:56,057] Trial 195 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:15:59,229] Trial 196 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:16:02,299] Trial 197 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:16:23,032] Trial 198 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.05389149674043013, 'sequence_length': 13}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:16:26,158] Trial 199 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:16:48,664] Trial 200 finished with value: 0.5 and parameters: {'lstm_units_1': 128, 'lstm_units_2': 32, 'dense_units': 256, 'eta': 0.08602822947338015, 'sequence_length': 29}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:16:51,778] Trial 201 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:16:57,980] Trial 202 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:17:01,142] Trial 203 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:17:04,279] Trial 204 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:17:07,383] Trial 205 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:17:10,457] Trial 206 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:17:39,377] Trial 207 finished with value: 0.5 and parameters: {'lstm_units_1': 256, 'lstm_units_2': 256, 'dense_units': 16, 'eta': 0.03675428710834393, 'sequence_length': 24}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:17:42,511] Trial 208 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:17:45,653] Trial 209 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:17:51,992] Trial 210 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:17:55,136] Trial 211 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:17:58,219] Trial 212 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:18:01,369] Trial 213 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:18:04,501] Trial 214 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:18:24,778] Trial 215 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.05228932993304546, 'sequence_length': 14}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:18:27,999] Trial 216 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:18:31,128] Trial 217 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:18:57,618] Trial 218 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.024842380739640124, 'sequence_length': 22}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:19:00,928] Trial 219 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:19:04,133] Trial 220 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:19:07,255] Trial 221 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:19:10,348] Trial 222 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:19:32,871] Trial 223 finished with value: 0.5 and parameters: {'lstm_units_1': 128, 'lstm_units_2': 128, 'dense_units': 64, 'eta': 0.07891327571397827, 'sequence_length': 24}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:19:36,037] Trial 224 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:19:39,147] Trial 225 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:19:42,367] Trial 226 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:19:48,911] Trial 227 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:19:52,022] Trial 228 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:20:23,296] Trial 229 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.03581494355781622, 'sequence_length': 55}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:20:26,465] Trial 230 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:20:29,675] Trial 231 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:20:52,424] Trial 232 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 256, 'dense_units': 32, 'eta': 0.024427812145185974, 'sequence_length': 20}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:20:55,683] Trial 233 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:21:25,302] Trial 234 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 256, 'dense_units': 32, 'eta': 0.013926701341937409, 'sequence_length': 42}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:21:28,448] Trial 235 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:21:35,242] Trial 236 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:21:38,374] Trial 237 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:21:41,664] Trial 238 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:21:44,821] Trial 239 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:21:47,928] Trial 240 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:21:51,152] Trial 241 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:21:54,442] Trial 242 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:21:57,674] Trial 243 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:22:00,933] Trial 244 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:22:07,771] Trial 245 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:22:10,932] Trial 246 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:22:14,265] Trial 247 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:22:17,383] Trial 248 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:22:20,504] Trial 249 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:22:23,840] Trial 250 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:22:26,972] Trial 251 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:22:30,067] Trial 252 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:22:33,247] Trial 253 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:22:36,648] Trial 254 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:22:43,601] Trial 255 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:23:07,921] Trial 256 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 128, 'dense_units': 64, 'eta': 0.08768892531559415, 'sequence_length': 45}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:23:11,125] Trial 257 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:23:14,327] Trial 258 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:23:17,506] Trial 259 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:23:20,652] Trial 260 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:23:23,917] Trial 261 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:23:27,011] Trial 262 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:23:30,091] Trial 263 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:23:33,254] Trial 264 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:23:40,323] Trial 265 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:23:43,514] Trial 266 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:23:46,633] Trial 267 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:24:12,477] Trial 268 finished with value: 0.5321564078330994 and parameters: {'lstm_units_1': 32, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.0564080222330128, 'sequence_length': 30}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:24:15,764] Trial 269 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:24:18,857] Trial 270 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:24:21,963] Trial 271 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:24:25,051] Trial 272 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:24:50,310] Trial 273 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 16, 'eta': 0.05380152757331454, 'sequence_length': 31}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:24:53,453] Trial 274 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:25:00,674] Trial 275 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:25:24,411] Trial 276 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.0739059136249032, 'sequence_length': 26}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:25:47,730] Trial 277 finished with value: 0.5 and parameters: {'lstm_units_1': 128, 'lstm_units_2': 32, 'dense_units': 256, 'eta': 0.09946198022685136, 'sequence_length': 32}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:25:50,875] Trial 278 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:25:54,099] Trial 279 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:25:57,288] Trial 280 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:26:00,450] Trial 281 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:26:03,639] Trial 282 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:26:06,816] Trial 283 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:26:09,908] Trial 284 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:26:17,266] Trial 285 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:26:20,543] Trial 286 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:26:23,691] Trial 287 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:26:26,809] Trial 288 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:26:29,980] Trial 289 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:26:33,109] Trial 290 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:26:36,233] Trial 291 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:26:39,293] Trial 292 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:26:42,427] Trial 293 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:26:45,568] Trial 294 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:27:05,781] Trial 295 finished with value: 0.5 and parameters: {'lstm_units_1': 16, 'lstm_units_2': 64, 'dense_units': 128, 'eta': 0.05746222893064261, 'sequence_length': 23}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:27:13,484] Trial 296 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:27:16,598] Trial 297 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:27:19,846] Trial 298 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:27:46,071] Trial 299 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.033943494395827264, 'sequence_length': 32}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:27:49,175] Trial 300 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:28:10,951] Trial 301 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 256, 'eta': 0.04863736999262648, 'sequence_length': 19}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:28:14,116] Trial 302 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:28:17,352] Trial 303 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:28:20,500] Trial 304 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:28:23,734] Trial 305 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:28:26,861] Trial 306 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:28:30,033] Trial 307 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:28:37,800] Trial 308 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:28:40,931] Trial 309 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:28:44,069] Trial 310 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:29:16,513] Trial 311 finished with value: 0.5 and parameters: {'lstm_units_1': 128, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.04884782618478905, 'sequence_length': 43}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:29:38,396] Trial 312 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 128, 'dense_units': 32, 'eta': 0.023542097162472027, 'sequence_length': 33}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:29:41,517] Trial 313 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:29:44,652] Trial 314 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:30:14,828] Trial 315 finished with value: 0.5 and parameters: {'lstm_units_1': 16, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.056377606657468715, 'sequence_length': 50}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:30:17,986] Trial 316 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:30:21,098] Trial 317 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:30:24,216] Trial 318 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:30:32,117] Trial 319 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:30:35,350] Trial 320 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:30:38,648] Trial 321 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:30:41,752] Trial 322 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:30:45,005] Trial 323 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:30:48,821] Trial 324 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:30:51,932] Trial 325 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:30:55,097] Trial 326 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:30:58,251] Trial 327 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:31:01,425] Trial 328 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:31:04,731] Trial 329 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:31:07,933] Trial 330 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:31:16,112] Trial 331 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:31:19,395] Trial 332 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:31:22,542] Trial 333 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:31:25,757] Trial 334 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:31:28,961] Trial 335 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:31:32,153] Trial 336 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:31:52,605] Trial 337 finished with value: 0.5 and parameters: {'lstm_units_1': 16, 'lstm_units_2': 256, 'dense_units': 256, 'eta': 0.023148350430807697, 'sequence_length': 15}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:31:55,759] Trial 338 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:31:58,837] Trial 339 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:32:02,140] Trial 340 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:32:05,372] Trial 341 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:32:08,569] Trial 342 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:32:11,693] Trial 343 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:32:20,020] Trial 344 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:32:41,506] Trial 345 finished with value: 0.5 and parameters: {'lstm_units_1': 32, 'lstm_units_2': 128, 'dense_units': 256, 'eta': 0.05145734615543893, 'sequence_length': 24}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:32:44,778] Trial 346 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:32:47,945] Trial 347 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:32:51,033] Trial 348 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:32:54,228] Trial 349 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:32:57,336] Trial 350 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:33:00,583] Trial 351 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:33:03,723] Trial 352 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:33:07,026] Trial 353 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:33:10,158] Trial 354 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:33:40,378] Trial 355 finished with value: 0.5 and parameters: {'lstm_units_1': 8, 'lstm_units_2': 256, 'dense_units': 256, 'eta': 0.05648264044007575, 'sequence_length': 49}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:33:43,507] Trial 356 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:33:51,940] Trial 357 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:33:55,113] Trial 358 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:33:58,357] Trial 359 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:34:19,222] Trial 360 finished with value: 0.5 and parameters: {'lstm_units_1': 16, 'lstm_units_2': 64, 'dense_units': 256, 'eta': 0.0684726843491424, 'sequence_length': 23}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:34:22,391] Trial 361 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:34:46,926] Trial 362 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 16, 'eta': 0.08476334466549539, 'sequence_length': 28}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:34:50,206] Trial 363 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:34:53,336] Trial 364 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:34:56,455] Trial 365 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:34:59,669] Trial 366 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:35:02,839] Trial 367 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:35:06,232] Trial 368 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:35:09,372] Trial 369 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:35:18,047] Trial 370 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:35:21,290] Trial 371 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:35:41,151] Trial 372 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.015603650060217618, 'sequence_length': 11}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:35:44,416] Trial 373 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:35:47,622] Trial 374 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:35:50,890] Trial 375 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:35:54,193] Trial 376 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:35:57,531] Trial 377 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:36:00,644] Trial 378 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:36:03,808] Trial 379 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:36:37,857] Trial 380 finished with value: 0.5 and parameters: {'lstm_units_1': 128, 'lstm_units_2': 256, 'dense_units': 16, 'eta': 0.08187571098933401, 'sequence_length': 46}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:36:41,048] Trial 381 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:36:44,256] Trial 382 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:37:07,639] Trial 383 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 256, 'dense_units': 256, 'eta': 0.02408110799397142, 'sequence_length': 20}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:37:16,499] Trial 384 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:37:19,865] Trial 385 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:37:23,158] Trial 386 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:37:26,315] Trial 387 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:37:29,523] Trial 388 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:37:32,686] Trial 389 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:37:35,867] Trial 390 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:37:38,988] Trial 391 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:37:42,174] Trial 392 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:37:45,358] Trial 393 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:37:48,612] Trial 394 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:37:51,859] Trial 395 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:37:55,070] Trial 396 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:37:58,297] Trial 397 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:38:01,470] Trial 398 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:38:38,323] Trial 399 finished with value: 0.5 and parameters: {'lstm_units_1': 256, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.0531001005124606, 'sequence_length': 26}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:38:41,532] Trial 400 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:39:03,054] Trial 401 finished with value: 0.5 and parameters: {'lstm_units_1': 16, 'lstm_units_2': 128, 'dense_units': 256, 'eta': 0.07530898576348748, 'sequence_length': 27}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:39:06,274] Trial 402 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:39:09,531] Trial 403 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:39:12,758] Trial 404 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:39:15,934] Trial 405 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:39:19,085] Trial 406 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:39:22,232] Trial 407 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:39:25,460] Trial 408 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:39:28,801] Trial 409 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:39:31,921] Trial 410 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:39:35,062] Trial 411 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:39:58,914] Trial 412 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 256, 'dense_units': 128, 'eta': 0.021214805091963433, 'sequence_length': 22}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:40:08,246] Trial 413 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:40:11,510] Trial 414 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:40:14,894] Trial 415 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:40:36,392] Trial 416 finished with value: 0.5 and parameters: {'lstm_units_1': 8, 'lstm_units_2': 128, 'dense_units': 256, 'eta': 0.04394459827318918, 'sequence_length': 25}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:40:39,630] Trial 417 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:40:42,849] Trial 418 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:40:46,041] Trial 419 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:40:49,209] Trial 420 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:40:52,464] Trial 421 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:40:55,629] Trial 422 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:40:58,898] Trial 423 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:41:02,205] Trial 424 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:41:05,517] Trial 425 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:41:08,815] Trial 426 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:41:41,834] Trial 427 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 256, 'dense_units': 32, 'eta': 0.09998688474717131, 'sequence_length': 50}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:41:45,065] Trial 428 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:41:54,621] Trial 429 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:41:57,930] Trial 430 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:42:01,124] Trial 431 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:42:04,482] Trial 432 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:42:07,647] Trial 433 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:42:10,864] Trial 434 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:42:31,285] Trial 435 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.029156919914219016, 'sequence_length': 14}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:42:34,504] Trial 436 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:42:37,746] Trial 437 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:42:41,086] Trial 438 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:42:44,261] Trial 439 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:42:47,470] Trial 440 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:42:50,642] Trial 441 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:43:12,159] Trial 442 finished with value: 0.5 and parameters: {'lstm_units_1': 16, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.033091174152966224, 'sequence_length': 17}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:43:15,336] Trial 443 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:43:18,582] Trial 444 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:43:28,388] Trial 445 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:43:31,592] Trial 446 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:43:34,885] Trial 447 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:43:38,253] Trial 448 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:43:41,475] Trial 449 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:43:44,676] Trial 450 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:43:47,816] Trial 451 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:43:51,199] Trial 452 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:43:54,389] Trial 453 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:43:57,604] Trial 454 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:44:00,874] Trial 455 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:44:04,120] Trial 456 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:44:07,324] Trial 457 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:44:10,565] Trial 458 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:44:13,874] Trial 459 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:44:17,070] Trial 460 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:44:27,084] Trial 461 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:44:30,389] Trial 462 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:44:33,605] Trial 463 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:44:36,759] Trial 464 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:45:10,343] Trial 465 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 256, 'dense_units': 256, 'eta': 0.08098311444278518, 'sequence_length': 52}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:45:13,669] Trial 466 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:45:16,847] Trial 467 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:45:20,184] Trial 468 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:45:23,352] Trial 469 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:45:26,648] Trial 470 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:45:29,880] Trial 471 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:45:33,068] Trial 472 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:45:36,259] Trial 473 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:45:39,575] Trial 474 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:45:42,799] Trial 475 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:45:46,214] Trial 476 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:45:49,408] Trial 477 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:45:59,735] Trial 478 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:46:03,005] Trial 479 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:46:29,627] Trial 480 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 256, 'dense_units': 256, 'eta': 0.02855318785089261, 'sequence_length': 29}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:46:32,840] Trial 481 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:46:36,091] Trial 482 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:46:39,310] Trial 483 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:46:42,511] Trial 484 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:46:45,709] Trial 485 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:46:48,933] Trial 486 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:46:52,137] Trial 487 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:47:18,777] Trial 488 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 128, 'dense_units': 256, 'eta': 0.03348762722471459, 'sequence_length': 49}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:47:22,010] Trial 489 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:47:25,276] Trial 490 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:47:28,462] Trial 491 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:47:31,717] Trial 492 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:47:34,986] Trial 493 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:47:38,301] Trial 494 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:47:41,496] Trial 495 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:47:52,142] Trial 496 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:47:55,572] Trial 497 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:47:58,860] Trial 498 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:48:20,485] Trial 499 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 64, 'dense_units': 128, 'eta': 0.0305204038892529, 'sequence_length': 29}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:48:23,816] Trial 500 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:48:26,992] Trial 501 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:48:30,187] Trial 502 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:48:33,539] Trial 503 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:48:36,662] Trial 504 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:48:39,827] Trial 505 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:48:42,964] Trial 506 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:48:46,267] Trial 507 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:48:49,408] Trial 508 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:48:52,672] Trial 509 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:48:56,078] Trial 510 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:49:16,998] Trial 511 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 128, 'dense_units': 32, 'eta': 0.08686488017311787, 'sequence_length': 19}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:49:20,242] Trial 512 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:49:23,605] Trial 513 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:49:34,670] Trial 514 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:49:38,009] Trial 515 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:49:41,320] Trial 516 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:49:44,613] Trial 517 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:49:47,838] Trial 518 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:49:51,155] Trial 519 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:50:19,768] Trial 520 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 128, 'dense_units': 256, 'eta': 0.07137214674218163, 'sequence_length': 54}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:50:23,046] Trial 521 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:50:50,006] Trial 522 finished with value: 0.5074400901794434 and parameters: {'lstm_units_1': 32, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.08992979507523735, 'sequence_length': 33}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:51:16,817] Trial 523 finished with value: 0.5 and parameters: {'lstm_units_1': 32, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.08504777039827303, 'sequence_length': 34}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:51:20,051] Trial 524 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:51:42,436] Trial 525 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 64, 'dense_units': 64, 'eta': 0.09420527676340794, 'sequence_length': 32}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:51:45,624] Trial 526 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:51:48,799] Trial 527 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:52:16,159] Trial 528 finished with value: 0.5 and parameters: {'lstm_units_1': 32, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.07704388500604797, 'sequence_length': 34}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:52:19,391] Trial 529 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:52:22,595] Trial 530 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:52:25,918] Trial 531 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:52:29,146] Trial 532 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:52:40,513] Trial 533 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:52:43,764] Trial 534 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:52:46,975] Trial 535 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:52:50,194] Trial 536 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:52:53,463] Trial 537 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:52:56,619] Trial 538 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:52:59,777] Trial 539 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:53:03,055] Trial 540 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:53:06,395] Trial 541 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:53:09,654] Trial 542 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:53:12,825] Trial 543 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:53:16,000] Trial 544 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:53:19,143] Trial 545 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:53:22,369] Trial 546 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:53:25,825] Trial 547 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:53:28,993] Trial 548 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:53:32,326] Trial 549 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:53:35,697] Trial 550 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:53:38,902] Trial 551 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:53:42,101] Trial 552 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:54:23,790] Trial 553 finished with value: 0.5 and parameters: {'lstm_units_1': 32, 'lstm_units_2': 256, 'dense_units': 16, 'eta': 0.05814932085576862, 'sequence_length': 55}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:54:27,007] Trial 554 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:54:30,293] Trial 555 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:54:54,650] Trial 556 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 32, 'dense_units': 256, 'eta': 0.06755023530147973, 'sequence_length': 49}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:54:57,819] Trial 557 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:55:00,992] Trial 558 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:55:04,185] Trial 559 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:55:07,382] Trial 560 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:55:31,052] Trial 561 finished with value: 0.5 and parameters: {'lstm_units_1': 8, 'lstm_units_2': 256, 'dense_units': 128, 'eta': 0.08456111693460915, 'sequence_length': 24}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:55:54,973] Trial 562 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 128, 'dense_units': 256, 'eta': 0.035280642257198985, 'sequence_length': 35}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:55:58,246] Trial 563 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:56:01,469] Trial 564 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:56:04,618] Trial 565 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:56:07,765] Trial 566 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:56:10,909] Trial 567 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:56:14,284] Trial 568 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:56:17,469] Trial 569 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:56:20,735] Trial 570 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:56:44,251] Trial 571 finished with value: 0.5 and parameters: {'lstm_units_1': 16, 'lstm_units_2': 64, 'dense_units': 256, 'eta': 0.03209946212153271, 'sequence_length': 48}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:56:47,472] Trial 572 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:56:59,198] Trial 573 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:57:02,457] Trial 574 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:57:05,746] Trial 575 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:57:29,851] Trial 576 finished with value: 0.5 and parameters: {'lstm_units_1': 32, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.0670636521985577, 'sequence_length': 25}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:57:33,053] Trial 577 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:57:36,449] Trial 578 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:57:39,590] Trial 579 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:57:42,755] Trial 580 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:57:45,892] Trial 581 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:57:49,002] Trial 582 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:57:52,312] Trial 583 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:57:55,538] Trial 584 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:57:58,699] Trial 585 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:58:02,012] Trial 586 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:58:05,176] Trial 587 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:58:08,569] Trial 588 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:58:11,713] Trial 589 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:58:14,911] Trial 590 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:58:18,035] Trial 591 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:58:21,202] Trial 592 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:58:24,581] Trial 593 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:58:36,573] Trial 594 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:58:39,833] Trial 595 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:58:43,038] Trial 596 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:58:46,284] Trial 597 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:58:49,451] Trial 598 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:58:52,651] Trial 599 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:59:19,882] Trial 600 finished with value: 0.5 and parameters: {'lstm_units_1': 32, 'lstm_units_2': 128, 'dense_units': 32, 'eta': 0.09987071921746368, 'sequence_length': 56}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 21:59:23,013] Trial 601 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:59:26,227] Trial 602 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:59:29,501] Trial 603 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:59:32,822] Trial 604 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:59:36,042] Trial 605 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:59:39,260] Trial 606 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:59:42,399] Trial 607 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:59:45,584] Trial 608 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:59:48,754] Trial 609 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:59:51,940] Trial 610 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 21:59:55,113] Trial 611 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:00:16,028] Trial 612 finished with value: 0.5 and parameters: {'lstm_units_1': 16, 'lstm_units_2': 256, 'dense_units': 16, 'eta': 0.08149980766172499, 'sequence_length': 15}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:00:19,172] Trial 613 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:00:22,327] Trial 614 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:00:25,607] Trial 615 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:00:38,032] Trial 616 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:00:41,289] Trial 617 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:00:44,557] Trial 618 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:00:47,754] Trial 619 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:00:50,966] Trial 620 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:00:54,276] Trial 621 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:00:57,445] Trial 622 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:01:00,654] Trial 623 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:01:03,918] Trial 624 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:01:07,117] Trial 625 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:01:30,615] Trial 626 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.06001431226913764, 'sequence_length': 23}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:01:33,894] Trial 627 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:01:37,137] Trial 628 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:01:40,385] Trial 629 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:01:43,578] Trial 630 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:01:46,831] Trial 631 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:01:50,071] Trial 632 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:01:53,211] Trial 633 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:01:56,462] Trial 634 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:02:17,842] Trial 635 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.01962824270837363, 'sequence_length': 16}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:02:21,075] Trial 636 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:02:24,266] Trial 637 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:03:02,049] Trial 638 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 256, 'dense_units': 16, 'eta': 0.0769400770100727, 'sequence_length': 35}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:03:05,337] Trial 639 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:03:08,681] Trial 640 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:03:11,899] Trial 641 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:03:15,366] Trial 642 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:03:18,496] Trial 643 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:03:21,754] Trial 644 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:03:25,040] Trial 645 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:03:28,176] Trial 646 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:03:31,392] Trial 647 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:03:34,588] Trial 648 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:03:37,720] Trial 649 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:03:40,930] Trial 650 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:03:44,137] Trial 651 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:03:47,319] Trial 652 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:03:50,479] Trial 653 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:03:53,624] Trial 654 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:03:56,866] Trial 655 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:04:00,039] Trial 656 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:04:03,395] Trial 657 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:04:06,666] Trial 658 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:04:37,740] Trial 659 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.06723363106583419, 'sequence_length': 53}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:04:40,950] Trial 660 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:04:53,977] Trial 661 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:04:57,310] Trial 662 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:05:00,463] Trial 663 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:05:25,050] Trial 664 finished with value: 0.5 and parameters: {'lstm_units_1': 32, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.038344492106360414, 'sequence_length': 25}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:05:28,270] Trial 665 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:05:31,444] Trial 666 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:05:56,114] Trial 667 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.025092801426127985, 'sequence_length': 30}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:05:59,349] Trial 668 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:06:02,649] Trial 669 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:06:05,824] Trial 670 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:06:08,962] Trial 671 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:06:12,122] Trial 672 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:06:15,315] Trial 673 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:06:18,644] Trial 674 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:06:21,791] Trial 675 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:06:25,058] Trial 676 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:06:28,199] Trial 677 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:06:31,343] Trial 678 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:06:34,589] Trial 679 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:06:37,836] Trial 680 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:06:40,990] Trial 681 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:06:44,218] Trial 682 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:06:47,378] Trial 683 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:06:50,752] Trial 684 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:07:04,312] Trial 685 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:07:07,604] Trial 686 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:07:11,020] Trial 687 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:07:14,255] Trial 688 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:07:17,568] Trial 689 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:07:20,841] Trial 690 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:07:24,233] Trial 691 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:07:27,416] Trial 692 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:07:30,608] Trial 693 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:07:33,775] Trial 694 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:07:36,914] Trial 695 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:07:40,142] Trial 696 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:07:43,487] Trial 697 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:07:46,694] Trial 698 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:07:49,911] Trial 699 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:08:17,410] Trial 700 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 256, 'eta': 0.06787483045711112, 'sequence_length': 39}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:08:20,582] Trial 701 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:08:23,892] Trial 702 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:08:27,128] Trial 703 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:08:30,339] Trial 704 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:08:33,705] Trial 705 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:08:36,875] Trial 706 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:08:40,148] Trial 707 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:08:43,374] Trial 708 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:08:46,662] Trial 709 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:09:00,495] Trial 710 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:09:03,805] Trial 711 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:09:34,841] Trial 712 finished with value: 0.5 and parameters: {'lstm_units_1': 32, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.09959618149885179, 'sequence_length': 46}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:09:38,171] Trial 713 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:09:41,424] Trial 714 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:09:44,738] Trial 715 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:10:07,524] Trial 716 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 16, 'eta': 0.06068978536801953, 'sequence_length': 21}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:10:10,745] Trial 717 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:10:13,991] Trial 718 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:10:17,226] Trial 719 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:10:20,461] Trial 720 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:10:23,663] Trial 721 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:10:26,912] Trial 722 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:10:47,913] Trial 723 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 64, 'dense_units': 64, 'eta': 0.05487969483912233, 'sequence_length': 19}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:10:51,222] Trial 724 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:10:54,467] Trial 725 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:10:57,624] Trial 726 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:11:00,828] Trial 727 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:11:04,083] Trial 728 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:11:07,326] Trial 729 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:11:10,561] Trial 730 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:11:13,803] Trial 731 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:11:45,348] Trial 732 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 128, 'eta': 0.08495556481887738, 'sequence_length': 52}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:11:48,534] Trial 733 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:11:51,839] Trial 734 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:11:55,111] Trial 735 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:12:09,353] Trial 736 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:12:12,766] Trial 737 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:12:16,080] Trial 738 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:12:19,324] Trial 739 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:12:22,516] Trial 740 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:12:25,720] Trial 741 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:12:28,963] Trial 742 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:12:32,124] Trial 743 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:12:35,287] Trial 744 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:12:38,616] Trial 745 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:12:41,926] Trial 746 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:12:45,161] Trial 747 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:12:48,357] Trial 748 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:12:51,524] Trial 749 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:12:54,737] Trial 750 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:12:57,952] Trial 751 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:13:20,208] Trial 752 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.06070333618376283, 'sequence_length': 20}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:13:23,437] Trial 753 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:13:26,642] Trial 754 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:13:29,854] Trial 755 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:13:33,141] Trial 756 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:13:36,388] Trial 757 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:13:39,662] Trial 758 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:13:42,872] Trial 759 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:13:46,162] Trial 760 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:13:49,382] Trial 761 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:14:03,905] Trial 762 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:14:07,199] Trial 763 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:14:10,471] Trial 764 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:14:13,787] Trial 765 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:14:16,978] Trial 766 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:14:20,274] Trial 767 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:14:23,544] Trial 768 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:14:26,770] Trial 769 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:14:29,955] Trial 770 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:14:33,170] Trial 771 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:14:36,330] Trial 772 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:14:39,542] Trial 773 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:14:42,776] Trial 774 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:14:46,077] Trial 775 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:14:49,259] Trial 776 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:14:52,549] Trial 777 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:14:55,762] Trial 778 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:14:58,934] Trial 779 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:15:02,115] Trial 780 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:15:05,348] Trial 781 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:15:08,476] Trial 782 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:15:11,719] Trial 783 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:15:33,797] Trial 784 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.08216004435129244, 'sequence_length': 20}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:15:37,026] Trial 785 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:15:40,322] Trial 786 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:15:43,515] Trial 787 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:15:46,876] Trial 788 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:15:50,150] Trial 789 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:16:05,135] Trial 790 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:16:08,532] Trial 791 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:16:11,782] Trial 792 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:16:15,035] Trial 793 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:16:18,349] Trial 794 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:16:21,554] Trial 795 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:16:24,822] Trial 796 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:16:28,138] Trial 797 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:16:31,288] Trial 798 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:16:34,668] Trial 799 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:16:37,908] Trial 800 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:16:41,113] Trial 801 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:16:44,354] Trial 802 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:16:47,526] Trial 803 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:16:50,736] Trial 804 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:16:53,954] Trial 805 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:16:57,141] Trial 806 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:17:00,355] Trial 807 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:17:03,584] Trial 808 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:17:06,720] Trial 809 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:17:10,143] Trial 810 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:17:13,311] Trial 811 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:17:16,532] Trial 812 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:17:19,776] Trial 813 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:17:23,083] Trial 814 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:17:26,368] Trial 815 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:17:29,525] Trial 816 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:17:32,801] Trial 817 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:17:48,118] Trial 818 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:17:51,630] Trial 819 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:18:13,664] Trial 820 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 32, 'dense_units': 128, 'eta': 0.07196646242883817, 'sequence_length': 32}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:18:16,872] Trial 821 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:18:20,158] Trial 822 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:18:23,455] Trial 823 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:18:26,771] Trial 824 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:18:29,939] Trial 825 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:18:33,139] Trial 826 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:18:36,394] Trial 827 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:18:39,571] Trial 828 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:19:08,344] Trial 829 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 8, 'eta': 0.011938060172255873, 'sequence_length': 44}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:19:11,537] Trial 830 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:19:14,760] Trial 831 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:19:17,918] Trial 832 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:19:38,114] Trial 833 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 64, 'dense_units': 256, 'eta': 0.0658750032287996, 'sequence_length': 17}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:19:41,379] Trial 834 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:19:44,677] Trial 835 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:19:47,833] Trial 836 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:19:50,990] Trial 837 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:19:54,276] Trial 838 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:19:57,458] Trial 839 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:20:00,782] Trial 840 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:20:04,153] Trial 841 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:20:07,342] Trial 842 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:20:10,635] Trial 843 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:20:13,893] Trial 844 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:20:17,253] Trial 845 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:20:20,518] Trial 846 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:20:23,722] Trial 847 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:20:56,912] Trial 848 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 64, 'dense_units': 256, 'eta': 0.07950305838420652, 'sequence_length': 14}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:21:22,844] Trial 849 finished with value: 0.5 and parameters: {'lstm_units_1': 128, 'lstm_units_2': 256, 'dense_units': 128, 'eta': 0.04271098436106652, 'sequence_length': 23}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:21:26,094] Trial 850 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:21:29,477] Trial 851 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:21:32,856] Trial 852 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:21:36,134] Trial 853 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:21:39,339] Trial 854 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:21:42,549] Trial 855 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:21:45,809] Trial 856 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:21:49,110] Trial 857 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:21:52,378] Trial 858 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:21:55,577] Trial 859 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:21:58,800] Trial 860 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:22:01,974] Trial 861 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:22:05,447] Trial 862 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:22:08,723] Trial 863 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:22:11,883] Trial 864 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:22:15,111] Trial 865 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:22:18,331] Trial 866 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:22:21,588] Trial 867 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:22:24,816] Trial 868 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:22:27,982] Trial 869 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:22:31,263] Trial 870 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:22:34,509] Trial 871 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:22:37,896] Trial 872 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:22:41,113] Trial 873 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:22:44,382] Trial 874 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:22:47,577] Trial 875 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:22:50,834] Trial 876 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:22:54,052] Trial 877 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:23:10,468] Trial 878 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:23:13,909] Trial 879 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:23:17,199] Trial 880 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:23:20,591] Trial 881 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:23:23,902] Trial 882 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:23:27,279] Trial 883 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:23:30,501] Trial 884 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:23:33,739] Trial 885 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:23:59,948] Trial 886 finished with value: 0.5 and parameters: {'lstm_units_1': 16, 'lstm_units_2': 256, 'dense_units': 32, 'eta': 0.012244104007992375, 'sequence_length': 33}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:24:03,194] Trial 887 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:24:07,024] Trial 888 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:24:10,271] Trial 889 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:24:37,487] Trial 890 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.0481710819935601, 'sequence_length': 38}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:24:40,710] Trial 891 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:24:44,016] Trial 892 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:24:47,182] Trial 893 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:24:50,535] Trial 894 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:24:53,762] Trial 895 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:24:56,910] Trial 896 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:25:00,184] Trial 897 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:25:03,536] Trial 898 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:25:06,784] Trial 899 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:25:09,949] Trial 900 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:25:13,166] Trial 901 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:25:16,475] Trial 902 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:25:19,805] Trial 903 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:25:23,051] Trial 904 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:25:50,031] Trial 905 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 256, 'eta': 0.0897312590873775, 'sequence_length': 36}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:25:53,251] Trial 906 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:25:56,599] Trial 907 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:25:59,897] Trial 908 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:26:03,170] Trial 909 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:26:20,174] Trial 910 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:26:23,601] Trial 911 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:26:26,888] Trial 912 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:26:30,164] Trial 913 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:26:33,608] Trial 914 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:26:36,852] Trial 915 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:26:40,157] Trial 916 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:26:43,551] Trial 917 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:27:15,176] Trial 918 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 32, 'eta': 0.03402918775447913, 'sequence_length': 55}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:27:18,473] Trial 919 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:27:21,715] Trial 920 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:27:24,985] Trial 921 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:27:28,265] Trial 922 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:27:31,492] Trial 923 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:27:34,702] Trial 924 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:27:37,897] Trial 925 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:27:41,183] Trial 926 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:27:44,436] Trial 927 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:27:47,736] Trial 928 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:27:50,931] Trial 929 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:27:54,263] Trial 930 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:27:57,473] Trial 931 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:28:00,637] Trial 932 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:28:03,927] Trial 933 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:28:07,171] Trial 934 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:28:10,378] Trial 935 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:28:13,663] Trial 936 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:28:16,898] Trial 937 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:28:20,070] Trial 938 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:28:23,302] Trial 939 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:28:26,826] Trial 940 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:28:30,171] Trial 941 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:29:10,806] Trial 942 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 128, 'dense_units': 4, 'eta': 0.07730921775154388, 'sequence_length': 57}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:29:14,322] Trial 943 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:29:17,629] Trial 944 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:29:20,957] Trial 945 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:29:24,231] Trial 946 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:29:27,708] Trial 947 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:29:31,013] Trial 948 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:29:34,303] Trial 949 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:29:37,561] Trial 950 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:29:40,780] Trial 951 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:29:44,025] Trial 952 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:30:16,663] Trial 953 finished with value: 0.5 and parameters: {'lstm_units_1': 64, 'lstm_units_2': 256, 'dense_units': 16, 'eta': 0.048398263865680635, 'sequence_length': 50}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:30:19,824] Trial 954 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:30:22,989] Trial 955 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:30:26,273] Trial 956 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:30:29,559] Trial 957 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:30:32,797] Trial 958 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:30:36,092] Trial 959 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:31:06,201] Trial 960 finished with value: 0.5 and parameters: {'lstm_units_1': 16, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.06890569412090038, 'sequence_length': 48}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:31:09,461] Trial 961 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:31:12,735] Trial 962 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:31:16,001] Trial 963 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:31:19,231] Trial 964 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:31:43,234] Trial 965 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 256, 'eta': 0.03432636010019072, 'sequence_length': 27}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:31:46,479] Trial 966 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:31:49,715] Trial 967 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:31:52,924] Trial 968 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:31:56,177] Trial 969 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:31:59,546] Trial 970 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:32:02,846] Trial 971 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:32:06,200] Trial 972 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:32:09,574] Trial 973 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:32:12,828] Trial 974 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:32:36,474] Trial 975 finished with value: 0.5 and parameters: {'lstm_units_1': 4, 'lstm_units_2': 256, 'dense_units': 64, 'eta': 0.09978673267948499, 'sequence_length': 26}. Best is trial 53 with value: 0.5783417820930481.\n", "[I 2024-05-31 22:32:54,506] Trial 976 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:32:57,990] Trial 977 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:33:01,376] Trial 978 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:33:04,753] Trial 979 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:33:08,081] Trial 980 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:33:11,453] Trial 981 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:33:14,828] Trial 982 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:33:18,090] Trial 983 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:33:21,468] Trial 984 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:33:24,897] Trial 985 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:33:28,099] Trial 986 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:33:31,412] Trial 987 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:33:34,677] Trial 988 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:33:37,993] Trial 989 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:33:41,408] Trial 990 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:33:44,666] Trial 991 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:33:47,974] Trial 992 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:33:51,319] Trial 993 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:33:54,563] Trial 994 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:33:57,868] Trial 995 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:34:01,125] Trial 996 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:34:04,455] Trial 997 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:34:07,739] Trial 998 pruned. Trial was pruned at epoch 0.\n", "[I 2024-05-31 22:34:10,964] Trial 999 pruned. Trial was pruned at epoch 0.\n" ] } ], "source": [ "# Ensure TensorFlow uses GPU\n", "gpus = tf.config.experimental.list_physical_devices('GPU')\n", "if gpus:\n", " try:\n", " for gpu in gpus:\n", " tf.config.experimental.set_memory_growth(gpu, True)\n", " except RuntimeError as e:\n", " print(e)\n", "\n", "# \n", "\n", "\n", "def create_lstm_model(input_shape, lstm_units_1, lstm_units_2, dense_units, eta):\n", " inputs = Input(shape=input_shape)\n", " x = LSTM(units=lstm_units_1, return_sequences=True)(inputs)\n", " x = LSTM(units=lstm_units_2)(x)\n", " x = Dense(units=dense_units, activation='relu')(x)\n", " outputs = Dense(units=1, activation='sigmoid')(x)\n", " \n", " model = Model(inputs=inputs, outputs=outputs)\n", " \n", " model.compile(optimizer=Adam(eta), loss='binary_crossentropy', metrics=[tf.keras.metrics.AUC()])\n", " return model\n", "\n", "def objective(trial, df):\n", " # Hyperparameter space\n", " \n", " powers_of_two = [2 ** n for n in range(2, 9)]\n", " lstm_units_1 = trial.suggest_categorical('lstm_units_1', powers_of_two)\n", " lstm_units_2 = trial.suggest_categorical('lstm_units_2', powers_of_two)\n", " dense_units = trial.suggest_categorical('dense_units', powers_of_two)\n", " batch_size = 128\n", " epochs = 128\n", " eta = trial.suggest_float('eta', 1e-4, 1e-1, log=True)\n", " slen = trial.suggest_int('sequence_length', 10, 60)\n", " fs, t = get_sequences(df, 'clabel', slen)\n", " trs, vs, tss, trt, vt, tst = get_train_val_test(fs, t)\n", " # Create model\n", " model = create_lstm_model(input_shape=(trs.shape[1], trs.shape[2]), \n", " lstm_units_1=lstm_units_1, \n", " lstm_units_2=lstm_units_2, \n", " dense_units=dense_units,\n", " eta=eta)\n", " \n", " # Early stopping callback\n", "# early_stopping = EarlyStopping(monitor='val_loss', patience=5)\n", " \n", " # Pruning callback\n", " pruning_callback = TFKerasPruningCallback(trial, 'val_loss')\n", " \n", " \n", " # Train model\n", " history = model.fit(trs, trt,\n", " validation_data=(vs, vt),\n", " epochs=epochs,\n", " batch_size=batch_size,\n", " callbacks=[pruning_callback],\n", " verbose=0)\n", " \n", " # Evaluate model\n", " val_loss, auc = model.evaluate(vs, vt, verbose=0)\n", " return auc\n", "\n", "objective_wrap = partial(objective, df=df)\n", "study = optuna.create_study(direction='maximize')\n", "study.optimize(objective_wrap, n_trials=1000)\n", "\n", "ofile = open('/kaggle/working/output.txt', 'w')\n", "print('Best trial:', file=ofile)\n", "trial = study.best_trial\n", "\n", "print(' Value: {}'.format(trial.value), file=ofile)\n", "\n", "print(' Params: ', file=ofile)\n", "for key, value in trial.params.items():\n", " print(' {}: {}'.format(key, value), file=ofile)" ] }, { "cell_type": "code", "execution_count": 6, "id": "3183eb5d", "metadata": { "execution": { "iopub.execute_input": "2024-05-31T22:34:11.148541Z", "iopub.status.busy": "2024-05-31T22:34:11.147692Z", "iopub.status.idle": "2024-05-31T22:34:34.124999Z", "shell.execute_reply": "2024-05-31T22:34:34.124249Z" }, "papermill": { "duration": 23.063909, "end_time": "2024-05-31T22:34:34.127008", "exception": false, "start_time": "2024-05-31T22:34:11.063099", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m3s\u001B[0m 30ms/step - auc_1000: 0.4947 - loss: 0.8483 - val_auc_1000: 0.5151 - val_loss: 0.6937\n", "Epoch 2/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.4836 - loss: 0.6938 - val_auc_1000: 0.5000 - val_loss: 0.6936\n", "Epoch 3/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.4897 - loss: 0.6924 - val_auc_1000: 0.4357 - val_loss: 0.6935\n", "Epoch 4/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.4804 - loss: 0.6931 - val_auc_1000: 0.4417 - val_loss: 0.6943\n", "Epoch 5/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.4897 - loss: 0.6900 - val_auc_1000: 0.4839 - val_loss: 0.6937\n", "Epoch 6/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.4883 - loss: 0.6903 - val_auc_1000: 0.4769 - val_loss: 0.6939\n", "Epoch 7/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5078 - loss: 0.6901 - val_auc_1000: 0.4845 - val_loss: 0.6938\n", "Epoch 8/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.4940 - loss: 0.6902 - val_auc_1000: 0.4845 - val_loss: 0.6938\n", "Epoch 9/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5008 - loss: 0.6901 - val_auc_1000: 0.4623 - val_loss: 0.6937\n", "Epoch 10/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.4956 - loss: 0.6902 - val_auc_1000: 0.4664 - val_loss: 0.6938\n", "Epoch 11/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.4948 - loss: 0.6902 - val_auc_1000: 0.4843 - val_loss: 0.6933\n", "Epoch 12/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.4950 - loss: 0.6908 - val_auc_1000: 0.4768 - val_loss: 0.6940\n", "Epoch 13/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.4860 - loss: 0.6899 - val_auc_1000: 0.4729 - val_loss: 0.6950\n", "Epoch 14/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5019 - loss: 0.6903 - val_auc_1000: 0.4996 - val_loss: 0.6936\n", "Epoch 15/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.4833 - loss: 0.6900 - val_auc_1000: 0.5094 - val_loss: 0.6932\n", "Epoch 16/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5107 - loss: 0.6903 - val_auc_1000: 0.5059 - val_loss: 0.6933\n", "Epoch 17/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5102 - loss: 0.6900 - val_auc_1000: 0.5239 - val_loss: 0.6932\n", "Epoch 18/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5067 - loss: 0.6900 - val_auc_1000: 0.5174 - val_loss: 0.6932\n", "Epoch 19/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.4991 - loss: 0.6900 - val_auc_1000: 0.5140 - val_loss: 0.6931\n", "Epoch 20/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5028 - loss: 0.6899 - val_auc_1000: 0.4933 - val_loss: 0.6931\n", "Epoch 21/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - 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auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6940\n", "Epoch 102/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6940\n", "Epoch 103/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6940\n", "Epoch 104/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6940\n", "Epoch 105/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6940\n", "Epoch 106/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6940\n", "Epoch 107/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6940\n", "Epoch 108/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6940\n", "Epoch 109/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6940\n", "Epoch 110/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6940\n", "Epoch 111/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6940\n", "Epoch 112/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6940\n", "Epoch 113/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6940\n", "Epoch 114/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6940\n", "Epoch 115/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6939\n", "Epoch 116/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6940\n", "Epoch 117/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6940\n", "Epoch 118/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6939\n", "Epoch 119/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6939\n", "Epoch 120/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6939\n", "Epoch 121/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6939\n", "Epoch 122/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6939\n", "Epoch 123/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6939\n", "Epoch 124/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 9ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6939\n", "Epoch 125/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6939\n", "Epoch 126/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6939\n", "Epoch 127/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6939\n", "Epoch 128/128\n", "\u001B[1m17/17\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step - auc_1000: 0.5000 - loss: 0.6899 - val_auc_1000: 0.5000 - val_loss: 0.6939\n", "\u001B[1m8/8\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 4ms/step - auc_1000: 0.5000 - loss: 0.6965 \n" ] } ], "source": [ "best_params = trial.params\n", "fs, t = get_sequences(df, 'clabel', best_params['sequence_length'])\n", "trs, tss, trt, tst = get_tt(fs, t)\n", "final_model = create_lstm_model(input_shape=(trs.shape[1], trs.shape[2]),\n", " lstm_units_1=best_params['lstm_units_1'],\n", " lstm_units_2=best_params['lstm_units_2'],\n", " dense_units=best_params['dense_units'],\n", " eta=best_params['eta'])\n", "\n", "# Train final model\n", "final_model.fit(trs, trt,\n", " validation_data=(tss, tst),\n", " epochs=128,\n", " batch_size=128,\n", " verbose=1)\n", "\n", "# Predict and compute AUC\n", "val_loss, auc = final_model.evaluate(tss, tst, verbose=1)\n", "print(f'AUC {auc:.6f}', file=ofile)\n", "ofile.close()" ] }, { "cell_type": "code", "execution_count": null, "id": "870bb8dc", "metadata": { "papermill": { "duration": 0.124658, "end_time": "2024-05-31T22:34:34.378137", "exception": false, "start_time": "2024-05-31T22:34:34.253479", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "3a319e3d", "metadata": { "papermill": { "duration": 0.12381, "end_time": "2024-05-31T22:34:34.625077", "exception": false, "start_time": "2024-05-31T22:34:34.501267", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] } ], "metadata": { "kaggle": { "accelerator": "gpu", "dataSources": [ { "datasetId": 5124665, "sourceId": 8570662, "sourceType": "datasetVersion" } ], "dockerImageVersionId": 30716, "isGpuEnabled": true, "isInternetEnabled": true, "language": "python", "sourceType": "notebook" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.13" }, "papermill": { "default_parameters": {}, "duration": 6503.803871, "end_time": "2024-05-31T22:34:38.185905", "environment_variables": {}, "exception": null, "input_path": "__notebook__.ipynb", "output_path": "__notebook__.ipynb", "parameters": {}, "start_time": "2024-05-31T20:46:14.382034", "version": "2.5.0" } }, "nbformat": 4, "nbformat_minor": 5 }