{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":81933,"databundleVersionId":9643020,"sourceType":"competition"}],"dockerImageVersionId":30786,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np\nimport polars as pl\nimport pandas as pd\nimport pandas as pd\nimport numpy as np\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import StandardScaler\nimport matplotlib.pyplot as plt\n\nfrom sklearn.base import clone\nfrom copy import deepcopy\nimport optuna\nfrom scipy.optimize import minimize\nimport os\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport re\nfrom colorama import Fore, Style\n\nfrom tqdm import tqdm\nfrom IPython.display import clear_output\nfrom concurrent.futures import ThreadPoolExecutor\n\nimport warnings\nwarnings.filterwarnings('ignore')\npd.options.display.max_columns = None\n\nimport lightgbm as lgb\nfrom catboost import CatBoostRegressor, CatBoostClassifier\nfrom xgboost import XGBRegressor\nfrom sklearn.ensemble import VotingRegressor\nfrom sklearn.model_selection import *\nfrom sklearn.metrics import *\n\nSEED = 42\nn_splits = 5\n\nimport numpy as np\nimport pandas as pd\nimport os\nimport re\nfrom sklearn.base import clone\nfrom sklearn.metrics import cohen_kappa_score\nfrom sklearn.model_selection import StratifiedKFold\nfrom scipy.optimize import minimize\nfrom concurrent.futures import ThreadPoolExecutor\nfrom tqdm import tqdm\n\nfrom colorama import Fore, Style\nfrom IPython.display import clear_output\nimport warnings\nfrom lightgbm import LGBMRegressor\nfrom xgboost import XGBRegressor\nfrom catboost import CatBoostRegressor\nfrom sklearn.ensemble import VotingRegressor\nwarnings.filterwarnings('ignore')\npd.options.display.max_columns = None\n\nSEED = 42\nn_splits = 5\nMAX_TS_ENC_LENGTH = 9000\n\nimport numpy as np\nimport pandas as pd\nimport os\nfrom concurrent.futures import ThreadPoolExecutor\nfrom tqdm import tqdm\nfrom sklearn.preprocessing import StandardScaler\nimport matplotlib.pyplot as plt\nfrom keras.models import Model\nfrom keras.layers import Input, Dense\nfrom keras.optimizers import Adam\n\n# Function to process files and load data (same as your original one)\ndef process_file(filename, dirname):\n df = pd.read_parquet(os.path.join(dirname, filename, 'part-0.parquet'))\n df.drop('step', axis=1, inplace=True)\n return df.describe().values.reshape(-1), filename.split('=')[1]\n\n# Function to process files and load data (same as your original one)\ndef process_raw_file(filename, dirname, cols):\n df = pd.read_parquet(os.path.join(dirname, filename, 'part-0.parquet'))\n df.drop('step', axis=1, inplace=True)\n return df[cols].iloc[:MAX_TS_ENC_LENGTH, :]\n\ndef flatten(xss):\n return [x for xs in xss for x in xs]\n\n\ndef load_time_series(dirname) -> pd.DataFrame:\n ids = os.listdir(dirname)\n \n with ThreadPoolExecutor() as executor:\n results = list(tqdm(executor.map(lambda fname: process_file(fname, dirname), ids), total=len(ids)))\n \n stats, indexes = zip(*results)\n \n df = pd.DataFrame(stats, columns=[f\"Stat_{i}\" for i in range(len(stats[0]))])\n df['id'] = indexes\n return df\n\ndef load_raw_time_series(dirname, cols) -> pd.DataFrame:\n ids = os.listdir(dirname)\n cols_df = [[col+f'_{i}' for i in range(MAX_TS_ENC_LENGTH)] for col in cols]\n cols_df = flatten(cols_df)\n res_df = []\n for i in tqdm(ids):\n vals = process_raw_file(i, dirname, cols)\n res_df.append(vals.T.values.flatten())\n df = pd.DataFrame(res_df, columns = cols_df)\n df['id'] = ids\n return df\n\n# Build the autoencoder model\ndef build_autoencoder(input_dim, encoding_dim):\n input_layer = Input(shape=(input_dim,))\n \n # Encoder: compressing the input\n encoded = Dense(encoding_dim, activation='relu')(input_layer)\n \n # Decoder: reconstructing the input\n decoded = Dense(input_dim, activation='sigmoid')(encoded)\n \n # Autoencoder model\n autoencoder = Model(inputs=input_layer, outputs=decoded)\n \n # Encoder model (for getting the compressed representation)\n encoder = Model(inputs=input_layer, outputs=encoded)\n \n autoencoder.compile(optimizer=Adam(), loss='mse')\n \n return autoencoder, encoder\n\n# Function to perform dimensionality reduction using an autoencoder\ndef perform_autoencoder(df, encoding_dim=50, epochs=50, batch_size=32):\n \"\"\"\n Perform dimensionality reduction using an Autoencoder.\n\n Parameters:\n df (pd.DataFrame): The input DataFrame with numerical features.\n encoding_dim (int): The dimension of the encoded space.\n epochs (int): Number of epochs to train the autoencoder.\n batch_size (int): Size of the batches for training.\n\n Returns:\n pd.DataFrame: DataFrame containing the reduced-dimensional representation (encoding).\n \"\"\"\n \n # Step 1: Standardize the data\n scaler = StandardScaler()\n df_scaled = scaler.fit_transform(df)\n \n # Step 2: Build the autoencoder\n input_dim = df_scaled.shape[1]\n autoencoder, encoder = build_autoencoder(input_dim, encoding_dim)\n \n # Step 3: Train the autoencoder\n autoencoder.fit(df_scaled, df_scaled, epochs=epochs, batch_size=batch_size, shuffle=True, verbose=1)\n \n # Step 4: Get the encoded (reduced) representation\n encoded_data = encoder.predict(df_scaled)\n \n # Step 5: Create a DataFrame for the encoded features\n df_encoded = pd.DataFrame(encoded_data, columns=[f'Enc_{i+1}' for i in range(encoded_data.shape[1])])\n \n return df_encoded\n\n\n# Load the data\ntrain = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/train.csv')\ntest = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/test.csv')\nsample = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/sample_submission.csv')\n\ntrain_ts = load_time_series(\"/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet\")\ntest_ts = load_time_series(\"/kaggle/input/child-mind-institute-problematic-internet-use/series_test.parquet\")\n\n# # Drop 'id' column for training\ndf_train = train_ts.drop('id', axis=1)\ndf_test = test_ts.drop('id', axis=1)\n\n# # Perform autoencoder dimensionality reduction\ntrain_ts_encoded = perform_autoencoder(df_train, encoding_dim=50, epochs=100, batch_size=32)\ntest_ts_encoded = perform_autoencoder(df_test, encoding_dim=50, epochs=100, batch_size=32)\n\ncols = ['X', 'Y', 'Z', 'enmo', 'anglez']\ntrain_ts_raw = load_raw_time_series(\"/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet\", cols)\ntest_ts_raw = load_raw_time_series(\"/kaggle/input/child-mind-institute-problematic-internet-use/series_test.parquet\", cols)\n\ncols_df = {col : [col+f'_{i}' for i in range(MAX_TS_ENC_LENGTH)] for col in cols}\ntrain_ts_encoded_new = perform_autoencoder(train_ts_raw.drop(['id'],axis = 1).fillna(0), encoding_dim=30, epochs=100, batch_size=32)\ntest_ts_encoded_new = perform_autoencoder(test_ts_raw.drop(['id'],axis = 1).fillna(0), encoding_dim=30, epochs=100, batch_size=32)\ntrain_ts_encoded_new['id'] = train_ts_raw['id']\ntest_ts_encoded_new['id'] = test_ts_raw['id']\ntrain_ts_encoded['id'] = train_ts['id']\ntest_ts_encoded['id'] = test_ts['id']\ntrain = train.merge(train_ts_encoded, on = 'id', how = 'left')\ntest = test.merge(test_ts_encoded, on = 'id', how = 'left')\ntrain_ts_encoded_new['id'] = train_ts_encoded_new['id'].apply(lambda x: x.split('=')[1])\ntest_ts_encoded_new['id'] = test_ts_encoded_new['id'].apply(lambda x: x.split('=')[1])\ntrain = train.merge(train_ts_encoded_new, on = 'id', how = 'left')\ntest = test.merge(test_ts_encoded_new, on = 'id', how = 'left')\n\ncat_c = ['Basic_Demos-Enroll_Season', 'CGAS-Season', 'Physical-Season', 'Fitness_Endurance-Season', \n 'FGC-Season', 'BIA-Season', 'PAQ_A-Season', 'PAQ_C-Season', 'SDS-Season', 'PreInt_EduHx-Season']\n\ndef update(df):\n for c in cat_c: \n df[c] = df[c].fillna('Missing')\n df[c] = df[c].astype('category')\n return df\n \ntrain = update(train)\ntest = update(test)\n\ndef create_mapping(column, dataset):\n unique_values = dataset[column].unique()\n return {value: idx for idx, value in enumerate(unique_values)}\n\n\"\"\"This Mapping Works Fine For me I also Check Each Values in Train and test Using Logic. There no Data Lekage.\"\"\"\n\nfor col in cat_c:\n mapping_train = create_mapping(col, train)\n mapping_test = create_mapping(col, test)\n \n train[col] = train[col].replace(mapping_train).astype(int)\n test[col] = test[col].replace(mapping_test).astype(int)\n \ntrain = train[test.columns.tolist() + ['sii']]\ntrain_na_target = train[train['sii'].isna()]\ntrain = train[~train['sii'].isna()]\ntrain_ids = train.pop('id')\ntest_ids = test.pop('id')","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-10-13T09:35:59.227368Z","iopub.execute_input":"2024-10-13T09:35:59.227929Z","iopub.status.idle":"2024-10-13T09:41:21.201206Z","shell.execute_reply.started":"2024-10-13T09:35:59.227871Z","shell.execute_reply":"2024-10-13T09:41:21.199728Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","text":"100%|██████████| 996/996 [01:35<00:00, 10.40it/s]\n100%|██████████| 2/2 [00:00<00:00, 8.51it/s]","output_type":"stream"},{"name":"stdout","text":"Epoch 1/100\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 1.2265 \nEpoch 2/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 1.0316 \nEpoch 3/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.8480 \nEpoch 4/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.8969 \nEpoch 5/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.8068 \nEpoch 6/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.7663 \nEpoch 7/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.7365 \nEpoch 8/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.7683 \nEpoch 9/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6616 \nEpoch 10/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6980 \nEpoch 11/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.7038 \nEpoch 12/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.7260 \nEpoch 13/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6966 \nEpoch 14/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6815 \nEpoch 15/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6947 \nEpoch 16/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6946 \nEpoch 17/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6906 \nEpoch 18/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6751 \nEpoch 19/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6527 \nEpoch 20/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6913 \nEpoch 21/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6775 \nEpoch 22/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6474 \nEpoch 23/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6903 \nEpoch 24/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6437 \nEpoch 25/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6818\nEpoch 26/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.7177 \nEpoch 27/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6500 \nEpoch 28/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.7123\nEpoch 29/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6968\nEpoch 30/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6579\nEpoch 31/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.6943\nEpoch 32/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6442 \nEpoch 33/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6609 \nEpoch 34/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6513 \nEpoch 35/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6892 \nEpoch 36/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6920 \nEpoch 37/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6795 \nEpoch 38/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6703 \nEpoch 39/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6405 \nEpoch 40/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6354 \nEpoch 41/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6405 \nEpoch 42/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6656 \nEpoch 43/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6630\nEpoch 44/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6351 \nEpoch 45/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6461 \nEpoch 46/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6394 \nEpoch 47/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6777 \nEpoch 48/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6595 \nEpoch 49/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6456 \nEpoch 50/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6579 \nEpoch 51/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6729 \nEpoch 52/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6238 \nEpoch 53/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6811 \nEpoch 54/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6747 \nEpoch 55/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6405 \nEpoch 56/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6697 \nEpoch 57/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6261 \nEpoch 58/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6575 \nEpoch 59/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6276 \nEpoch 60/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6319 \nEpoch 61/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6520 \nEpoch 62/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6041 \nEpoch 63/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6843 \nEpoch 64/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6283 \nEpoch 65/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6956 \nEpoch 66/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6338 \nEpoch 67/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6160 \nEpoch 68/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.5917 \nEpoch 69/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6622 \nEpoch 70/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6641 \nEpoch 71/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6487 \nEpoch 72/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6437 \nEpoch 73/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6074 \nEpoch 74/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6451 \nEpoch 75/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6369 \nEpoch 76/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6189 \nEpoch 77/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.5587 \nEpoch 78/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6140 \nEpoch 79/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.5697 \nEpoch 80/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6804 \nEpoch 81/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6508 \nEpoch 82/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6267 \nEpoch 83/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6796 \nEpoch 84/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6818 \nEpoch 85/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6525 \nEpoch 86/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6215 \nEpoch 87/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.5971 \nEpoch 88/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6239 \nEpoch 89/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6823 \nEpoch 90/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6124 \nEpoch 91/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6762 \nEpoch 92/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6591 \nEpoch 93/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.5989 \nEpoch 94/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6343 \nEpoch 95/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6231 \nEpoch 96/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6292 \nEpoch 97/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6140 \nEpoch 98/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6143 \nEpoch 99/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6175 \nEpoch 100/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6080 \n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\nEpoch 1/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 774ms/step - loss: 1.1211\nEpoch 2/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 1.1025\nEpoch 3/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 1.0848\nEpoch 4/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 1.0675\nEpoch 5/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 1.0506\nEpoch 6/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 1.0343\nEpoch 7/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 1.0184\nEpoch 8/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.0029\nEpoch 9/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.9875\nEpoch 10/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.9722\nEpoch 11/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.9569\nEpoch 12/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 0.9419\nEpoch 13/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.9269\nEpoch 14/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.9117\nEpoch 15/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.8963\nEpoch 16/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.8807\nEpoch 17/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.8649\nEpoch 18/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.8489\nEpoch 19/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.8328\nEpoch 20/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.8166\nEpoch 21/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.8004\nEpoch 22/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.7842\nEpoch 23/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.7681\nEpoch 24/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.7521\nEpoch 25/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.7362\nEpoch 26/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.7205\nEpoch 27/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.7050\nEpoch 28/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.6898\nEpoch 29/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 0.6750\nEpoch 30/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.6605\nEpoch 31/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.6464\nEpoch 32/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.6328\nEpoch 33/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.6197\nEpoch 34/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 0.6071\nEpoch 35/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.5951\nEpoch 36/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.5836\nEpoch 37/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.5726\nEpoch 38/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 0.5623\nEpoch 39/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.5524\nEpoch 40/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.5432\nEpoch 41/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.5344\nEpoch 42/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 0.5262\nEpoch 43/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 0.5185\nEpoch 44/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.5113\nEpoch 45/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 0.5045\nEpoch 46/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.4981\nEpoch 47/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4922\nEpoch 48/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4866\nEpoch 49/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 0.4815\nEpoch 50/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4766\nEpoch 51/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4722\nEpoch 52/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4681\nEpoch 53/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4643\nEpoch 54/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.4609\nEpoch 55/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4577\nEpoch 56/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.4549\nEpoch 57/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.4523\nEpoch 58/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.4500\nEpoch 59/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4479\nEpoch 60/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.4461\nEpoch 61/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4444\nEpoch 62/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.4430\nEpoch 63/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4417\nEpoch 64/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4405\nEpoch 65/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.4395\nEpoch 66/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.4386\nEpoch 67/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 0.4378\nEpoch 68/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.4371\nEpoch 69/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4364\nEpoch 70/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.4358\nEpoch 71/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.4353\nEpoch 72/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.4348\nEpoch 73/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4344\nEpoch 74/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4340\nEpoch 75/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.4337\nEpoch 76/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.4334\nEpoch 77/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 0.4331\nEpoch 78/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.4328\nEpoch 79/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4326\nEpoch 80/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4323\nEpoch 81/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.4321\nEpoch 82/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.4319\nEpoch 83/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4318\nEpoch 84/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4316\nEpoch 85/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4314\nEpoch 86/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.4313\nEpoch 87/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4312\nEpoch 88/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4310\nEpoch 89/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4309\nEpoch 90/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4308\nEpoch 91/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4307\nEpoch 92/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 0.4306\nEpoch 93/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4305\nEpoch 94/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4304\nEpoch 95/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4304\nEpoch 96/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4303\nEpoch 97/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4302\nEpoch 98/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.4301\nEpoch 99/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.4301\nEpoch 100/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.4300\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 996/996 [00:44<00:00, 22.22it/s]\n100%|██████████| 2/2 [00:00<00:00, 26.70it/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch 1/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 28ms/step - loss: 1.0698\nEpoch 2/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 1.0573\nEpoch 3/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.9309\nEpoch 4/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step - loss: 0.8223\nEpoch 5/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - loss: 0.9488\nEpoch 6/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step - loss: 0.8955\nEpoch 7/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.9456\nEpoch 8/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.9009\nEpoch 9/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.9604\nEpoch 10/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.8774\nEpoch 11/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.7084\nEpoch 12/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.7624\nEpoch 13/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.8416\nEpoch 14/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.9739\nEpoch 15/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.8404\nEpoch 16/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.7886\nEpoch 17/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.9592\nEpoch 18/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.7936\nEpoch 19/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.7829\nEpoch 20/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.8222\nEpoch 21/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.9640\nEpoch 22/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.8015\nEpoch 23/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.7016\nEpoch 24/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.8319\nEpoch 25/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.7645\nEpoch 26/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.7714\nEpoch 27/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.7013\nEpoch 28/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.8096\nEpoch 29/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.9536\nEpoch 30/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.7970\nEpoch 31/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 1.0318\nEpoch 32/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.8812\nEpoch 33/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.7950\nEpoch 34/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.7407\nEpoch 35/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.7622\nEpoch 36/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 1.0401\nEpoch 37/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.6860\nEpoch 38/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.8555\nEpoch 39/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - loss: 0.7699\nEpoch 40/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 1.0389\nEpoch 41/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.7354\nEpoch 42/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.9621\nEpoch 43/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.7351\nEpoch 44/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.7825\nEpoch 45/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.9041\nEpoch 46/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.7844\nEpoch 47/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.9663\nEpoch 48/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.8728\nEpoch 49/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.8970\nEpoch 50/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.7621\nEpoch 51/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.9859\nEpoch 52/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.8163\nEpoch 53/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.6831\nEpoch 54/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.5939\nEpoch 55/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.8855\nEpoch 56/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.7257\nEpoch 57/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.9719\nEpoch 58/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.7805\nEpoch 59/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.9979\nEpoch 60/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.7798\nEpoch 61/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.6580\nEpoch 62/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.8792\nEpoch 63/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.7942\nEpoch 64/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.6214\nEpoch 65/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.8854\nEpoch 66/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.8309\nEpoch 67/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.9387\nEpoch 68/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.8223\nEpoch 69/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.7972\nEpoch 70/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 1.0457\nEpoch 71/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.7244\nEpoch 72/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step - loss: 1.0212\nEpoch 73/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 0.7713\nEpoch 74/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.8717\nEpoch 75/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 1.1472\nEpoch 76/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.9339\nEpoch 77/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.9334\nEpoch 78/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.6723\nEpoch 79/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.7886\nEpoch 80/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.8939\nEpoch 81/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.7830\nEpoch 82/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.8285\nEpoch 83/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.8198\nEpoch 84/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.8062\nEpoch 85/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.8381\nEpoch 86/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.6874\nEpoch 87/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.9708\nEpoch 88/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.8211\nEpoch 89/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.9039\nEpoch 90/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.9651\nEpoch 91/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 1.0186\nEpoch 92/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.8851\nEpoch 93/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 1.0180\nEpoch 94/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.6191\nEpoch 95/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 0.9194\nEpoch 96/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 1.0492\nEpoch 97/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.9822\nEpoch 98/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 1.0475\nEpoch 99/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.8186\nEpoch 100/100\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.9606\n\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\nEpoch 1/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 847ms/step - loss: 1.2498\nEpoch 2/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.2854\nEpoch 3/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.0955\nEpoch 4/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.9488\nEpoch 5/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.7824\nEpoch 6/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.6519\nEpoch 7/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.5712\nEpoch 8/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.5295\nEpoch 9/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.5111\nEpoch 10/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.5040\nEpoch 11/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.5015\nEpoch 12/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.5006\nEpoch 13/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.5003\nEpoch 14/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.5002\nEpoch 15/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.5001\nEpoch 16/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.5000\nEpoch 17/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.5000\nEpoch 18/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.5000\nEpoch 19/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.5000\nEpoch 20/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.5000\nEpoch 21/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.4999\nEpoch 22/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.4999\nEpoch 23/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 24/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.4999\nEpoch 25/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.4999\nEpoch 26/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.4999\nEpoch 27/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.4999\nEpoch 28/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.4999\nEpoch 29/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 30/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.4999\nEpoch 31/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 32/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.4999\nEpoch 33/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.4999\nEpoch 34/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.4999\nEpoch 35/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 36/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.4999\nEpoch 37/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 38/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.4999\nEpoch 39/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.4999\nEpoch 40/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.4999\nEpoch 41/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.4999\nEpoch 42/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.4999\nEpoch 43/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.4999\nEpoch 44/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.4999\nEpoch 45/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 46/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.4999\nEpoch 47/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.4999\nEpoch 48/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 49/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.4999\nEpoch 50/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.4999\nEpoch 51/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 52/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.4999\nEpoch 53/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 54/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.4999\nEpoch 55/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 56/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.4999\nEpoch 57/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.4999\nEpoch 58/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.4999\nEpoch 59/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 60/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 61/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.4999\nEpoch 62/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 63/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.4999\nEpoch 64/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 65/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.4999\nEpoch 66/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.4999\nEpoch 67/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 68/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.4999\nEpoch 69/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 70/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 71/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.4999\nEpoch 72/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.4999\nEpoch 73/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.4999\nEpoch 74/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 75/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.4999\nEpoch 76/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.4999\nEpoch 77/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 78/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.4999\nEpoch 79/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.4999\nEpoch 80/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.4999\nEpoch 81/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 82/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.4999\nEpoch 83/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 84/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.4999\nEpoch 85/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.4999\nEpoch 86/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 87/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.4999\nEpoch 88/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.4999\nEpoch 89/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 90/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 91/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.4999\nEpoch 92/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.4999\nEpoch 93/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.4999\nEpoch 94/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.4999\nEpoch 95/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.4999\nEpoch 96/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 97/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 98/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4999\nEpoch 99/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.4999\nEpoch 100/100\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.4999\n\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n","output_type":"stream"}]},{"cell_type":"code","source":"train = train[~train['Enc_3_x'].isna()]\ntest = test[~test['Enc_3_x'].isna()]","metadata":{"execution":{"iopub.status.busy":"2024-10-13T09:43:52.250152Z","iopub.execute_input":"2024-10-13T09:43:52.250689Z","iopub.status.idle":"2024-10-13T09:43:52.261663Z","shell.execute_reply.started":"2024-10-13T09:43:52.250645Z","shell.execute_reply":"2024-10-13T09:43:52.260264Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"def quadratic_weighted_kappa(y_true, y_pred):\n return cohen_kappa_score(y_true, y_pred, weights='quadratic')\n\ndef threshold_Rounder(oof_non_rounded, thresholds):\n return np.where(oof_non_rounded < thresholds[0], 0,\n np.where(oof_non_rounded < thresholds[1], 1,\n np.where(oof_non_rounded < thresholds[2], 2, 3)))\n\ndef evaluate_predictions(thresholds, y_true, oof_non_rounded):\n rounded_p = threshold_Rounder(oof_non_rounded, thresholds)\n return -quadratic_weighted_kappa(y_true, rounded_p)\n\ndef TrainML(model_class, test_data, is_test):\n X = train.drop(['sii'], axis=1)\n y = train['sii']\n\n SKF = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=SEED)\n \n train_S = []\n test_S = []\n \n oof_non_rounded = np.zeros(len(y), dtype=float) \n oof_rounded = np.zeros(len(y), dtype=int) \n test_preds = np.zeros((len(test_data), n_splits))\n\n for fold, (train_idx, test_idx) in enumerate(tqdm(SKF.split(X, y), desc=\"Training Folds\", total=n_splits)):\n X_train, X_val = X.iloc[train_idx], X.iloc[test_idx]\n y_train, y_val = y.iloc[train_idx], y.iloc[test_idx]\n\n model = clone(model_class)\n model.fit(X_train, y_train)\n\n y_train_pred = model.predict(X_train)\n y_val_pred = model.predict(X_val)\n\n oof_non_rounded[test_idx] = y_val_pred\n y_val_pred_rounded = y_val_pred.round(0).astype(int)\n oof_rounded[test_idx] = y_val_pred_rounded\n\n train_kappa = quadratic_weighted_kappa(y_train, y_train_pred.round(0).astype(int))\n val_kappa = quadratic_weighted_kappa(y_val, y_val_pred_rounded)\n\n train_S.append(train_kappa)\n test_S.append(val_kappa)\n \n test_preds[:, fold] = model.predict(test_data)\n \n print(f\"Fold {fold+1} - Train QWK: {train_kappa:.4f}, Validation QWK: {val_kappa:.4f}\")\n\n print(f\"Mean Train QWK --> {np.mean(train_S):.4f}\")\n print(f\"Mean Validation QWK ---> {np.mean(test_S):.4f}\")\n\n KappaOPtimizer = minimize(evaluate_predictions,\n x0=[0.5, 1.5, 2.5], args=(y, oof_non_rounded), \n method='Nelder-Mead')\n assert KappaOPtimizer.success, \"Optimization did not converge.\"\n \n oof_tuned = threshold_Rounder(oof_non_rounded, KappaOPtimizer.x)\n tKappa = quadratic_weighted_kappa(y, oof_tuned)\n\n print(f\"----> || Optimized QWK SCORE :: {Fore.CYAN}{Style.BRIGHT} {tKappa:.3f}{Style.RESET_ALL}\")\n\n tpm = test_preds.mean(axis=1)\n tpTuned = threshold_Rounder(tpm, KappaOPtimizer.x)\n \n# submission = pd.DataFrame({\n# 'id': sample['id'],\n# 'sii': tpTuned\n# })\n if is_test == True:\n return np.mean(test_S)\n else:\n return None, model\n\n\n \nparams_tuned = {'learning_rate_lg': 0.11965123195383306,\n 'max_depth_lg': 6,\n 'num_leaves_lg': 456,\n 'min_data_in_leaf_lg': 6,\n 'feature_fraction_lg': 0.7533533832022282,\n 'bagging_fraction_lg': 0.6359114604065055,\n 'bagging_freq_lg': 561,\n 'lambda_l1': 0.1957821997688816,\n 'lambda_l2': 0.1984726678268079,\n 'learning_rate_xg': 0.029307319276363673,\n 'max_depth_xg': 5,\n 'n_estimators_xg': 352,\n 'subsample_xg': 0.4266374315221965,\n 'colsample_bytree_xg': 0.9604013150736684,\n 'learning_rate_cb': 0.1731509674575017,\n 'depth_cb': 3,\n 'iterations_cb': 230,\n 'weight1': 0.08053337530239457,\n 'weight2': 0.894803379646874} \n# Model parameters for LightGBM\nParams = {\n 'learning_rate': params_tuned['learning_rate_lg'],\n 'max_depth': params_tuned['max_depth_lg'],\n 'num_leaves': params_tuned['num_leaves_lg'],\n 'min_data_in_leaf': params_tuned['min_data_in_leaf_lg'],\n 'feature_fraction': params_tuned['feature_fraction_lg'],\n 'bagging_fraction': params_tuned['bagging_fraction_lg'],\n 'bagging_freq': params_tuned['bagging_freq_lg'],\n 'lambda_l1': params_tuned['lambda_l1'], # Increased from 6.59\n 'lambda_l2': params_tuned['lambda_l2'] # Increased from 2.68e-06\n}\n# XGBoost parameters\nXGB_Params = {\n 'learning_rate': params_tuned['learning_rate_xg'],\n 'max_depth': params_tuned['max_depth_xg'],\n 'n_estimators': params_tuned['n_estimators_xg'],\n 'subsample': params_tuned['subsample_xg'],\n 'colsample_bytree': params_tuned['colsample_bytree_xg'],\n 'reg_alpha': 1, # Increased from 0.1\n 'reg_lambda': 5, # Increased from 1\n 'random_state': SEED\n}\nCatBoost_Params = {\n 'learning_rate': params_tuned['learning_rate_cb'],\n 'depth': params_tuned['depth_cb'],\n 'iterations': params_tuned['iterations_cb'],\n 'random_seed': SEED,\n 'cat_features': cat_c,\n 'verbose': 0,\n 'l2_leaf_reg': 1 # Increase this value\n}\n\nweights = [params_tuned['weight1'],\n params_tuned['weight2'],\n 1 - params_tuned['weight2'] - params_tuned['weight1']]\n\n# Create model instances\nLight = LGBMRegressor(**Params, random_state=SEED, verbose=-1, n_estimators=300)\nXGB_Model = XGBRegressor(**XGB_Params)\nCatBoost_Model = CatBoostRegressor(**CatBoost_Params)\n\n# Combine models using Voting Regressor\nvoting_model = VotingRegressor(estimators=[\n ('lightgbm', Light),\n ('xgboost', XGB_Model),\n ('catboost', CatBoost_Model)\n],weights=weights)\n\n# Train the ensemble model\nSubmission_new,voting_model = TrainML(voting_model, test, False)\n","metadata":{"execution":{"iopub.status.busy":"2024-10-13T10:15:53.325006Z","iopub.execute_input":"2024-10-13T10:15:53.325975Z","iopub.status.idle":"2024-10-13T10:16:40.726626Z","shell.execute_reply.started":"2024-10-13T10:15:53.32592Z","shell.execute_reply":"2024-10-13T10:16:40.725428Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stderr","text":"Training Folds: 20%|██ | 1/5 [00:09<00:36, 9.25s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 1 - Train QWK: 0.9703, Validation QWK: 0.3109\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 40%|████ | 2/5 [00:18<00:27, 9.28s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 2 - Train QWK: 0.9582, Validation QWK: 0.3200\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 60%|██████ | 3/5 [00:27<00:18, 9.25s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 3 - Train QWK: 0.9667, Validation QWK: 0.3229\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 80%|████████ | 4/5 [00:37<00:09, 9.56s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 4 - Train QWK: 0.9689, Validation QWK: 0.3544\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 100%|██████████| 5/5 [00:47<00:00, 9.44s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 5 - Train QWK: 0.9678, Validation QWK: 0.3472\nMean Train QWK --> 0.9664\nMean Validation QWK ---> 0.3311\n----> || Optimized QWK SCORE :: \u001b[36m\u001b[1m 0.334\u001b[0m\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"}]},{"cell_type":"code","source":"import optuna\nimport warnings \nimport copy\nwarnings.simplefilter('ignore')\nmodels = []\n\ndef objective(trial):\n \n Params = {\n 'learning_rate': trial.suggest_float('learning_rate_lg', 1e-3, 0.2), \n 'max_depth': trial.suggest_int('max_depth_lg', 1, 15),\n 'num_leaves': trial.suggest_int('num_leaves_lg', 200, 1000),\n 'min_data_in_leaf': trial.suggest_int('min_data_in_leaf_lg', 3, 300),\n 'feature_fraction': trial.suggest_float('feature_fraction_lg', 1e-3, 1),\n 'bagging_fraction': trial.suggest_float('bagging_fraction_lg', 1e-3, 1),\n 'bagging_freq': trial.suggest_int('bagging_freq_lg', 300, 1000),\n 'lambda_l1': trial.suggest_float('lambda_l1',1e-3 , 1), # Increased from 6.59\n 'lambda_l2': trial.suggest_float('lambda_l2', 1e-3, 1) # Increased from 2.68e-06\n }\n # XGBoost parameters\n XGB_Params = {\n 'learning_rate': trial.suggest_float('learning_rate_xg', 1e-3, 0.2),\n 'max_depth': trial.suggest_int('max_depth_xg', 1, 7),\n 'n_estimators': trial.suggest_int('n_estimators_xg', 300, 500),\n 'subsample': trial.suggest_float('subsample_xg', 1e-3, 1),\n 'colsample_bytree': trial.suggest_float('colsample_bytree_xg', 1e-3, 1.0),\n 'reg_alpha': 1, # Increased from 0.1\n 'reg_lambda': 5, # Increased from 1\n 'random_state': SEED\n }\n\n\n CatBoost_Params = {\n 'learning_rate': trial.suggest_float('learning_rate_cb', 1e-3, 0.2),\n 'depth': trial.suggest_int('depth_cb', 1, 7),\n 'iterations': trial.suggest_int('iterations_cb', 200, 500),\n 'random_seed': SEED,\n 'cat_features': cat_c,\n 'verbose': 0,\n 'l2_leaf_reg': 1 # Increase this value\n }\n Light = LGBMRegressor(**Params, random_state=SEED, verbose=-1, n_estimators=300)\n XGB_Model = XGBRegressor(**XGB_Params)\n CatBoost_Model = CatBoostRegressor(**CatBoost_Params)\n weight1 = trial.suggest_float('weight1', 0, 1)\n weight2 = trial.suggest_float('weight2', 0, 1-weight1)\n voting_model = VotingRegressor(estimators=[\n ('lightgbm', Light),\n ('xgboost', XGB_Model),\n ('catboost', CatBoost_Model)\n], weights=[weight1, weight2, 1 - weight1 - weight2])\n \n qwa = TrainML(voting_model, test, True)\n return qwa\n\nprint('started')\n\nstudy = optuna.create_study(direction='maximize')\nstudy.optimize(objective, n_trials=200)\nnew_best_val = study.best_value","metadata":{"execution":{"iopub.status.busy":"2024-10-13T10:17:44.849762Z","iopub.execute_input":"2024-10-13T10:17:44.850911Z","iopub.status.idle":"2024-10-13T10:21:05.657144Z","shell.execute_reply.started":"2024-10-13T10:17:44.85086Z","shell.execute_reply":"2024-10-13T10:21:05.654855Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":8,"outputs":[{"name":"stderr","text":"[I 2024-10-13 10:17:44,864] A new study created in memory with name: no-name-5801609e-cc2d-4c75-beb0-3f67dfc39a58\n","output_type":"stream"},{"name":"stdout","text":"started\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 20%|██ | 1/5 [00:08<00:34, 8.69s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 1 - Train QWK: 0.5830, Validation QWK: 0.2316\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 40%|████ | 2/5 [00:18<00:28, 9.46s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 2 - Train QWK: 0.5549, Validation QWK: 0.3021\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 60%|██████ | 3/5 [00:27<00:18, 9.17s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 3 - Train QWK: 0.5542, Validation QWK: 0.2234\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 80%|████████ | 4/5 [00:36<00:09, 9.02s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 4 - Train QWK: 0.5412, Validation QWK: 0.3136\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 100%|██████████| 5/5 [00:45<00:00, 9.02s/it]\n[I 2024-10-13 10:18:30,183] Trial 0 finished with value: 0.2825980687225148 and parameters: {'learning_rate_lg': 0.18715545926271843, 'max_depth_lg': 4, 'num_leaves_lg': 408, 'min_data_in_leaf_lg': 281, 'feature_fraction_lg': 0.9819894804277364, 'bagging_fraction_lg': 0.5688861728881927, 'bagging_freq_lg': 667, 'lambda_l1': 0.09082507058269956, 'lambda_l2': 0.19261346838789953, 'learning_rate_xg': 0.16275095722410937, 'max_depth_xg': 1, 'n_estimators_xg': 341, 'subsample_xg': 0.9253039177353315, 'colsample_bytree_xg': 0.15660461397799322, 'learning_rate_cb': 0.1453830207161089, 'depth_cb': 6, 'iterations_cb': 266, 'weight1': 0.25030704510419466, 'weight2': 0.6919923728676243}. Best is trial 0 with value: 0.2825980687225148.\n","output_type":"stream"},{"name":"stdout","text":"Fold 5 - Train QWK: 0.5693, Validation QWK: 0.3424\nMean Train QWK --> 0.5605\nMean Validation QWK ---> 0.2826\n----> || Optimized QWK SCORE :: \u001b[36m\u001b[1m 0.286\u001b[0m\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 20%|██ | 1/5 [00:02<00:11, 2.97s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 1 - Train QWK: 0.8442, Validation QWK: 0.2274\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 40%|████ | 2/5 [00:05<00:08, 2.80s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 2 - Train QWK: 0.8366, Validation QWK: 0.3111\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 60%|██████ | 3/5 [00:08<00:05, 2.76s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 3 - Train QWK: 0.8282, Validation QWK: 0.2061\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 80%|████████ | 4/5 [00:10<00:02, 2.71s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 4 - Train QWK: 0.8051, Validation QWK: 0.3056\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 100%|██████████| 5/5 [00:13<00:00, 2.74s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 5 - Train QWK: 0.8150, Validation QWK: 0.2898\nMean Train QWK --> 0.8258\nMean Validation QWK ---> 0.2680\n","output_type":"stream"},{"name":"stderr","text":"\n[I 2024-10-13 10:18:44,120] Trial 1 finished with value: 0.2679808624048608 and parameters: {'learning_rate_lg': 0.1803976676545534, 'max_depth_lg': 2, 'num_leaves_lg': 362, 'min_data_in_leaf_lg': 118, 'feature_fraction_lg': 0.058223974409597944, 'bagging_fraction_lg': 0.7858151180161685, 'bagging_freq_lg': 737, 'lambda_l1': 0.21523351284987172, 'lambda_l2': 0.4049564824787811, 'learning_rate_xg': 0.09443646936087897, 'max_depth_xg': 3, 'n_estimators_xg': 344, 'subsample_xg': 0.9934147525952862, 'colsample_bytree_xg': 0.316969003722195, 'learning_rate_cb': 0.12954622562315232, 'depth_cb': 3, 'iterations_cb': 235, 'weight1': 0.3350182974224096, 'weight2': 0.1080572762029421}. Best is trial 0 with value: 0.2825980687225148.\n","output_type":"stream"},{"name":"stdout","text":"----> || Optimized QWK SCORE :: \u001b[36m\u001b[1m 0.342\u001b[0m\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 20%|██ | 1/5 [00:08<00:35, 8.89s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 1 - Train QWK: 0.7707, Validation QWK: 0.2437\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 40%|████ | 2/5 [00:17<00:26, 8.94s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 2 - Train QWK: 0.7572, Validation QWK: 0.3903\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 60%|██████ | 3/5 [00:26<00:17, 8.99s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 3 - Train QWK: 0.7577, Validation QWK: 0.2272\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 80%|████████ | 4/5 [00:35<00:08, 8.98s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 4 - Train QWK: 0.7336, Validation QWK: 0.3567\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 100%|██████████| 5/5 [00:44<00:00, 8.94s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 5 - Train QWK: 0.7595, Validation QWK: 0.3765\nMean Train QWK --> 0.7558\nMean Validation QWK ---> 0.3189\n","output_type":"stream"},{"name":"stderr","text":"\n[I 2024-10-13 10:19:29,106] Trial 2 finished with value: 0.31889516031845844 and parameters: {'learning_rate_lg': 0.10937495865892692, 'max_depth_lg': 2, 'num_leaves_lg': 612, 'min_data_in_leaf_lg': 71, 'feature_fraction_lg': 0.27486686699565316, 'bagging_fraction_lg': 0.8485544984300156, 'bagging_freq_lg': 790, 'lambda_l1': 0.8341166872888743, 'lambda_l2': 0.2951186587132663, 'learning_rate_xg': 0.05902675820840768, 'max_depth_xg': 6, 'n_estimators_xg': 318, 'subsample_xg': 0.10407317086403245, 'colsample_bytree_xg': 0.864868513009757, 'learning_rate_cb': 0.02785066958407647, 'depth_cb': 5, 'iterations_cb': 275, 'weight1': 0.10837243230960636, 'weight2': 0.3944094114004234}. Best is trial 2 with value: 0.31889516031845844.\n","output_type":"stream"},{"name":"stdout","text":"----> || Optimized QWK SCORE :: \u001b[36m\u001b[1m 0.418\u001b[0m\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 20%|██ | 1/5 [00:02<00:10, 2.50s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 1 - Train QWK: 0.0000, Validation QWK: 0.0000\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 40%|████ | 2/5 [00:04<00:07, 2.48s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 2 - Train QWK: 0.0000, Validation QWK: 0.0000\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 60%|██████ | 3/5 [00:07<00:05, 2.62s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 3 - Train QWK: 0.0000, Validation QWK: 0.0000\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 80%|████████ | 4/5 [00:10<00:02, 2.58s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 4 - Train QWK: 0.0000, Validation QWK: 0.0000\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 100%|██████████| 5/5 [00:12<00:00, 2.55s/it]\n[I 2024-10-13 10:19:42,028] Trial 3 finished with value: 0.0 and parameters: {'learning_rate_lg': 0.0157292963542282, 'max_depth_lg': 14, 'num_leaves_lg': 905, 'min_data_in_leaf_lg': 156, 'feature_fraction_lg': 0.4802787088328778, 'bagging_fraction_lg': 0.14008161724369853, 'bagging_freq_lg': 979, 'lambda_l1': 0.4219182084816106, 'lambda_l2': 0.5673219592330024, 'learning_rate_xg': 0.10095108684935201, 'max_depth_xg': 3, 'n_estimators_xg': 493, 'subsample_xg': 0.2600721841212218, 'colsample_bytree_xg': 0.024571522999598593, 'learning_rate_cb': 0.13509487756972505, 'depth_cb': 2, 'iterations_cb': 365, 'weight1': 0.9596346307635739, 'weight2': 0.018231819819758098}. Best is trial 2 with value: 0.31889516031845844.\n","output_type":"stream"},{"name":"stdout","text":"Fold 5 - Train QWK: 0.0000, Validation QWK: 0.0000\nMean Train QWK --> 0.0000\nMean Validation QWK ---> 0.0000\n----> || Optimized QWK SCORE :: \u001b[36m\u001b[1m 0.000\u001b[0m\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 20%|██ | 1/5 [00:05<00:20, 5.20s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 1 - Train QWK: 0.5848, Validation QWK: 0.2210\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 40%|████ | 2/5 [00:10<00:15, 5.19s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 2 - Train QWK: 0.5718, Validation QWK: 0.3446\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 60%|██████ | 3/5 [00:15<00:10, 5.21s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 3 - Train QWK: 0.5807, Validation QWK: 0.2944\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 80%|████████ | 4/5 [00:20<00:05, 5.22s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 4 - Train QWK: 0.5477, Validation QWK: 0.3290\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 100%|██████████| 5/5 [00:26<00:00, 5.30s/it]\n[I 2024-10-13 10:20:08,712] Trial 4 finished with value: 0.3204118782902718 and parameters: {'learning_rate_lg': 0.037045740345022934, 'max_depth_lg': 6, 'num_leaves_lg': 656, 'min_data_in_leaf_lg': 197, 'feature_fraction_lg': 0.005267269801120751, 'bagging_fraction_lg': 0.8782585600539453, 'bagging_freq_lg': 913, 'lambda_l1': 0.7983508034338355, 'lambda_l2': 0.45525176294133823, 'learning_rate_xg': 0.16685852912558555, 'max_depth_xg': 4, 'n_estimators_xg': 342, 'subsample_xg': 0.13316200062274067, 'colsample_bytree_xg': 0.47542785758799166, 'learning_rate_cb': 0.02662329579030383, 'depth_cb': 4, 'iterations_cb': 350, 'weight1': 0.3789526463949785, 'weight2': 0.06840089800554523}. Best is trial 4 with value: 0.3204118782902718.\n","output_type":"stream"},{"name":"stdout","text":"Fold 5 - Train QWK: 0.5284, Validation QWK: 0.4130\nMean Train QWK --> 0.5627\nMean Validation QWK ---> 0.3204\n----> || Optimized QWK SCORE :: \u001b[36m\u001b[1m 0.327\u001b[0m\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 20%|██ | 1/5 [00:05<00:22, 5.50s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 1 - Train QWK: 0.8763, Validation QWK: 0.2399\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 40%|████ | 2/5 [00:11<00:16, 5.56s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 2 - Train QWK: 0.8761, Validation QWK: 0.3242\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 60%|██████ | 3/5 [00:16<00:11, 5.53s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 3 - Train QWK: 0.8722, Validation QWK: 0.2260\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 80%|████████ | 4/5 [00:22<00:05, 5.53s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 4 - Train QWK: 0.8567, Validation QWK: 0.2966\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 100%|██████████| 5/5 [00:27<00:00, 5.53s/it]\n[I 2024-10-13 10:20:36,532] Trial 5 finished with value: 0.2832151517201103 and parameters: {'learning_rate_lg': 0.0721903486917751, 'max_depth_lg': 3, 'num_leaves_lg': 849, 'min_data_in_leaf_lg': 209, 'feature_fraction_lg': 0.46973948916847835, 'bagging_fraction_lg': 0.8019795967560815, 'bagging_freq_lg': 419, 'lambda_l1': 0.05985392694536235, 'lambda_l2': 0.06468300162394233, 'learning_rate_xg': 0.07577197246568779, 'max_depth_xg': 4, 'n_estimators_xg': 329, 'subsample_xg': 0.9995537705994614, 'colsample_bytree_xg': 0.39345448006955785, 'learning_rate_cb': 0.09096869836277585, 'depth_cb': 5, 'iterations_cb': 230, 'weight1': 0.4335719396221862, 'weight2': 0.5403766379160011}. Best is trial 4 with value: 0.3204118782902718.\n","output_type":"stream"},{"name":"stdout","text":"Fold 5 - Train QWK: 0.8796, Validation QWK: 0.3293\nMean Train QWK --> 0.8722\nMean Validation QWK ---> 0.2832\n----> || Optimized QWK SCORE :: \u001b[36m\u001b[1m 0.283\u001b[0m\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 20%|██ | 1/5 [00:05<00:20, 5.23s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 1 - Train QWK: 0.0000, Validation QWK: 0.0000\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 40%|████ | 2/5 [00:10<00:15, 5.07s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 2 - Train QWK: 0.0000, Validation QWK: 0.0000\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 60%|██████ | 3/5 [00:15<00:10, 5.03s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 3 - Train QWK: 0.0000, Validation QWK: 0.0000\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 80%|████████ | 4/5 [00:20<00:05, 5.02s/it]","output_type":"stream"},{"name":"stdout","text":"Fold 4 - Train QWK: 0.0000, Validation QWK: 0.0000\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 100%|██████████| 5/5 [00:25<00:00, 5.04s/it]\n[I 2024-10-13 10:21:01,913] Trial 6 finished with value: 0.0 and parameters: {'learning_rate_lg': 0.036720391975363406, 'max_depth_lg': 1, 'num_leaves_lg': 798, 'min_data_in_leaf_lg': 193, 'feature_fraction_lg': 0.8676024511837064, 'bagging_fraction_lg': 0.09586433623925997, 'bagging_freq_lg': 515, 'lambda_l1': 0.6127958049434139, 'lambda_l2': 0.38549532050613, 'learning_rate_xg': 0.16765403093485626, 'max_depth_xg': 6, 'n_estimators_xg': 464, 'subsample_xg': 0.36276235749561947, 'colsample_bytree_xg': 0.33991469605485053, 'learning_rate_cb': 0.11409002937878514, 'depth_cb': 2, 'iterations_cb': 243, 'weight1': 0.9715736147273019, 'weight2': 0.003733794065640696}. Best is trial 4 with value: 0.3204118782902718.\n","output_type":"stream"},{"name":"stdout","text":"Fold 5 - Train QWK: 0.0000, Validation QWK: 0.0000\nMean Train QWK --> 0.0000\nMean Validation QWK ---> 0.0000\n----> || Optimized QWK SCORE :: \u001b[36m\u001b[1m 0.000\u001b[0m\n","output_type":"stream"},{"name":"stderr","text":"Training Folds: 0%| | 0/5 [00:01 59\u001b[0m \u001b[43mstudy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobjective\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_trials\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m200\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 60\u001b[0m new_best_val \u001b[38;5;241m=\u001b[39m study\u001b[38;5;241m.\u001b[39mbest_value\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/optuna/study/study.py:475\u001b[0m, in \u001b[0;36mStudy.optimize\u001b[0;34m(self, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[0m\n\u001b[1;32m 373\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21moptimize\u001b[39m(\n\u001b[1;32m 374\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 375\u001b[0m func: ObjectiveFuncType,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 382\u001b[0m show_progress_bar: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 383\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 384\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Optimize an objective function.\u001b[39;00m\n\u001b[1;32m 385\u001b[0m \n\u001b[1;32m 386\u001b[0m \u001b[38;5;124;03m Optimization is done by choosing a suitable set of hyperparameter values from a given\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 473\u001b[0m \u001b[38;5;124;03m If nested invocation of this method occurs.\u001b[39;00m\n\u001b[1;32m 474\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 475\u001b[0m \u001b[43m_optimize\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 476\u001b[0m \u001b[43m \u001b[49m\u001b[43mstudy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 477\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 478\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_trials\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_trials\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 479\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 480\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_jobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 481\u001b[0m \u001b[43m \u001b[49m\u001b[43mcatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcatch\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mIterable\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mcatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 482\u001b[0m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 483\u001b[0m \u001b[43m \u001b[49m\u001b[43mgc_after_trial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgc_after_trial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 484\u001b[0m \u001b[43m \u001b[49m\u001b[43mshow_progress_bar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshow_progress_bar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 485\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/optuna/study/_optimize.py:63\u001b[0m, in \u001b[0;36m_optimize\u001b[0;34m(study, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_jobs \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m---> 63\u001b[0m \u001b[43m_optimize_sequential\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 64\u001b[0m \u001b[43m \u001b[49m\u001b[43mstudy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 65\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 66\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_trials\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 67\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 68\u001b[0m \u001b[43m \u001b[49m\u001b[43mcatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 69\u001b[0m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 70\u001b[0m \u001b[43m \u001b[49m\u001b[43mgc_after_trial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 71\u001b[0m \u001b[43m \u001b[49m\u001b[43mreseed_sampler_rng\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[43m \u001b[49m\u001b[43mtime_start\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[43m \u001b[49m\u001b[43mprogress_bar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprogress_bar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 74\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 76\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_jobs \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/optuna/study/_optimize.py:160\u001b[0m, in \u001b[0;36m_optimize_sequential\u001b[0;34m(study, func, n_trials, timeout, catch, callbacks, gc_after_trial, reseed_sampler_rng, time_start, progress_bar)\u001b[0m\n\u001b[1;32m 157\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m 159\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 160\u001b[0m frozen_trial \u001b[38;5;241m=\u001b[39m \u001b[43m_run_trial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstudy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcatch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 162\u001b[0m \u001b[38;5;66;03m# The following line mitigates memory problems that can be occurred in some\u001b[39;00m\n\u001b[1;32m 163\u001b[0m \u001b[38;5;66;03m# environments (e.g., services that use computing containers such as GitHub Actions).\u001b[39;00m\n\u001b[1;32m 164\u001b[0m \u001b[38;5;66;03m# Please refer to the following PR for further details:\u001b[39;00m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;66;03m# https://github.com/optuna/optuna/pull/325.\u001b[39;00m\n\u001b[1;32m 166\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m gc_after_trial:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/optuna/study/_optimize.py:248\u001b[0m, in \u001b[0;36m_run_trial\u001b[0;34m(study, func, catch)\u001b[0m\n\u001b[1;32m 241\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mShould not reach.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 243\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 244\u001b[0m frozen_trial\u001b[38;5;241m.\u001b[39mstate \u001b[38;5;241m==\u001b[39m TrialState\u001b[38;5;241m.\u001b[39mFAIL\n\u001b[1;32m 245\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m func_err \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 246\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(func_err, catch)\n\u001b[1;32m 247\u001b[0m ):\n\u001b[0;32m--> 248\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m func_err\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m frozen_trial\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/optuna/study/_optimize.py:197\u001b[0m, in \u001b[0;36m_run_trial\u001b[0;34m(study, func, catch)\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m get_heartbeat_thread(trial\u001b[38;5;241m.\u001b[39m_trial_id, study\u001b[38;5;241m.\u001b[39m_storage):\n\u001b[1;32m 196\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 197\u001b[0m value_or_values \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrial\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m exceptions\u001b[38;5;241m.\u001b[39mTrialPruned \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 199\u001b[0m \u001b[38;5;66;03m# TODO(mamu): Handle multi-objective cases.\u001b[39;00m\n\u001b[1;32m 200\u001b[0m state \u001b[38;5;241m=\u001b[39m TrialState\u001b[38;5;241m.\u001b[39mPRUNED\n","Cell \u001b[0;32mIn[8], line 53\u001b[0m, in \u001b[0;36mobjective\u001b[0;34m(trial)\u001b[0m\n\u001b[1;32m 46\u001b[0m weight2 \u001b[38;5;241m=\u001b[39m trial\u001b[38;5;241m.\u001b[39msuggest_float(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweight2\u001b[39m\u001b[38;5;124m'\u001b[39m, 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model\u001b[38;5;241m.\u001b[39mpredict(X_val)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/ensemble/_voting.py:598\u001b[0m, in \u001b[0;36mVotingRegressor.fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m 597\u001b[0m y \u001b[38;5;241m=\u001b[39m column_or_1d(y, warn\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m--> 598\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/ensemble/_voting.py:81\u001b[0m, in 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\u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_fit_single_estimator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 83\u001b[0m \u001b[43m \u001b[49m\u001b[43mclone\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 84\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 85\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 86\u001b[0m \u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 87\u001b[0m \u001b[43m \u001b[49m\u001b[43mmessage_clsname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mVoting\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 88\u001b[0m \u001b[43m \u001b[49m\u001b[43mmessage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_log_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnames\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mclfs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 89\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 90\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclf\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mclfs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 91\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mclf\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m!=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdrop\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m 92\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnamed_estimators_ \u001b[38;5;241m=\u001b[39m Bunch()\n\u001b[1;32m 96\u001b[0m \u001b[38;5;66;03m# Uses 'drop' as placeholder for dropped estimators\u001b[39;00m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/utils/parallel.py:63\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 58\u001b[0m config \u001b[38;5;241m=\u001b[39m get_config()\n\u001b[1;32m 59\u001b[0m iterable_with_config \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 60\u001b[0m (_with_config(delayed_func, config), args, kwargs)\n\u001b[1;32m 61\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m delayed_func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m iterable\n\u001b[1;32m 62\u001b[0m )\n\u001b[0;32m---> 63\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43miterable_with_config\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/joblib/parallel.py:1918\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 1916\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_sequential_output(iterable)\n\u001b[1;32m 1917\u001b[0m \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[0;32m-> 1918\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1920\u001b[0m \u001b[38;5;66;03m# Let's create an ID that uniquely identifies the current call. If the\u001b[39;00m\n\u001b[1;32m 1921\u001b[0m \u001b[38;5;66;03m# call is interrupted early and that the same instance is immediately\u001b[39;00m\n\u001b[1;32m 1922\u001b[0m \u001b[38;5;66;03m# re-used, this id will be used to prevent workers that were\u001b[39;00m\n\u001b[1;32m 1923\u001b[0m \u001b[38;5;66;03m# concurrently finalizing a task from the previous call to run the\u001b[39;00m\n\u001b[1;32m 1924\u001b[0m \u001b[38;5;66;03m# callback.\u001b[39;00m\n\u001b[1;32m 1925\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/joblib/parallel.py:1847\u001b[0m, in \u001b[0;36mParallel._get_sequential_output\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 1845\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_batches \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 1846\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m-> 1847\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1848\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_completed_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 1849\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_progress()\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/utils/parallel.py:123\u001b[0m, in \u001b[0;36m_FuncWrapper.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 121\u001b[0m config \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m 122\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconfig):\n\u001b[0;32m--> 123\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/sklearn/ensemble/_base.py:46\u001b[0m, in \u001b[0;36m_fit_single_estimator\u001b[0;34m(estimator, X, y, sample_weight, message_clsname, message)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _print_elapsed_time(message_clsname, message):\n\u001b[0;32m---> 46\u001b[0m \u001b[43mestimator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m estimator\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/catboost/core.py:5873\u001b[0m, in \u001b[0;36mCatBoostRegressor.fit\u001b[0;34m(self, X, y, cat_features, text_features, embedding_features, graph, sample_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot, plot_file, column_description, verbose_eval, metric_period, silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model, callbacks, log_cout, log_cerr)\u001b[0m\n\u001b[1;32m 5871\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mloss_function\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m params:\n\u001b[1;32m 5872\u001b[0m CatBoostRegressor\u001b[38;5;241m.\u001b[39m_check_is_compatible_loss(params[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mloss_function\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m-> 5873\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcat_features\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtext_features\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43membedding_features\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgraph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbaseline\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5874\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_best_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meval_set\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogging_level\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mplot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mplot_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumn_description\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5875\u001b[0m \u001b[43m \u001b[49m\u001b[43mverbose_eval\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetric_period\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msilent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mearly_stopping_rounds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5876\u001b[0m \u001b[43m \u001b[49m\u001b[43msave_snapshot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msnapshot_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msnapshot_interval\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minit_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlog_cout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlog_cerr\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/catboost/core.py:2410\u001b[0m, in \u001b[0;36mCatBoost._fit\u001b[0;34m(self, X, y, cat_features, text_features, embedding_features, pairs, graph, sample_weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot, plot_file, column_description, verbose_eval, metric_period, silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model, callbacks, log_cout, log_cerr)\u001b[0m\n\u001b[1;32m 2407\u001b[0m allow_clear_pool \u001b[38;5;241m=\u001b[39m train_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_clear_pool\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 2409\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m plot_wrapper(plot, plot_file, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTraining plots\u001b[39m\u001b[38;5;124m'\u001b[39m, [_get_train_dir(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_params())]):\n\u001b[0;32m-> 2410\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_train\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2411\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_pool\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2412\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_params\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meval_sets\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2413\u001b[0m \u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2414\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_clear_pool\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2415\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_params\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minit_model\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 2416\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2418\u001b[0m \u001b[38;5;66;03m# Have property feature_importance possibly set\u001b[39;00m\n\u001b[1;32m 2419\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_object\u001b[38;5;241m.\u001b[39m_get_loss_function_name()\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/catboost/core.py:1790\u001b[0m, in \u001b[0;36m_CatBoostBase._train\u001b[0;34m(self, train_pool, test_pool, params, allow_clear_pool, init_model)\u001b[0m\n\u001b[1;32m 1789\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_train\u001b[39m(\u001b[38;5;28mself\u001b[39m, train_pool, test_pool, params, allow_clear_pool, init_model):\n\u001b[0;32m-> 1790\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_object\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_train\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_pool\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_pool\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_clear_pool\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minit_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_object\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43minit_model\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 1791\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_trained_model_attributes()\n","File \u001b[0;32m_catboost.pyx:5017\u001b[0m, in \u001b[0;36m_catboost._CatBoost._train\u001b[0;34m()\u001b[0m\n","File \u001b[0;32m_catboost.pyx:5066\u001b[0m, in \u001b[0;36m_catboost._CatBoost._train\u001b[0;34m()\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"}]},{"cell_type":"code","source":"from sklearn.cluster import KMeans, DBSCAN\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.metrics import silhouette_score\ncols = [i for i in train.columns.tolist() if 'Enc_' in i]\ndata_for_cl = train[cols + ['sii']].copy()\n\nscaler = StandardScaler()\ndata_for_cl[cols] = scaler.fit_transform(data_for_cl[cols])\n\n# for i in range(2,100):\n# model = KMeans(n_clusters=i)\n# cluster_num = model.fit_predict(data_for_cl[cols])\n# data_for_cl['cluster_num'] = cluster_num\n# print(i, silhouette_score(data_for_cl[cols], cluster_num))\n \n\nmodel = KMeans(n_clusters=2)\ncluster_num = model.fit_predict(data_for_cl[cols])\ndata_for_cl['cluster_num'] = cluster_num\nprint(i, silhouette_score(data_for_cl[cols], cluster_num))","metadata":{"execution":{"iopub.status.busy":"2024-10-13T10:42:27.733761Z","iopub.execute_input":"2024-10-13T10:42:27.734215Z","iopub.status.idle":"2024-10-13T10:42:27.886095Z","shell.execute_reply.started":"2024-10-13T10:42:27.734173Z","shell.execute_reply":"2024-10-13T10:42:27.884799Z"},"trusted":true},"execution_count":31,"outputs":[{"name":"stdout","text":"99 0.18422435\n","output_type":"stream"}]},{"cell_type":"code","source":"data_for_cl['sii'].mean()","metadata":{"execution":{"iopub.status.busy":"2024-10-13T10:42:30.892608Z","iopub.execute_input":"2024-10-13T10:42:30.893631Z","iopub.status.idle":"2024-10-13T10:42:30.901597Z","shell.execute_reply.started":"2024-10-13T10:42:30.893581Z","shell.execute_reply":"2024-10-13T10:42:30.900189Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"0.572289156626506"},"metadata":{}}]},{"cell_type":"code","source":"data_for_cl.groupby('cluster_num')['sii'].agg(['mean','size']).reset_index()","metadata":{"execution":{"iopub.status.busy":"2024-10-13T10:42:35.262298Z","iopub.execute_input":"2024-10-13T10:42:35.263216Z","iopub.status.idle":"2024-10-13T10:42:35.278068Z","shell.execute_reply.started":"2024-10-13T10:42:35.263161Z","shell.execute_reply":"2024-10-13T10:42:35.27689Z"},"trusted":true},"execution_count":33,"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":" cluster_num mean size\n0 0 0.495549 337\n1 1 0.611533 659","text/html":"
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cluster_nummeansize
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"},"metadata":{}}]}]}