PronosticTempTranformers.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyMRU8psq7xuAOFeLLFrb/kS"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"00o81QyxhaMK","executionInfo":{"status":"ok","timestamp":1719506272601,"user_tz":300,"elapsed":154839,"user":{"displayName":"subenomai haku","userId":"01968901610098885489"}},"outputId":"bffdf74b-6301-4734-b642-73a6145358e0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","73/73 [==============================] - 16s 26ms/step - loss: 0.0659 - val_loss: 0.0284\n","Epoch 2/100\n","73/73 [==============================] - 2s 25ms/step - loss: 0.0350 - val_loss: 0.0234\n","Epoch 3/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0315 - val_loss: 0.0224\n","Epoch 4/100\n","73/73 [==============================] - 1s 15ms/step - loss: 0.0296 - val_loss: 0.0223\n","Epoch 5/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0282 - val_loss: 0.0235\n","Epoch 6/100\n","73/73 [==============================] - 1s 17ms/step - loss: 0.0282 - val_loss: 0.0233\n","Epoch 7/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0276 - val_loss: 0.0228\n","Epoch 8/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0269 - val_loss: 0.0234\n","Epoch 9/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0275 - val_loss: 0.0234\n","Epoch 10/100\n","73/73 [==============================] - 1s 17ms/step - loss: 0.0268 - val_loss: 0.0230\n","Epoch 11/100\n","73/73 [==============================] - 2s 21ms/step - loss: 0.0270 - val_loss: 0.0229\n","Epoch 12/100\n","73/73 [==============================] - 2s 23ms/step - loss: 0.0263 - val_loss: 0.0230\n","Epoch 13/100\n","73/73 [==============================] - 1s 18ms/step - loss: 0.0258 - val_loss: 0.0230\n","Epoch 14/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0254 - val_loss: 0.0229\n","Epoch 15/100\n","73/73 [==============================] - 1s 15ms/step - loss: 0.0259 - val_loss: 0.0226\n","Epoch 16/100\n","73/73 [==============================] - 1s 17ms/step - loss: 0.0258 - val_loss: 0.0223\n","Epoch 17/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0255 - val_loss: 0.0216\n","Epoch 18/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0244 - val_loss: 0.0219\n","Epoch 19/100\n","73/73 [==============================] - 1s 18ms/step - loss: 0.0251 - val_loss: 0.0219\n","Epoch 20/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0250 - val_loss: 0.0216\n","Epoch 21/100\n","73/73 [==============================] - 1s 19ms/step - loss: 0.0246 - val_loss: 0.0224\n","Epoch 22/100\n","73/73 [==============================] - 2s 23ms/step - loss: 0.0247 - val_loss: 0.0214\n","Epoch 23/100\n","73/73 [==============================] - 2s 27ms/step - loss: 0.0248 - val_loss: 0.0223\n","Epoch 24/100\n","73/73 [==============================] - 1s 20ms/step - loss: 0.0240 - val_loss: 0.0213\n","Epoch 25/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0246 - val_loss: 0.0220\n","Epoch 26/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0237 - val_loss: 0.0216\n","Epoch 27/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0244 - val_loss: 0.0214\n","Epoch 28/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0240 - val_loss: 0.0213\n","Epoch 29/100\n","73/73 [==============================] - 1s 17ms/step - loss: 0.0240 - val_loss: 0.0215\n","Epoch 30/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0234 - val_loss: 0.0215\n","Epoch 31/100\n","73/73 [==============================] - 2s 23ms/step - loss: 0.0237 - val_loss: 0.0210\n","Epoch 32/100\n","73/73 [==============================] - 2s 22ms/step - loss: 0.0235 - val_loss: 0.0212\n","Epoch 33/100\n","73/73 [==============================] - 1s 17ms/step - loss: 0.0233 - val_loss: 0.0216\n","Epoch 34/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0232 - val_loss: 0.0211\n","Epoch 35/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0232 - val_loss: 0.0212\n","Epoch 36/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0230 - val_loss: 0.0212\n","Epoch 37/100\n","73/73 [==============================] - 1s 15ms/step - loss: 0.0225 - val_loss: 0.0208\n","Epoch 38/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0229 - val_loss: 0.0209\n","Epoch 39/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0228 - val_loss: 0.0212\n","Epoch 40/100\n","73/73 [==============================] - 1s 15ms/step - loss: 0.0230 - val_loss: 0.0208\n","Epoch 41/100\n","73/73 [==============================] - 2s 23ms/step - loss: 0.0225 - val_loss: 0.0211\n","Epoch 42/100\n","73/73 [==============================] - 2s 23ms/step - loss: 0.0230 - val_loss: 0.0217\n","Epoch 43/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0230 - val_loss: 0.0211\n","Epoch 44/100\n","73/73 [==============================] - 1s 17ms/step - loss: 0.0228 - val_loss: 0.0208\n","Epoch 45/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0228 - val_loss: 0.0209\n","Epoch 46/100\n","73/73 [==============================] - 1s 17ms/step - loss: 0.0223 - val_loss: 0.0210\n","Epoch 47/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0226 - val_loss: 0.0208\n","Epoch 48/100\n","73/73 [==============================] - 1s 17ms/step - loss: 0.0224 - val_loss: 0.0214\n","Epoch 49/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0224 - val_loss: 0.0216\n","Epoch 50/100\n","73/73 [==============================] - 1s 15ms/step - loss: 0.0224 - val_loss: 0.0210\n","Epoch 51/100\n","73/73 [==============================] - 1s 20ms/step - loss: 0.0222 - val_loss: 0.0213\n","Epoch 52/100\n","73/73 [==============================] - 2s 24ms/step - loss: 0.0226 - val_loss: 0.0213\n","Epoch 53/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0223 - val_loss: 0.0208\n","Epoch 54/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0224 - val_loss: 0.0208\n","Epoch 55/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0223 - val_loss: 0.0207\n","Epoch 56/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0221 - val_loss: 0.0206\n","Epoch 57/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0227 - val_loss: 0.0215\n","Epoch 58/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0226 - val_loss: 0.0214\n","Epoch 59/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0224 - val_loss: 0.0215\n","Epoch 60/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0226 - val_loss: 0.0212\n","Epoch 61/100\n","73/73 [==============================] - 2s 22ms/step - loss: 0.0225 - val_loss: 0.0211\n","Epoch 62/100\n","73/73 [==============================] - 2s 26ms/step - loss: 0.0233 - val_loss: 0.0213\n","Epoch 63/100\n","73/73 [==============================] - 1s 17ms/step - loss: 0.0228 - val_loss: 0.0210\n","Epoch 64/100\n","73/73 [==============================] - 1s 18ms/step - loss: 0.0226 - val_loss: 0.0209\n","Epoch 65/100\n","73/73 [==============================] - 1s 17ms/step - loss: 0.0226 - val_loss: 0.0210\n","Epoch 66/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0225 - val_loss: 0.0211\n","Epoch 67/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0225 - val_loss: 0.0211\n","Epoch 68/100\n","73/73 [==============================] - 1s 17ms/step - loss: 0.0227 - val_loss: 0.0208\n","Epoch 69/100\n","73/73 [==============================] - 1s 17ms/step - loss: 0.0228 - val_loss: 0.0215\n","Epoch 70/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0225 - val_loss: 0.0212\n","Epoch 71/100\n","73/73 [==============================] - 2s 23ms/step - loss: 0.0225 - val_loss: 0.0212\n","Epoch 72/100\n","73/73 [==============================] - 2s 23ms/step - loss: 0.0223 - val_loss: 0.0212\n","Epoch 73/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0224 - val_loss: 0.0212\n","Epoch 74/100\n","73/73 [==============================] - 1s 17ms/step - loss: 0.0226 - val_loss: 0.0210\n","Epoch 75/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0222 - val_loss: 0.0214\n","Epoch 76/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0222 - val_loss: 0.0208\n","Epoch 77/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0225 - val_loss: 0.0210\n","Epoch 78/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0229 - val_loss: 0.0221\n","Epoch 79/100\n","73/73 [==============================] - 1s 15ms/step - loss: 0.0227 - val_loss: 0.0214\n","Epoch 80/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0229 - val_loss: 0.0214\n","Epoch 81/100\n","73/73 [==============================] - 2s 22ms/step - loss: 0.0228 - val_loss: 0.0212\n","Epoch 82/100\n","73/73 [==============================] - 2s 23ms/step - loss: 0.0226 - val_loss: 0.0213\n","Epoch 83/100\n","73/73 [==============================] - 1s 17ms/step - loss: 0.0227 - val_loss: 0.0212\n","Epoch 84/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0227 - val_loss: 0.0214\n","Epoch 85/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0225 - val_loss: 0.0213\n","Epoch 86/100\n","73/73 [==============================] - 1s 15ms/step - loss: 0.0224 - val_loss: 0.0214\n","Epoch 87/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0225 - val_loss: 0.0211\n","Epoch 88/100\n","73/73 [==============================] - 1s 17ms/step - loss: 0.0222 - val_loss: 0.0213\n","Epoch 89/100\n","73/73 [==============================] - 1s 17ms/step - loss: 0.0224 - val_loss: 0.0211\n","Epoch 90/100\n","73/73 [==============================] - 1s 17ms/step - loss: 0.0224 - val_loss: 0.0210\n","Epoch 91/100\n","73/73 [==============================] - 1s 19ms/step - loss: 0.0224 - val_loss: 0.0211\n","Epoch 92/100\n","73/73 [==============================] - 2s 23ms/step - loss: 0.0223 - val_loss: 0.0210\n","Epoch 93/100\n","73/73 [==============================] - 1s 19ms/step - loss: 0.0225 - val_loss: 0.0212\n","Epoch 94/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0226 - val_loss: 0.0214\n","Epoch 95/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0227 - val_loss: 0.0209\n","Epoch 96/100\n","73/73 [==============================] - 1s 17ms/step - loss: 0.0226 - val_loss: 0.0211\n","Epoch 97/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0225 - val_loss: 0.0210\n","Epoch 98/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0227 - val_loss: 0.0212\n","Epoch 99/100\n","73/73 [==============================] - 1s 16ms/step - loss: 0.0222 - val_loss: 0.0210\n","Epoch 100/100\n","73/73 [==============================] - 1s 17ms/step - loss: 0.0224 - val_loss: 0.0210\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 464ms/step\n","Temperatura predicha para 1991-01-01: 11.85\n","1/1 [==============================] - 0s 19ms/step\n","Temperatura predicha para 1998-01-01: 11.64\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}],"source":["import pandas as pd\n","import numpy as np\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.model_selection import train_test_split\n","from tensorflow import keras\n","from tensorflow.keras import layers\n","import matplotlib.pyplot as plt\n","from datetime import datetime, timedelta\n","\n","# Cargar y preparar los datos (igual que antes)\n","url = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv'\n","df = pd.read_csv(url, parse_dates=['Date'], index_col='Date')\n","\n","# Crear características de tiempo (igual que antes)\n","df['day'] = df.index.day\n","df['month'] = df.index.month\n","df['year'] = df.index.year\n","\n","# Normalizar los datos (igual que antes)\n","scaler_features = MinMaxScaler()\n","scaler_temp = MinMaxScaler()\n","X = scaler_features.fit_transform(df[['day', 'month', 'year']])\n","y = scaler_temp.fit_transform(df[['Temp']])\n","\n","# Dividir los datos en conjuntos de entrenamiento y prueba\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# Redimensionar los datos para el transformer\n","X_train = X_train.reshape((X_train.shape[0], 1, X_train.shape[1]))\n","X_test = X_test.reshape((X_test.shape[0], 1, X_test.shape[1]))\n","\n","# Crear el modelo con transformer\n","def transformer_encoder(inputs, head_size, num_heads, ff_dim, dropout=0):\n","    x = layers.LayerNormalization(epsilon=1e-6)(inputs)\n","    x = layers.MultiHeadAttention(\n","        key_dim=head_size, num_heads=num_heads, dropout=dropout\n","    )(x, x)\n","    x = layers.Dropout(dropout)(x)\n","    res = x + inputs\n","\n","    x = layers.LayerNormalization(epsilon=1e-6)(res)\n","    x = layers.Conv1D(filters=ff_dim, kernel_size=1, activation=\"relu\")(x)\n","    x = layers.Dropout(dropout)(x)\n","    x = layers.Conv1D(filters=inputs.shape[-1], kernel_size=1)(x)\n","    return x + res\n","\n","def build_model(\n","    input_shape,\n","    head_size,\n","    num_heads,\n","    ff_dim,\n","    num_transformer_blocks,\n","    mlp_units,\n","    dropout=0,\n","    mlp_dropout=0,\n","):\n","    inputs = keras.Input(shape=input_shape)\n","    x = inputs\n","    for _ in range(num_transformer_blocks):\n","        x = transformer_encoder(x, head_size, num_heads, ff_dim, dropout)\n","\n","    x = layers.GlobalAveragePooling1D(data_format=\"channels_first\")(x)\n","    for dim in mlp_units:\n","        x = layers.Dense(dim, activation=\"relu\")(x)\n","        x = layers.Dropout(mlp_dropout)(x)\n","    outputs = layers.Dense(1)(x)\n","    return keras.Model(inputs, outputs)\n","\n","model = build_model(\n","    input_shape=(1, 3),\n","    head_size=256,\n","    num_heads=4,\n","    ff_dim=4,\n","    num_transformer_blocks=4,\n","    mlp_units=[128],\n","    mlp_dropout=0.4,\n","    dropout=0.25,\n",")\n","\n","# Compilar el modelo\n","model.compile(optimizer=\"adam\", loss=\"mse\")\n","\n","# Entrenar el modelo\n","history = model.fit(\n","    X_train,\n","    y_train,\n","    validation_split=0.2,\n","    epochs=100,\n","    batch_size=32,\n","    verbose=1,\n",")\n","\n","# Función para predecir la temperatura para una fecha dada\n","def predict_temperature(date_str):\n","    date = datetime.strptime(date_str, '%Y-%m-%d')\n","    input_data = np.array([[date.day, date.month, date.year]])\n","    input_scaled = scaler_features.transform(input_data)\n","    input_reshaped = input_scaled.reshape((1, 1, 3))\n","    prediction_scaled = model.predict(input_reshaped)\n","    prediction = scaler_temp.inverse_transform(prediction_scaled)[0][0]\n","    return prediction\n","\n","# Ejemplo de uso\n","print(f\"Temperatura predicha para 1991-01-01: {predict_temperature('1991-01-01'):.2f}\")\n","print(f\"Temperatura predicha para 1998-01-01: {predict_temperature('1998-01-01'):.2f}\")\n","\n","# Visualizar el rendimiento del modelo\n","plt.plot(history.history['loss'], label='train')\n","plt.plot(history.history['val_loss'], label='validation')\n","plt.title('Model Loss')\n","plt.ylabel('Loss')\n","plt.xlabel('Epoch')\n","plt.legend()\n","plt.show()\n","\n"]},{"cell_type":"code","source":["# Visualizar predicciones vs valores reales\n","dates_to_predict = pd.date_range(start='1991-01-01', end='1992-12-31', freq='D')\n","predictions = [predict_temperature(date.strftime('%Y-%m-%d')) for date in dates_to_predict]\n","\n","plt.figure(figsize=(12, 6))\n","plt.plot(df.index, df['Temp'], label='Datos reales', alpha=0.7)\n","plt.plot(dates_to_predict, predictions, label='Predicciones', color='red')\n","plt.title('Temperaturas reales vs Predicciones')\n","plt.xlabel('Fecha')\n","plt.ylabel('Temperatura')\n","plt.legend()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"WMr1h-WWjACM","executionInfo":{"status":"ok","timestamp":1719506340640,"user_tz":300,"elapsed":54386,"user":{"displayName":"subenomai haku","userId":"01968901610098885489"}},"outputId":"a2fbb689-330a-42ac-9a22-fcfbbfa9ef3f"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 17ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 27ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 34ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 33ms/step\n","1/1 [==============================] - 0s 33ms/step\n","1/1 [==============================] - ETA: 0s"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 39ms/step\n","1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 27ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 27ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 34ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 32ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 33ms/step\n","1/1 [==============================] - 0s 31ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 31ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 42ms/step\n","1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 46ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 25ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - ETA: 0s"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 27ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 25ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 17ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 33ms/step\n","1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 36ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 36ms/step\n","1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 29ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 31ms/step\n","1/1 [==============================] - 0s 38ms/step\n","1/1 [==============================] - 0s 31ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 31ms/step\n","1/1 [==============================] - 0s 35ms/step\n","1/1 [==============================] - 0s 30ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 38ms/step\n","1/1 [==============================] - 0s 30ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 32ms/step\n","1/1 [==============================] - 0s 32ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 48ms/step\n","1/1 [==============================] - ETA: 0s"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 33ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 17ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 32ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 26ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 36ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 42ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 27ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 30ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 30ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 48ms/step\n","1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 32ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 32ms/step\n","1/1 [==============================] - 0s 38ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 21ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 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19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 22ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 24ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 17ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 33ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 33ms/step\n","1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 32ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 42ms/step\n","1/1 [==============================] - 0s 26ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 29ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 45ms/step\n","1/1 [==============================] - 0s 37ms/step\n","1/1 [==============================] - 0s 28ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 33ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 30ms/step\n","1/1 [==============================] - 0s 31ms/step\n","1/1 [==============================] - 0s 32ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 31ms/step\n","1/1 [==============================] - 0s 31ms/step\n","1/1 [==============================] - ETA: 0s"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 33ms/step\n","1/1 [==============================] - 0s 34ms/step\n","1/1 [==============================] - ETA: 0s"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 33ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 28ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 23ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 20ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 24ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - ETA: 0s"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 20ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - ETA: 0s"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":["from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n","# Función de evaluación\n","def evaluate_model(model, X_test, y_test, scaler_temp):\n","    # Hacer predicciones\n","    y_pred_scaled = model.predict(X_test)\n","\n","    # Invertir la normalización\n","    y_pred = scaler_temp.inverse_transform(y_pred_scaled)\n","    y_true = scaler_temp.inverse_transform(y_test)\n","\n","    # Calcular métricas\n","    mse = mean_squared_error(y_true, y_pred)\n","    rmse = np.sqrt(mse)\n","    mae = mean_absolute_error(y_true, y_pred)\n","    mape = np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n","    r2 = r2_score(y_true, y_pred)\n","\n","    print(f\"MSE: {mse:.4f}\")\n","    print(f\"RMSE: {rmse:.4f}\")\n","    print(f\"MAE: {mae:.4f}\")\n","    print(f\"MAPE: {mape:.4f}%\")\n","    print(f\"R²: {r2:.4f}\")\n","\n","    # Graficar predicciones vs valores reales\n","    plt.figure(figsize=(12, 6))\n","    plt.plot(y_true, label='Valores reales')\n","    plt.plot(y_pred, label='Predicciones')\n","    plt.title('Predicciones vs Valores reales')\n","    plt.xlabel('Muestras')\n","    plt.ylabel('Temperatura')\n","    plt.legend()\n","    plt.show()\n","\n","    # Graficar residuos\n","    residuals = y_true - y_pred\n","    plt.figure(figsize=(12, 6))\n","    plt.plot(residuals)\n","    plt.title('Residuos')\n","    plt.xlabel('Muestras')\n","    plt.ylabel('Error')\n","    plt.axhline(y=0, color='r', linestyle='--')\n","    plt.show()\n","\n","    # Histograma de residuos\n","    plt.figure(figsize=(12, 6))\n","    plt.hist(residuals, bins=50)\n","    plt.title('Distribución de residuos')\n","    plt.xlabel('Error')\n","    plt.ylabel('Frecuencia')\n","    plt.show()\n","\n","# Evaluar el modelo\n","evaluate_model(model, X_test, y_test, scaler_temp)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"bunT-eO5kauJ","executionInfo":{"status":"ok","timestamp":1719506444932,"user_tz":300,"elapsed":2366,"user":{"displayName":"subenomai haku","userId":"01968901610098885489"}},"outputId":"5255f36a-6e90-4c85-ac70-0b400e121949"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["23/23 [==============================] - 0s 4ms/step\n","MSE: 15.2983\n","RMSE: 3.9113\n","MAE: 3.0991\n","MAPE: 40.4206%\n","R²: 0.1066\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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tc+fOrfG1/7Of//zn2WGHHZYVFBRkffv2zX7xi19kI0aMqHLZsx2+//3vZ/369ctatGiRtWnTJjvyyCOza665Jlu9evVO9zFixIisVatWtT6+q3mff/757LOf/WzWvXv3rKCgIOvUqVP28Y9/PFu0aFGVeWr6Oc6bNy/r169f1rx58+zggw/Opk+fXu04y7J//J4uvvjirLi4OGvTpk32mc98Jlu7dm29fzc33XRTdsIJJ2Rt27bNWrRokR166KHZxIkTs82bN+/0ZwXAvikvy3J05hYAAACgznyHHAAAABJQyAEAACABhRwAAAASUMgBAAAgAYUcAAAAElDIAQAAIIH81AH2tu3bt8fq1aujTZs2kZeXlzoOAAAAjVyWZfHOO+9E165do0mT2tfBG30hX716dXTr1i11DAAAAPYzq1atioMOOqjWxxt9IW/Tpk1E/OMHUVRUlDgNAAAAjd2GDRuiW7dulX20No2+kO/4mHpRUZFCDgAAwAdmV1+bdlI3AAAASEAhBwAAgAQUcgAAAEhAIQcAAIAEFHIAAABIQCEHAACABBRyAAAASEAhBwAAgAQUcgAAAEhAIQcAAIAEFHIAAABIQCEHAACABBRyAAAASEAhBwAAgAQUcgAAAEhAIQcAAIAEFHIAAABIQCEHAACABBRyAAAASCA/dQAAaEhKx8zc4zlWTB6agyQAQGNnhRwAAAASUMgBAAAgAYUcAAAAElDIAQAAIAGFHAAAABJQyAEAACABhRwAAAASUMgBAAAgAYUcAAAAElDIAQAAIAGFHAAAABJQyAEAACABhRwAAAASUMgBAAAgAYUcAAAAElDIAQAAIAGFHAAAABJQyAEAACABhRwAAAASyE8dAIDGoXTMzD16/orJQ3OUBABg32CFHAAAABJQyAEAACABhRwAAAASUMgBAAAgAYUcAAAAElDIAQAAIIEGU8gnT54ceXl5cdVVV1WObdq0KUaNGhUHHHBAtG7dOoYPHx7l5eXpQgIAAECONIhC/txzz8Vdd90VRx11VJXxq6++On71q1/FQw89FPPmzYvVq1fHeeedlyglAAAA5E7yQv7uu+/GhRdeGD/4wQ+iXbt2lePr16+Pu+++O6ZMmRKnn3569OvXL2bMmBHPPvtsLFiwoNb5KioqYsOGDVVuAAAA0NAkL+SjRo2KoUOHxqBBg6qML168OLZs2VJl/NBDD43u3bvH/Pnza51v0qRJUVxcXHnr1q3bXssOAAAA9ZW0kD/wwAPx/PPPx6RJk6o9VlZWFs2bN4+2bdtWGS8pKYmysrJa5xw7dmysX7++8rZq1apcxwYAAIA9lp9qx6tWrYqvfe1rMXv27CgsLMzZvAUFBVFQUJCz+QAAAGBvSLZCvnjx4li7dm0cd9xxkZ+fH/n5+TFv3ry4/fbbIz8/P0pKSmLz5s2xbt26Ks8rLy+Pzp07pwkNAAAAOZJshfyMM86IF198scrYyJEj49BDD41rr702unXrFs2aNYs5c+bE8OHDIyLilVdeiZUrV8aAAQNSRAYAAICcSVbI27RpE0cccUSVsVatWsUBBxxQOX7xxRfH6NGjo3379lFUVBRXXnllDBgwIE488cQUkQEAACBnkhXyurj11lujSZMmMXz48KioqIjBgwfHHXfckToWAAAA7LEGVcifeuqpKvcLCwtj2rRpMW3atDSBAAAAYC9Jfh1yAAAA2B8p5AAAAJCAQg4AAAAJKOQAAACQgEIOAAAACSjkAAAAkIBCDgAAAAko5AAAAJCAQg4AAAAJKOQAAACQgEIOAAAACSjkAAAAkIBCDgAAAAnkpw4AALlSOmZm6ggAAHVmhRwAAAASUMgBAAAgAYUcAAAAElDIAQAAIAGFHAAAABJQyAEAACABhRwAAAASUMgBAAAgAYUcAAAAEshPHQAAIiJKx8xMHaFBycXPY8XkoTlIAgDsLVbIAQAAIAGFHAAAABJQyAEAACABhRwAAAASUMgBAAAgAYUcAAAAElDIAQAAIAGFHAAAABJQyAEAACABhRwAAAASUMgBAAAgAYUcAAAAElDIAQAAIAGFHAAAABJQyAEAACABhRwAAAASUMgBAAAgAYUcAAAAElDIAQAAIAGFHAAAABLITx0AIIXSMTP3eI4Vk4fmIAkAAPsrK+QAAACQgEIOAAAACSjkAAAAkIBCDgAAAAko5AAAAJCAQg4AAAAJKOQAAACQgEIOAAAACSjkAAAAkIBCDgAAAAko5AAAAJCAQg4AAAAJKOQAAACQgEIOAAAACSjkAAAAkIBCDgAAAAko5AAAAJCAQg4AAAAJKOQAAACQgEIOAAAACSjkAAAAkIBCDgAAAAko5AAAAJCAQg4AAAAJKOQAAACQgEIOAAAACSjkAAAAkIBCDgAAAAko5AAAAJCAQg4AAAAJKOQAAACQgEIOAAAACSjkAAAAkIBCDgAAAAko5AAAAJCAQg4AAAAJKOQAAACQgEIOAAAACSjkAAAAkIBCDgAAAAnkpw4AwJ4pHTNzj+dYMXloDpIAALA7rJADAABAAgo5AAAAJKCQAwAAQAIKOQAAACSgkAMAAEACSQv5nXfeGUcddVQUFRVFUVFRDBgwIB577LHKxzdt2hSjRo2KAw44IFq3bh3Dhw+P8vLyhIkBAAAgN5IW8oMOOigmT54cixcvjkWLFsXpp58ew4YNi5deeikiIq6++ur41a9+FQ899FDMmzcvVq9eHeedd17KyAAAAJATSa9Dfs4551S5P3HixLjzzjtjwYIFcdBBB8Xdd98d999/f5x++ukRETFjxow47LDDYsGCBXHiiSemiAwAAAA50WC+Q75t27Z44IEHYuPGjTFgwIBYvHhxbNmyJQYNGlS5zaGHHhrdu3eP+fPn1zpPRUVFbNiwocoNAAAAGpqkK+QRES+++GIMGDAgNm3aFK1bt45f/vKX0bdv31iyZEk0b9482rZtW2X7kpKSKCsrq3W+SZMmxYQJE/ZyagCgrkrHzNzjOVZMHpqDJADQsCRfIT/kkENiyZIlsXDhwrjssstixIgRsXTp0nrPN3bs2Fi/fn3lbdWqVTlMCwAAALmRfIW8efPm0bt374iI6NevXzz33HNx2223xfnnnx+bN2+OdevWVVklLy8vj86dO9c6X0FBQRQUFOzt2AAAALBHkq+Qv9/27dujoqIi+vXrF82aNYs5c+ZUPvbKK6/EypUrY8CAAQkTAgAAwJ5LukI+duzYGDJkSHTv3j3eeeeduP/+++Opp56KJ554IoqLi+Piiy+O0aNHR/v27aOoqCiuvPLKGDBggDOsAwAAsM9LWsjXrl0bX/ziF2PNmjVRXFwcRx11VDzxxBNx5plnRkTErbfeGk2aNInhw4dHRUVFDB48OO64446UkQEAACAnkhbyu+++e6ePFxYWxrRp02LatGkfUCIAAAD4YDS475ADAADA/kAhBwAAgAQUcgAAAEgg+XXIAaCxKR0zM3UEAGAfYIUcAAAAElDIAQAAIAGFHAAAABJQyAEAACABhRwAAAASUMgBAAAgAYUcAAAAElDIAQAAIAGFHAAAABLITx0AYH9WOmZm6ggAACRihRwAAAASUMgBAAAgAYUcAAAAElDIAQAAIAGFHAAAABJQyAEAACABhRwAAAASUMgBAAAgAYUcAAAAEshPHQAAaLhKx8xMHQEAGi0r5AAAAJCAQg4AAAAJKOQAAACQgEIOAAAACSjkAAAAkIBCDgAAAAko5AAAAJCAQg4AAAAJKOQAAACQgEIOAAAACeSnDgBAeqVjZqaOwF7g9woADZsVcgAAAEig3ivkGzdujHnz5sXKlStj8+bNVR776le/usfBAAAAoDGrVyF/4YUX4mMf+1i89957sXHjxmjfvn28+eab0bJly+jUqZNCDgAAALtQr4+sX3311XHOOefE3/72t2jRokUsWLAgXn/99ejXr19897vfzXVGAAAAaHTqVciXLFkSX//616NJkybRtGnTqKioiG7dusUtt9wS1113Xa4zAgAAQKNTr0LerFmzaNLkH0/t1KlTrFy5MiIiiouLY9WqVblLBwAAAI1Uvb5Dfuyxx8Zzzz0Xffr0iVNOOSVuuOGGePPNN+PHP/5xHHHEEbnOCAAAAI1OvVbIb7755ujSpUtEREycODHatWsXl112Wbzxxhvx/e9/P6cBAQAAoDGq1wr58ccfX/nfnTp1iscffzxngQAAAGB/UO/rkAPs70rHzEwdAQCAfVidC/lxxx0Xc+bMiXbt2sWxxx4beXl5tW77/PPP5yQcAAAANFZ1LuTDhg2LgoKCiIg499xz91YeAAAA2C/UuZCPGzeuxv8GAAAAdl+9zrL+3HPPxcKFC6uNL1y4MBYtWrTHoQAAAKCxq1chHzVqVKxatara+P/8z//EqFGj9jgUAAAANHb1KuRLly6N4447rtr4scceG0uXLt3jUAAAANDY1auQFxQURHl5ebXxNWvWRH6+K6kBAADArtSrkJ911lkxduzYWL9+feXYunXr4rrrroszzzwzZ+EAAACgsarXcvZ3v/vdOPnkk6NHjx5x7LHHRkTEkiVLoqSkJH784x/nNCAAAAA0RvUq5AceeGD84Q9/iJ/+9Kfx+9//Plq0aBEjR46Mz372s9GsWbNcZwQAAIBGp95f+G7VqlV8+ctfzmUWAAAA2G/Uu5C/+uqrMXfu3Fi7dm1s3769ymM33HDDHgcDAACAxqxehfwHP/hBXHbZZdGhQ4fo3Llz5OXlVT6Wl5enkAMAAMAu1KuQ33TTTTFx4sS49tprc50HAAAA9gv1uuzZ3/72t/j0pz+d6ywAAACw36hXIf/0pz8ds2bNynUWAAAA2G/U6yPrvXv3juuvvz4WLFgQRx55ZLVLnX31q1/NSTgAAABorPKyLMt290k9e/asfcK8vPjLX/6yR6FyacOGDVFcXBzr16+PoqKi1HGABqJ0zMzUEYDdsGLy0NQRAKDO6tpD67VCvnz58noHAwAAAPbgOuQREZs3b47ly5dHr169Ij9/j6YC9rJcrAhboQJS8R4GQGNUr5O6vffee3HxxRdHy5Yt4/DDD4+VK1dGRMSVV14ZkydPzmlAAAAAaIzqVcjHjh0bv//97+Opp56KwsLCyvFBgwbFgw8+mLNwAAAA0FjV63PmjzzySDz44INx4oknRl5eXuX44YcfHsuWLctZOAAAAGis6rVC/sYbb0SnTp2qjW/cuLFKQQcAAABqVq9Cfvzxx8fMmf93cpUdJfyHP/xhDBgwIDfJAAAAoBGr10fWb7755hgyZEgsXbo0tm7dGrfddlssXbo0nn322Zg3b16uMwIAAECjU68V8oEDB8aSJUti69atceSRR8asWbOiU6dOMX/+/OjXr1+uMwIAAECjU++Lh/fq1St+8IMf5DILAAAA7DfqVch3XHe8Nt27d69XGAAAANhf1KuQl5aW7vRs6tu2bat3IAAAANgf1KuQv/DCC1Xub9myJV544YWYMmVKTJw4MSfBAGpTOmbmrjcCAIAGrl6F/Oijj642dvzxx0fXrl3jX//1X+O8887b42AAAADQmNXrLOu1OeSQQ+K5557L5ZQAAADQKNVrhXzDhg1V7mdZFmvWrInx48dHnz59chIMAAAAGrN6FfK2bdtWO6lblmXRrVu3eOCBB3ISDAAAABqzehXyJ598skohb9KkSXTs2DF69+4d+fn1vrQ5AAAA7Dfq1Z5PPfXUHMcAAACA/Uu9Tuo2adKkuOeee6qN33PPPfGd73xnj0MBAABAY1evQn7XXXfFoYceWm388MMPj+nTp+9xKAAAAGjs6lXIy8rKokuXLtXGO3bsGGvWrNnjUAAAANDY1es75N26dYtnnnkmevbsWWX8mWeeia5du+YkGNDwlI6ZucdzrJg8NAdJAABg31evQn7ppZfGVVddFVu2bInTTz89IiLmzJkT11xzTXz961/PaUAAAABojOpVyL/xjW/EW2+9FZdffnls3rw5IiIKCwvj2muvjbFjx+Y0IAAAADRG9SrkeXl58Z3vfCeuv/76ePnll6NFixbRp0+fKCgoyHU+AAAAaJTqdVK3HcrKyuLtt9+OXr16RUFBQWRZlqtcAAAA0KjVq5C/9dZbccYZZ8SHPvSh+NjHPlZ5ZvWLL77Yd8gBAACgDupVyK+++upo1qxZrFy5Mlq2bFk5fv7558fjjz9e53kmTZoUH/7wh6NNmzbRqVOnOPfcc+OVV16pss2mTZti1KhRccABB0Tr1q1j+PDhUV5eXp/YAAAA0GDUq5DPmjUrvvOd78RBBx1UZbxPnz7x+uuv13meefPmxahRo2LBggUxe/bs2LJlS5x11lmxcePGym2uvvrq+NWvfhUPPfRQzJs3L1avXh3nnXdefWIDAABAg1Gvk7pt3Lixysr4Dm+//fZundjt/avp9957b3Tq1CkWL14cJ598cqxfvz7uvvvuuP/++ysvrzZjxow47LDDYsGCBXHiiSfWJz4AAAAkV68V8pNOOinuu+++yvt5eXmxffv2uOWWW+K0006rd5j169dHRET79u0jImLx4sWxZcuWGDRoUOU2hx56aHTv3j3mz59f4xwVFRWxYcOGKjcAAABoaOq1Qn7LLbfEGWecEYsWLYrNmzfHNddcEy+99FK8/fbb8cwzz9QryPbt2+Oqq66Kj370o3HEEUdExD/O4t68efNo27ZtlW1LSkqirKysxnkmTZoUEyZMqFcGAAAA+KDUa4X8iCOOiD//+c8xcODAGDZsWGzcuDHOO++8eOGFF6JXr171CjJq1Kj44x//GA888EC9nr/D2LFjY/369ZW3VatW7dF8AAAAsDfs9gr5li1b4uyzz47p06fHN7/5zZyEuOKKK+LXv/51PP3001VOFNe5c+fYvHlzrFu3rsoqeXl5eXTu3LnGuQoKCnbre+wAAACQwm6vkDdr1iz+8Ic/5GTnWZbFFVdcEb/85S/jySefjJ49e1Z5vF+/ftGsWbOYM2dO5dgrr7wSK1eujAEDBuQkAwAAAKRQr4+sf/7zn4+77757j3c+atSo+MlPfhL3339/tGnTJsrKyqKsrCz+/ve/R0REcXFxXHzxxTF69OiYO3duLF68OEaOHBkDBgxwhnUAAAD2afU6qdvWrVvjnnvuid/85jfRr1+/aNWqVZXHp0yZUqd57rzzzoiIOPXUU6uMz5gxIy666KKIiLj11lujSZMmMXz48KioqIjBgwfHHXfcUZ/YAAAA0GDsViH/y1/+EqWlpfHHP/4xjjvuuIiI+POf/1xlm7y8vDrPl2XZLrcpLCyMadOmxbRp03YnKgAAADRou1XI+/TpE2vWrIm5c+dGRMT5558ft99+e5SUlOyVcAAAANBY7dZ3yN+/ov3YY4/Fxo0bcxoIAAAA9gf1OqnbDnX5yDkAAABQ3W59ZD0vL6/ad8R35zvjAKVjZqaOAFBve/oetmLy0BwlAaAx2K1CnmVZXHTRRVFQUBAREZs2bYp/+Zd/qXaW9V/84he5SwgAAACN0G4V8hEjRlS5//nPfz6nYQAAAGB/sVuFfMaMGXsrBwAAAOxX9uikbgAAAED9KOQAAACQgEIOAAAACSjkAAAAkIBCDgAAAAko5AAAAJCAQg4AAAAJ7NZ1yAEA9lWlY2amjgAAVVghBwAAgAQUcgAAAEhAIQcAAIAEFHIAAABIQCEHAACABBRyAAAASEAhBwAAgAQUcgAAAEhAIQcAAIAE8lMHAADYX5SOmbnHc6yYPDQHSQBoCKyQAwAAQAIKOQAAACSgkAMAAEACCjkAAAAkoJADAABAAgo5AAAAJKCQAwAAQAIKOQAAACSgkAMAAEAC+akDAABQd6VjZu7xHCsmD81BEgD2lBVyAAAASEAhBwAAgAQUcgAAAEhAIQcAAIAEFHIAAABIQCEHAACABBRyAAAASEAhBwAAgAQUcgAAAEhAIQcAAIAEFHIAAABIQCEHAACABBRyAAAASEAhBwAAgAQUcgAAAEhAIQcAAIAEFHIAAABIQCEHAACABBRyAAAASCA/dQBg10rHzEwdAQAAyDEr5AAAAJCAQg4AAAAJKOQAAACQgEIOAAAACSjkAAAAkIBCDgAAAAko5AAAAJCAQg4AAAAJKOQAAACQgEIOAAAACSjkAAAAkIBCDgAAAAko5AAAAJCAQg4AAAAJKOQAAACQgEIOAAAACSjkAAAAkIBCDgAAAAko5AAAAJCAQg4AAAAJKOQAAACQgEIOAAAACSjkAAAAkIBCDgAAAAko5AAAAJCAQg4AAAAJKOQAAACQgEIOAAAACSjkAAAAkIBCDgAAAAnkpw4A+4PSMTNTRwAAABoYK+QAAACQgEIOAAAACSjkAAAAkIBCDgAAAAko5AAAAJCAQg4AAAAJJC3kTz/9dJxzzjnRtWvXyMvLi0ceeaTK41mWxQ033BBdunSJFi1axKBBg+LVV19NExYAAAByKGkh37hxYxx99NExbdq0Gh+/5ZZb4vbbb4/p06fHwoULo1WrVjF48ODYtGnTB5wUAAAAcis/5c6HDBkSQ4YMqfGxLMti6tSp8a1vfSuGDRsWERH33XdflJSUxCOPPBIXXHBBjc+rqKiIioqKyvsbNmzIfXAAAADYQw32O+TLly+PsrKyGDRoUOVYcXFx9O/fP+bPn1/r8yZNmhTFxcWVt27dun0QcQEAAGC3NNhCXlZWFhERJSUlVcZLSkoqH6vJ2LFjY/369ZW3VatW7dWcAAAAUB9JP7K+NxQUFERBQUHqGAAAALBTDXaFvHPnzhERUV5eXmW8vLy88jEAAADYVzXYQt6zZ8/o3LlzzJkzp3Jsw4YNsXDhwhgwYEDCZAAAALDnkn5k/d13343XXnut8v7y5ctjyZIl0b59++jevXtcddVVcdNNN0WfPn2iZ8+ecf3110fXrl3j3HPPTRcaAAAAciBpIV+0aFGcdtpplfdHjx4dEREjRoyIe++9N6655prYuHFjfPnLX45169bFwIED4/HHH4/CwsJUkQEAACAn8rIsy1KH2Js2bNgQxcXFsX79+igqKkodh/1U6ZiZqSMAQKUVk4emjgDQqNW1hzbY75ADAABAY6aQAwAAQAIKOQAAACSQ9KRuAADsm3JxfhTfZQf2d1bIAQAAIAGFHAAAABJQyAEAACABhRwAAAASUMgBAAAgAYUcAAAAElDIAQAAIAGFHAAAABJQyAEAACABhRwAAAASUMgBAAAgAYUcAAAAElDIAQAAIAGFHAAAABJQyAEAACABhRwAAAASUMgBAAAgAYUcAAAAElDIAQAAIIH81AGgoSsdMzN1BACgFrn4d3rF5KE5SAKw+6yQAwAAQAIKOQAAACSgkAMAAEACCjkAAAAkoJADAABAAgo5AAAAJKCQAwAAQAIKOQAAACSgkAMAAEAC+akDwN5UOmZm6ggAAAA1skIOAAAACSjkAAAAkIBCDgAAAAko5AAAAJCAQg4AAAAJKOQAAACQgEIOAAAACSjkAAAAkIBCDgAAAAko5AAAAJBAfuoAAAB8sErHzEwdAYCwQg4AAABJKOQAAACQgEIOAAAACSjkAAAAkIBCDgAAAAko5AAAAJCAQg4AAAAJKOQAAACQgEIOAAAACeSnDkDjVDpmZuoIAEAD15j+XsjFa1kxeWgOkgD7EivkAAAAkIBCDgAAAAko5AAAAJCAQg4AAAAJKOQAAACQgEIOAAAACSjkAAAAkIBCDgAAAAko5AAAAJBAfuoA/J/SMTNTR4iIiBWTh6aOAABAInv6N2ku/pbMxd/F/qZlX2CFHAAAABJQyAEAACABhRwAAAASUMgBAAAgAYUcAAAAElDIAQAAIAGFHAAAABJQyAEAACABhRwAAAASyE8dAAAAUiodMzN1hIhoODn4P7n4nayYPDQHSWisrJADAABAAgo5AAAAJKCQAwAAQAIKOQAAACSgkAMAAEACCjkAAAAkoJADAABAAgo5AAAAJKCQAwAAQAL5qQPQ8JSOmZk6AgAA8L8ayt/nKyYP3eM5cvFacpGjobBCDgAAAAko5AAAAJCAQg4AAAAJKOQAAACQgEIOAAAACSjkAAAAkMA+UcinTZsWpaWlUVhYGP3794/f/e53qSMBAADAHmnwhfzBBx+M0aNHx7hx4+L555+Po48+OgYPHhxr165NHQ0AAADqrcEX8ilTpsSll14aI0eOjL59+8b06dOjZcuWcc8996SOBgAAAPWWnzrAzmzevDkWL14cY8eOrRxr0qRJDBo0KObPn1/jcyoqKqKioqLy/vr16yMiYsOGDXs3bA5sr3gvdQQAANgjufi7Oxd/F8uRew3ltewL3W5HxizLdrpdgy7kb775Zmzbti1KSkqqjJeUlMSf/vSnGp8zadKkmDBhQrXxbt267ZWMAADA/ymemjrBP8iRew3ltTSUHHXxzjvvRHFxca2PN+hCXh9jx46N0aNHV97fvn17vP3223HAAQdEXl7eXtvvhg0bolu3brFq1aooKiraa/uBXHLcsi9y3LKvcuyyL3Lcsi9qCMdtlmXxzjvvRNeuXXe6XYMu5B06dIimTZtGeXl5lfHy8vLo3Llzjc8pKCiIgoKCKmNt27bdWxGrKSoq8mbFPsdxy77Iccu+yrHLvshxy74o9XG7s5XxHRr0Sd2aN28e/fr1izlz5lSObd++PebMmRMDBgxImAwAAAD2TINeIY+IGD16dIwYMSKOP/74OOGEE2Lq1KmxcePGGDlyZOpoAAAAUG8NvpCff/758cYbb8QNN9wQZWVlccwxx8Tjjz9e7URvqRUUFMS4ceOqfVweGjLHLfsixy37Kscu+yLHLfuifem4zct2dR52AAAAIOca9HfIAQAAoLFSyAEAACABhRwAAAASUMgBAAAgAYU8ByZOnBgf+chHomXLltG2bdsat1m5cmUMHTo0WrZsGZ06dYpvfOMbsXXr1g82KOxEaWlp5OXlVblNnjw5dSyoZtq0aVFaWhqFhYXRv3//+N3vfpc6EtRq/Pjx1d5bDz300NSxoJqnn346zjnnnOjatWvk5eXFI488UuXxLMvihhtuiC5dukSLFi1i0KBB8eqrr6YJC/9rV8ftRRddVO09+Oyzz04TthYKeQ5s3rw5Pv3pT8dll11W4+Pbtm2LoUOHxubNm+PZZ5+NH/3oR3HvvffGDTfc8AEnhZ278cYbY82aNZW3K6+8MnUkqOLBBx+M0aNHx7hx4+L555+Po48+OgYPHhxr165NHQ1qdfjhh1d5b/3tb3+bOhJUs3Hjxjj66KNj2rRpNT5+yy23xO233x7Tp0+PhQsXRqtWrWLw4MGxadOmDzgp/J9dHbcREWeffXaV9+D/+I//+AAT7lqDvw75vmDChAkREXHvvffW+PisWbNi6dKl8Zvf/CZKSkrimGOOiW9/+9tx7bXXxvjx46N58+YfYFqoXZs2baJz586pY0CtpkyZEpdeemmMHDkyIiKmT58eM2fOjHvuuSfGjBmTOB3ULD8/33srDd6QIUNiyJAhNT6WZVlMnTo1vvWtb8WwYcMiIuK+++6LkpKSeOSRR+KCCy74IKNCpZ0dtzsUFBQ06PdgK+QfgPnz58eRRx4ZJSUllWODBw+ODRs2xEsvvZQwGVQ1efLkOOCAA+LYY4+Nf/3Xf/W1ChqUzZs3x+LFi2PQoEGVY02aNIlBgwbF/PnzEyaDnXv11Veja9eucfDBB8eFF14YK1euTB0Jdsvy5cujrKysyvtvcXFx9O/f3/svDd5TTz0VnTp1ikMOOSQuu+yyeOutt1JHqsIK+QegrKysShmPiMr7ZWVlKSJBNV/96lfjuOOOi/bt28ezzz4bY8eOjTVr1sSUKVNSR4OIiHjzzTdj27ZtNb6f/ulPf0qUCnauf//+ce+998YhhxwSa9asiQkTJsRJJ50Uf/zjH6NNmzap40Gd7Ph7tab3X3/L0pCdffbZcd5550XPnj1j2bJlcd1118WQIUNi/vz50bRp09TxIkIhr9WYMWPiO9/5zk63efnll52YhQZtd47j0aNHV44dddRR0bx58/jKV74SkyZNioKCgr0dFaBR+uePUh511FHRv3//6NGjR/zsZz+Liy++OGEygMbvn79OceSRR8ZRRx0VvXr1iqeeeirOOOOMhMn+j0Jei69//etx0UUX7XSbgw8+uE5zde7cudpZgMvLyysfg71lT47j/v37x9atW2PFihVxyCGH7IV0sHs6dOgQTZs2rXz/3KG8vNx7KfuMtm3bxoc+9KF47bXXUkeBOtvxHlteXh5dunSpHC8vL49jjjkmUSrYfQcffHB06NAhXnvtNYW8oevYsWN07NgxJ3MNGDAgJk6cGGvXro1OnTpFRMTs2bOjqKgo+vbtm5N9QE325DhesmRJNGnSpPKYhdSaN28e/fr1izlz5sS5554bERHbt2+POXPmxBVXXJE2HNTRu+++G8uWLYsvfOELqaNAnfXs2TM6d+4cc+bMqSzgGzZsiIULF9Z6lSFoiP7617/GW2+9VeV/LKWmkOfAypUr4+23346VK1fGtm3bYsmSJRER0bt372jdunWcddZZ0bdv3/jCF74Qt9xyS5SVlcW3vvWtGDVqlI8C0yDMnz8/Fi5cGKeddlq0adMm5s+fH1dffXV8/vOfj3bt2qWOB5VGjx4dI0aMiOOPPz5OOOGEmDp1amzcuLHyrOvQ0Py///f/4pxzzokePXrE6tWrY9y4cdG0adP47Gc/mzoaVPHuu+9W+eTG8uXLY8mSJdG+ffvo3r17XHXVVXHTTTdFnz59omfPnnH99ddH165dK/8HKaSws+O2ffv2MWHChBg+fHh07tw5li1bFtdcc0307t07Bg8enDD1+2TssREjRmQRUe02d+7cym1WrFiRDRkyJGvRokXWoUOH7Otf/3q2ZcuWdKHhnyxevDjr379/VlxcnBUWFmaHHXZYdvPNN2ebNm1KHQ2q+fd///ese/fuWfPmzbMTTjghW7BgQepIUKvzzz8/69KlS9a8efPswAMPzM4///zstddeSx0Lqpk7d26Nf8+OGDEiy7Is2759e3b99ddnJSUlWUFBQXbGGWdkr7zyStrQ7Pd2dty+99572VlnnZV17Ngxa9asWdajR4/s0ksvzcrKylLHriIvy7Iswf8HAAAAgP2a65ADAABAAgo5AAAAJKCQAwAAQAIKOQAAACSgkAMAAEACCjkAAAAkoJADAABAAgo5AAAAJKCQAwAAQAIKOQA0YhdddFHk5eVVu5199tmpowHAfi8/dQAAYO86++yzY8aMGVXGCgoKatx2y5Yt0axZsypjmzdvjubNm+/2fuv7PADYX1ghB4BGrqCgIDp37lzl1q5du4iIyMvLizvvvDM+8YlPRKtWrWLixIkxfvz4OOaYY+KHP/xh9OzZMwoLCyMiYuXKlTFs2LBo3bp1FBUVxWc+85koLy+v3E9tzwMAaqaQA8B+bvz48fHJT34yXnzxxfjSl74UERGvvfZa/PznP49f/OIXsWTJkti+fXsMGzYs3n777Zg3b17Mnj07/vKXv8T5559fZa73Pw8AqJ2PrANAI/frX/86WrduXWXsuuuui+uuuy4iIj73uc/FyJEjqzy+efPmuO+++6Jjx44RETF79ux48cUXY/ny5dGtW7eIiLjvvvvi8MMPj+eeey4+/OEP1/g8AKB2CjkANHKnnXZa3HnnnVXG2rdvX/nfxx9/fLXn9OjRo0qpfvnll6Nbt26VZTwiom/fvtG2bdt4+eWXKwv5+58HANROIQeARq5Vq1bRu3fvnT5el7G67gsAqBvfIQcAdumwww6LVatWxapVqyrHli5dGuvWrYu+ffsmTAYA+y4r5ADQyFVUVERZWVmVsfz8/OjQoUOd5xg0aFAceeSRceGFF8bUqVNj69atcfnll8cpp5xS40feAYBds0IOAI3c448/Hl26dKlyGzhw4G7NkZeXF48++mi0a9cuTj755Bg0aFAcfPDB8eCDD+6l1ADQ+OVlWZalDgEAAAD7GyvkAAAAkIBCDgAAAAko5AAAAJCAQg4AAAAJKOQAAACQgEIOAAAACSjkAAAAkIBCDgAAAAko5AAAAJCAQg4AAAAJKOQAAACQwP8HTusX3WnFCbQAAAAASUVORK5CYII=\n"},"metadata":{}}]}]}