Pronostictemperature1D.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyNlTJfB+GMrsUpmv/L1h6Xl"},"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":"EeHVlTScEEDN","executionInfo":{"status":"ok","timestamp":1719447787280,"user_tz":300,"elapsed":126680,"user":{"displayName":"subenomai haku","userId":"01968901610098885489"}},"outputId":"4171a8d8-1d3f-43c9-b48b-099fed83ad0a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","91/91 [==============================] - 7s 11ms/step - loss: 0.0323 - val_loss: 0.0115\n","Epoch 2/100\n","91/91 [==============================] - 1s 7ms/step - loss: 0.0120 - val_loss: 0.0115\n","Epoch 3/100\n","91/91 [==============================] - 1s 6ms/step - loss: 0.0103 - val_loss: 0.0080\n","Epoch 4/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0094 - val_loss: 0.0073\n","Epoch 5/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0090 - val_loss: 0.0074\n","Epoch 6/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0094 - val_loss: 0.0077\n","Epoch 7/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0089 - val_loss: 0.0082\n","Epoch 8/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0089 - val_loss: 0.0075\n","Epoch 9/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0087 - val_loss: 0.0073\n","Epoch 10/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0087 - val_loss: 0.0082\n","Epoch 11/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0090 - val_loss: 0.0071\n","Epoch 12/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0089 - val_loss: 0.0075\n","Epoch 13/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0087 - val_loss: 0.0079\n","Epoch 14/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0086 - val_loss: 0.0075\n","Epoch 15/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0086 - val_loss: 0.0076\n","Epoch 16/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0085 - val_loss: 0.0070\n","Epoch 17/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0085 - val_loss: 0.0085\n","Epoch 18/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0087 - val_loss: 0.0083\n","Epoch 19/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0086 - val_loss: 0.0077\n","Epoch 20/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0084 - val_loss: 0.0070\n","Epoch 21/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0084 - val_loss: 0.0074\n","Epoch 22/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0086 - val_loss: 0.0071\n","Epoch 23/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0084 - val_loss: 0.0075\n","Epoch 24/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0083 - val_loss: 0.0076\n","Epoch 25/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0089 - val_loss: 0.0070\n","Epoch 26/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0083 - val_loss: 0.0070\n","Epoch 27/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0082 - val_loss: 0.0074\n","Epoch 28/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0082 - val_loss: 0.0070\n","Epoch 29/100\n","91/91 [==============================] - 1s 6ms/step - loss: 0.0082 - val_loss: 0.0075\n","Epoch 30/100\n","91/91 [==============================] - 1s 6ms/step - loss: 0.0083 - val_loss: 0.0071\n","Epoch 31/100\n","91/91 [==============================] - 1s 6ms/step - loss: 0.0082 - val_loss: 0.0070\n","Epoch 32/100\n","91/91 [==============================] - 1s 7ms/step - loss: 0.0083 - val_loss: 0.0100\n","Epoch 33/100\n","91/91 [==============================] - 1s 6ms/step - loss: 0.0082 - val_loss: 0.0074\n","Epoch 34/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0081 - val_loss: 0.0072\n","Epoch 35/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0081 - val_loss: 0.0072\n","Epoch 36/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0080 - val_loss: 0.0073\n","Epoch 37/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0080 - val_loss: 0.0071\n","Epoch 38/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0079 - val_loss: 0.0079\n","Epoch 39/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0081 - val_loss: 0.0073\n","Epoch 40/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0079 - val_loss: 0.0077\n","Epoch 41/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0078 - val_loss: 0.0076\n","Epoch 42/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0079 - val_loss: 0.0079\n","Epoch 43/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0078 - val_loss: 0.0073\n","Epoch 44/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0077 - val_loss: 0.0070\n","Epoch 45/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0076 - val_loss: 0.0082\n","Epoch 46/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0078 - val_loss: 0.0072\n","Epoch 47/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0077 - val_loss: 0.0076\n","Epoch 48/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0077 - val_loss: 0.0075\n","Epoch 49/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0078 - val_loss: 0.0073\n","Epoch 50/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0077 - val_loss: 0.0080\n","Epoch 51/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0075 - val_loss: 0.0076\n","Epoch 52/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0076 - val_loss: 0.0076\n","Epoch 53/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0075 - val_loss: 0.0079\n","Epoch 54/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0074 - val_loss: 0.0074\n","Epoch 55/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0073 - val_loss: 0.0081\n","Epoch 56/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0074 - val_loss: 0.0073\n","Epoch 57/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0072 - val_loss: 0.0078\n","Epoch 58/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0073 - val_loss: 0.0081\n","Epoch 59/100\n","91/91 [==============================] - 1s 6ms/step - loss: 0.0073 - val_loss: 0.0080\n","Epoch 60/100\n","91/91 [==============================] - 1s 6ms/step - loss: 0.0072 - val_loss: 0.0078\n","Epoch 61/100\n","91/91 [==============================] - 1s 6ms/step - loss: 0.0072 - val_loss: 0.0079\n","Epoch 62/100\n","91/91 [==============================] - 1s 7ms/step - loss: 0.0074 - val_loss: 0.0084\n","Epoch 63/100\n","91/91 [==============================] - 1s 6ms/step - loss: 0.0072 - val_loss: 0.0077\n","Epoch 64/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0072 - val_loss: 0.0080\n","Epoch 65/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0071 - val_loss: 0.0084\n","Epoch 66/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0070 - val_loss: 0.0104\n","Epoch 67/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0072 - val_loss: 0.0080\n","Epoch 68/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0071 - val_loss: 0.0085\n","Epoch 69/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0071 - val_loss: 0.0083\n","Epoch 70/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0070 - val_loss: 0.0080\n","Epoch 71/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0069 - val_loss: 0.0081\n","Epoch 72/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0070 - val_loss: 0.0078\n","Epoch 73/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0069 - val_loss: 0.0082\n","Epoch 74/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0069 - val_loss: 0.0080\n","Epoch 75/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0069 - val_loss: 0.0081\n","Epoch 76/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0068 - val_loss: 0.0080\n","Epoch 77/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0067 - val_loss: 0.0081\n","Epoch 78/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0068 - val_loss: 0.0081\n","Epoch 79/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0066 - val_loss: 0.0095\n","Epoch 80/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0066 - val_loss: 0.0095\n","Epoch 81/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0067 - val_loss: 0.0082\n","Epoch 82/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0065 - val_loss: 0.0087\n","Epoch 83/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0067 - val_loss: 0.0089\n","Epoch 84/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0066 - val_loss: 0.0089\n","Epoch 85/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0066 - val_loss: 0.0090\n","Epoch 86/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0065 - val_loss: 0.0080\n","Epoch 87/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0065 - val_loss: 0.0088\n","Epoch 88/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0066 - val_loss: 0.0092\n","Epoch 89/100\n","91/91 [==============================] - 1s 7ms/step - loss: 0.0064 - val_loss: 0.0084\n","Epoch 90/100\n","91/91 [==============================] - 1s 7ms/step - loss: 0.0065 - val_loss: 0.0093\n","Epoch 91/100\n","91/91 [==============================] - 1s 7ms/step - loss: 0.0063 - val_loss: 0.0084\n","Epoch 92/100\n","91/91 [==============================] - 1s 7ms/step - loss: 0.0063 - val_loss: 0.0079\n","Epoch 93/100\n","91/91 [==============================] - 1s 7ms/step - loss: 0.0064 - val_loss: 0.0088\n","Epoch 94/100\n","91/91 [==============================] - 1s 8ms/step - loss: 0.0062 - val_loss: 0.0085\n","Epoch 95/100\n","91/91 [==============================] - 1s 7ms/step - loss: 0.0061 - val_loss: 0.0086\n","Epoch 96/100\n","91/91 [==============================] - 1s 8ms/step - loss: 0.0061 - val_loss: 0.0093\n","Epoch 97/100\n","91/91 [==============================] - 1s 5ms/step - loss: 0.0064 - val_loss: 0.0091\n","Epoch 98/100\n","91/91 [==============================] - 0s 5ms/step - loss: 0.0061 - val_loss: 0.0089\n","Epoch 99/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0060 - val_loss: 0.0088\n","Epoch 100/100\n","91/91 [==============================] - 0s 4ms/step - loss: 0.0060 - val_loss: 0.0085\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 296ms/step\n","Temperatura predicha para 1991-01-01: 14.36\n","1/1 [==============================] - 0s 18ms/step\n","Temperatura predicha para 1998-01-01: 14.76\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 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["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 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 20ms/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 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"]},{"output_type":"stream","name":"stdout","text":["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 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 29ms/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 17ms/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 25ms/step\n","1/1 [==============================] - 0s 23ms/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 26ms/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 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 22ms/step\n","1/1 [==============================] - 0s 26ms/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 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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 25ms/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 21ms/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"]},{"output_type":"stream","name":"stdout","text":["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 27ms/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 24ms/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 21ms/step\n","1/1 [==============================] - 0s 20ms/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","/usr/local/lib/python3.10/dist-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 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 17ms/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 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"]},{"output_type":"stream","name":"stdout","text":["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"]},{"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 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"]},{"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 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 21ms/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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 19ms/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 17ms/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 25ms/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 17ms/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 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 23ms/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 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 34ms/step\n","1/1 [==============================] - 0s 18ms/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 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"]},{"output_type":"stream","name":"stdout","text":["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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 33ms/step\n","1/1 [==============================] - 0s 26ms/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 35ms/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 30ms/step\n","1/1 [==============================] - 0s 28ms/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 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 38ms/step\n","1/1 [==============================] - 0s 34ms/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 50ms/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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 32ms/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 29ms/step\n","1/1 [==============================] - 0s 45ms/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 30ms/step\n","1/1 [==============================] - 0s 39ms/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 34ms/step\n","1/1 [==============================] - 0s 52ms/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 34ms/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 17ms/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 21ms/step\n","1/1 [==============================] - 0s 17ms/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 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 23ms/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 25ms/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 29ms/step\n","1/1 [==============================] - 0s 19ms/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 21ms/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","/usr/local/lib/python3.10/dist-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 26ms/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 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 27ms/step\n","1/1 [==============================] - 0s 24ms/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 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 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 22ms/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"]},{"output_type":"stream","name":"stdout","text":["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 25ms/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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/step\n","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 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 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 29ms/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 20ms/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"]},{"output_type":"stream","name":"stdout","text":["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 31ms/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 23ms/step\n","1/1 [==============================] - 0s 24ms/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 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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 21ms/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 23ms/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 22ms/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 31ms/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"]},{"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"]},{"output_type":"stream","name":"stdout","text":["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 27ms/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 25ms/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 25ms/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"]},{"output_type":"stream","name":"stdout","text":["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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/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 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 25ms/step\n","1/1 [==============================] - 0s 19ms/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 22ms/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 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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 31ms/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 23ms/step\n","1/1 [==============================] - 0s 24ms/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","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 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"]},{"output_type":"stream","name":"stdout","text":["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 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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 29ms/step\n","1/1 [==============================] - 0s 27ms/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 30ms/step\n","1/1 [==============================] - 0s 28ms/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 25ms/step\n","1/1 [==============================] - 0s 38ms/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 35ms/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 36ms/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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 42ms/step\n","1/1 [==============================] - 0s 33ms/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"]},{"output_type":"stream","name":"stdout","text":["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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 37ms/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 35ms/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"]},{"output_type":"stream","name":"stdout","text":["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 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 21ms/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 18ms/step\n","1/1 [==============================] - 0s 21ms/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 20ms/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 21ms/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 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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/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 17ms/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 20ms/step\n","1/1 [==============================] - 0s 18ms/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 17ms/step\n","1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 17ms/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 21ms/step\n","1/1 [==============================] - 0s 30ms/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 18ms/step\n","1/1 [==============================] - 0s 17ms/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 17ms/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 21ms/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 24ms/step\n","1/1 [==============================] - 0s 21ms/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"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 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.keras.models import Sequential\n","from tensorflow.keras.layers import Dense, Conv1D, Flatten, Concatenate, Input\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.models import Model\n","import matplotlib.pyplot as plt\n","from datetime import datetime, timedelta\n","\n","# Cargar y preparar los datos\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\n","df['day'] = df.index.day\n","df['month'] = df.index.month\n","df['year'] = df.index.year\n","\n","# Normalizar los datos\n","scaler_temp = MinMaxScaler()\n","scaler_features = MinMaxScaler()\n","\n","temp_scaled = scaler_temp.fit_transform(df[['Temp']])\n","features_scaled = scaler_features.fit_transform(df[['day', 'month', 'year']])\n","\n","# Crear secuencias de datos\n","def create_sequences(data, seq_length):\n","    X, y = [], []\n","    for i in range(len(data) - seq_length):\n","        X.append(data[i:i+seq_length])\n","        y.append(data[i+seq_length])\n","    return np.array(X), np.array(y)\n","\n","seq_length = 10\n","X_temp, y = create_sequences(temp_scaled, seq_length)\n","\n","# Dividir los datos en conjuntos de entrenamiento y prueba\n","train_size = int(len(X_temp) * 0.8)\n","X_train_temp, X_test_temp = X_temp[:train_size], X_temp[train_size:]\n","y_train, y_test = y[:train_size], y[train_size:]\n","\n","X_train_features = features_scaled[seq_length:train_size+seq_length]\n","X_test_features = features_scaled[train_size+seq_length:]\n","\n","# Crear el modelo\n","temp_input = Input(shape=(seq_length, 1))\n","features_input = Input(shape=(3,))\n","\n","conv1 = Conv1D(filters=64, kernel_size=3, activation='relu')(temp_input)\n","conv2 = Conv1D(filters=32, kernel_size=3, activation='relu')(conv1)\n","flat = Flatten()(conv2)\n","\n","concat = Concatenate()([flat, features_input])\n","dense1 = Dense(32, activation='relu')(concat)\n","dense2 = Dense(16, activation='relu')(dense1)\n","output = Dense(1)(dense2)\n","\n","model = Model(inputs=[temp_input, features_input], outputs=output)\n","\n","# Compilar el modelo\n","model.compile(optimizer=Adam(learning_rate=0.001), loss='mse')\n","\n","# Entrenar el modelo\n","history = model.fit(\n","    [X_train_temp, X_train_features], y_train,\n","    validation_data=([X_test_temp, X_test_features], y_test),\n","    epochs=100, batch_size=32, 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_features = np.array([[date.day, date.month, date.year]])\n","    input_features_scaled = scaler_features.transform(input_features)\n","\n","    # Obtener los últimos 10 días de temperatura\n","    last_10_days = temp_scaled[-10:].reshape(1, 10, 1)\n","\n","    prediction_scaled = model.predict([last_10_days, input_features_scaled])\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","# Visualizar predicciones vs valores reales\n","dates_to_predict = pd.date_range(start='1991-01-01', end='1991-12-31', 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()"]},{"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":"1Xk4JrxHE2Sp","executionInfo":{"status":"ok","timestamp":1719447883056,"user_tz":300,"elapsed":61083,"user":{"displayName":"subenomai haku","userId":"01968901610098885489"}},"outputId":"c021e043-8336-4598-ac20-20aab327e960"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 19ms/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 17ms/step\n","1/1 [==============================] - 0s 17ms/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 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 17ms/step\n","1/1 [==============================] - 0s 38ms/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 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 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"]},{"output_type":"stream","name":"stdout","text":["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"]},{"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","/usr/local/lib/python3.10/dist-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 25ms/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 20ms/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 17ms/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 17ms/step\n","1/1 [==============================] - 0s 17ms/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 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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","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 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 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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 20ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 16ms/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 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","/usr/local/lib/python3.10/dist-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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/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 23ms/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 17ms/step\n","1/1 [==============================] - 0s 17ms/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 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 26ms/step\n","1/1 [==============================] - 0s 25ms/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 30ms/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"]},{"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 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 17ms/step\n","1/1 [==============================] - 0s 30ms/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 26ms/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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 44ms/step\n","1/1 [==============================] - 0s 35ms/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 25ms/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 24ms/step\n","1/1 [==============================] - 0s 27ms/step\n","1/1 [==============================] - 0s 42ms/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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 33ms/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 28ms/step\n","1/1 [==============================] - 0s 28ms/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 30ms/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 48ms/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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 39ms/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 24ms/step\n","1/1 [==============================] - 0s 17ms/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 17ms/step\n","1/1 [==============================] - 0s 17ms/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 20ms/step\n","1/1 [==============================] - 0s 17ms/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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 24ms/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 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 17ms/step\n","1/1 [==============================] - 0s 17ms/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 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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/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"]},{"output_type":"stream","name":"stdout","text":["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 22ms/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 23ms/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 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"]},{"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 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 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 17ms/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 24ms/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 22ms/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"]},{"output_type":"stream","name":"stdout","text":["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 17ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 21ms/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","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 19ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 17ms/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 20ms/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 18ms/step\n","1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 17ms/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 17ms/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 21ms/step\n","1/1 [==============================] - 0s 17ms/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 20ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 18ms/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","/usr/local/lib/python3.10/dist-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 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 18ms/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 19ms/step\n","1/1 [==============================] - 0s 19ms/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 17ms/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 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 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 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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 18ms/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 25ms/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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 26ms/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 32ms/step\n","1/1 [==============================] - 0s 20ms/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 21ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 17ms/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 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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 23ms/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 17ms/step\n","1/1 [==============================] - 0s 18ms/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 28ms/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 30ms/step\n","1/1 [==============================] - 0s 31ms/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 25ms/step\n","1/1 [==============================] - 0s 26ms/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 26ms/step\n","1/1 [==============================] - 0s 43ms/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 33ms/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 32ms/step\n","1/1 [==============================] - 0s 36ms/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"]},{"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 28ms/step\n","1/1 [==============================] - 0s 25ms/step\n","1/1 [==============================] - 0s 35ms/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 41ms/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 35ms/step\n","1/1 [==============================] - 0s 32ms/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 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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n","1/1 [==============================] - 0s 24ms/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 20ms/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 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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 26ms/step\n","1/1 [==============================] - 0s 18ms/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","/usr/local/lib/python3.10/dist-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 32ms/step\n","1/1 [==============================] - 0s 39ms/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 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 27ms/step\n","1/1 [==============================] - 0s 29ms/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 27ms/step\n","1/1 [==============================] - 0s 30ms/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"]},{"output_type":"stream","name":"stdout","text":["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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 46ms/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 38ms/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 32ms/step\n","1/1 [==============================] - 0s 28ms/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"]},{"output_type":"stream","name":"stdout","text":["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 29ms/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 37ms/step\n","1/1 [==============================] - 0s 52ms/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 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 32ms/step\n","1/1 [==============================] - 0s 23ms/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 20ms/step\n","1/1 [==============================] - 0s 18ms/step\n","1/1 [==============================] - 0s 17ms/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"]},{"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 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 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 20ms/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"]},{"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 98ms/step\n","1/1 [==============================] - 0s 29ms/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 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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 41ms/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 34ms/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 78ms/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 39ms/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 20ms/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 28ms/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 23ms/step\n","1/1 [==============================] - 0s 39ms/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 62ms/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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 40ms/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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 35ms/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 37ms/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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 71ms/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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 36ms/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 38ms/step\n","1/1 [==============================] - 0s 24ms/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 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 19ms/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 32ms/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"]},{"output_type":"stream","name":"stdout","text":["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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 39ms/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 26ms/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 30ms/step\n","1/1 [==============================] - 0s 40ms/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 30ms/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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 38ms/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 46ms/step\n","1/1 [==============================] - 0s 29ms/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 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 36ms/step\n","1/1 [==============================] - 0s 37ms/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 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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 34ms/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 37ms/step\n","1/1 [==============================] - 0s 41ms/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 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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/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 28ms/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 27ms/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 34ms/step\n","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","/usr/local/lib/python3.10/dist-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"]},{"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 26ms/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 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"]},{"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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/step\n","1/1 [==============================] - 0s 20ms/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 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 19ms/step\n","1/1 [==============================] - 0s 18ms/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 21ms/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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/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 21ms/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 27ms/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 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 24ms/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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 23ms/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 23ms/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 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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/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 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 19ms/step\n","1/1 [==============================] - 0s 18ms/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 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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/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 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 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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/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 23ms/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 27ms/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 31ms/step\n","1/1 [==============================] - 0s 22ms/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 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 26ms/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 17ms/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 23ms/step\n","1/1 [==============================] - 0s 24ms/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 20ms/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"]},{"output_type":"stream","name":"stdout","text":["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 21ms/step\n","1/1 [==============================] - 0s 17ms/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 23ms/step\n","1/1 [==============================] - 0s 20ms/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 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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 22ms/step\n","1/1 [==============================] - 0s 38ms/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 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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 45ms/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 37ms/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 36ms/step\n","1/1 [==============================] - 0s 34ms/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 34ms/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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 35ms/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 30ms/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 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 36ms/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 29ms/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 61ms/step\n","1/1 [==============================] - 0s 31ms/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 32ms/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 32ms/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 18ms/step\n","1/1 [==============================] - 0s 27ms/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 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 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 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 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 21ms/step\n","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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 17ms/step\n","1/1 [==============================] - 0s 17ms/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 17ms/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"]},{"output_type":"stream","name":"stdout","text":["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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 28ms/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"]},{"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"]},{"output_type":"stream","name":"stdout","text":["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 36ms/step\n","1/1 [==============================] - 0s 26ms/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 17ms/step\n","1/1 [==============================] - 0s 17ms/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 17ms/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 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 27ms/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 75ms/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 19ms/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 29ms/step\n","1/1 [==============================] - 0s 81ms/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 31ms/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 24ms/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 23ms/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 85ms/step\n","1/1 [==============================] - 0s 19ms/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 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 23ms/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 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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/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 23ms/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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 20ms/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 24ms/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 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"]},{"output_type":"stream","name":"stdout","text":["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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 21ms/step\n","1/1 [==============================] - 0s 22ms/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 23ms/step\n","1/1 [==============================] - 0s 27ms/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 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 21ms/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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 32ms/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"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 35ms/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"]},{"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_temp, X_test_features, y_test, scaler_temp):\n","    # Hacer predicciones\n","    y_pred_scaled = model.predict([X_test_temp, X_test_features])\n","\n","    # Invertir la normalización\n","    y_pred = scaler_temp.inverse_transform(y_pred_scaled)\n","    y_true = scaler_temp.inverse_transform(y_test.reshape(-1, 1))\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('Tiempo')\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('Tiempo')\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","evaluate_model(model, X_test_temp, X_test_features, y_test, scaler_temp)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"S_Gn1wq8FAzj","executionInfo":{"status":"ok","timestamp":1719448076602,"user_tz":300,"elapsed":2332,"user":{"displayName":"subenomai haku","userId":"01968901610098885489"}},"outputId":"5a8a5c73-9461-43aa-b5a1-76ca08b1e4a8"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["23/23 [==============================] - 0s 2ms/step\n","MSE: 5.8860\n","RMSE: 2.4261\n","MAE: 1.9138\n","MAPE: 22.4711%\n","R²: 0.6502\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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\n"},"metadata":{}}]}]}