multiple models predicitons
This commit is contained in:
yann22ahlgrim
@@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 159,
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"execution_count": 92,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -41,7 +41,7 @@
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{
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"cell_type": "code",
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"execution_count": 160,
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"execution_count": 93,
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"metadata": {},
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"outputs": [],
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@@ -66,7 +66,7 @@
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"cell_type": "code",
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"execution_count": 161,
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"execution_count": 94,
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"outputs": [],
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@@ -85,7 +85,7 @@
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"cell_type": "code",
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"execution_count": 162,
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"execution_count": 95,
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"metadata": {},
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"outputs": [],
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@@ -113,7 +113,7 @@
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{
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"cell_type": "code",
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"execution_count": 163,
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"execution_count": 96,
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"outputs": [],
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@@ -146,7 +146,7 @@
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{
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"cell_type": "code",
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"execution_count": 164,
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"execution_count": 97,
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"metadata": {},
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"outputs": [],
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@@ -175,12 +175,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 165,
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"execution_count": 98,
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"metadata": {},
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"outputs": [],
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"source": [
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"grid_params = {\"units1\": [8,16], \"units2\": [16,32], \"units3\": [32,64], \"activation1\": [\"relu\"], \"activation2\": [\"relu\"], \n",
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" \"activation3\": [\"relu\"], \"optimizer\": [Adam], \"learning_rate\": [0.001]}\n",
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"grid_params = {\"units1\": [8], \"units2\": [16,32], \"units3\": [64,128], \"activation1\": [\"relu\"], \"activation2\": [\"relu\"], \n",
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" \"activation3\": [\"relu\"], \"optimizer\": [Adam, RMSprop, SGD], \"learning_rate\": [0.001]}\n",
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"\n",
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"#GridSearch\n",
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"def grid_search_tf_model(X_train: pd.DataFrame, y_train: pd.DataFrame)->Model:\n",
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@@ -218,7 +218,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 166,
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"execution_count": 99,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -255,7 +255,7 @@
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"cell_type": "code",
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"execution_count": 167,
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"execution_count": 100,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -285,7 +285,7 @@
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{
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"cell_type": "code",
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"execution_count": 168,
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"execution_count": 101,
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"metadata": {},
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"outputs": [
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{
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@@ -295,7 +295,7 @@
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"57\n",
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"dataset shape: (617, 57)\n",
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"X shape: (431, 56) and y shape: (431,)\n",
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" Columns with MI equal zero: ['DN', 'CW ', 'CC', 'EG', 'CU', 'AH', 'CL', 'DF', 'CD ', 'GE', 'GB', 'FS', 'DE', 'FI', 'BD ', 'CB', 'FD ', 'DY', 'AY', 'EP', 'AZ', 'EJ', 'CF'] --> total length: 23\n"
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" Columns with MI equal zero: ['DH', 'CC', 'DN', 'BR', 'CL', 'EG', 'CD ', 'AZ', 'BD ', 'CB', 'GB', 'CF', 'EJ', 'CU', 'CW ', 'DE', 'DF', 'DY', 'AB'] --> total length: 19\n"
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]
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}
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],
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@@ -326,165 +326,191 @@
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},
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{
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"cell_type": "code",
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"execution_count": 169,
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"execution_count": 102,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"34\n",
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"dataset shape: (617, 34)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Number of columns: 33\n",
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"38\n",
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"dataset shape: (617, 38)\n",
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"Number of columns: 37\n",
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"Epoch 1/100\n",
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"11/11 [==============================] - 0s 15ms/step - loss: 0.5836 - accuracy: 0.8372 - val_loss: 0.4572 - val_accuracy: 0.8391\n",
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"11/11 [==============================] - 0s 16ms/step - loss: 0.6856 - accuracy: 0.5901 - val_loss: 0.6137 - val_accuracy: 0.7816\n",
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"Epoch 2/100\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.5413 - accuracy: 0.8372 - val_loss: 0.4360 - val_accuracy: 0.8391\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.5953 - accuracy: 0.8227 - val_loss: 0.5401 - val_accuracy: 0.8391\n",
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"Epoch 3/100\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.5107 - accuracy: 0.8372 - val_loss: 0.4228 - val_accuracy: 0.8391\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.5448 - accuracy: 0.8372 - val_loss: 0.4961 - val_accuracy: 0.8391\n",
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"Epoch 4/100\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.4856 - accuracy: 0.8372 - val_loss: 0.4138 - val_accuracy: 0.8391\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.5096 - accuracy: 0.8372 - val_loss: 0.4673 - val_accuracy: 0.8391\n",
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"Epoch 5/100\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.4655 - accuracy: 0.8372 - val_loss: 0.4073 - val_accuracy: 0.8391\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.4853 - accuracy: 0.8372 - val_loss: 0.4464 - val_accuracy: 0.8391\n",
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"Epoch 6/100\n",
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" 1/11 [=>............................] - ETA: 0s - loss: 0.3383 - accuracy: 0.8438Restoring model weights from the end of the best epoch: 1.\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.4464 - accuracy: 0.8372 - val_loss: 0.4030 - val_accuracy: 0.8391\n",
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"Epoch 6: early stopping\n",
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"Epoch 1/100\n",
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"11/11 [==============================] - 0s 15ms/step - loss: 0.6535 - accuracy: 0.7297 - val_loss: 0.5649 - val_accuracy: 0.8391\n",
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"Epoch 2/100\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.5767 - accuracy: 0.8372 - val_loss: 0.4984 - val_accuracy: 0.8391\n",
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"Epoch 3/100\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.5286 - accuracy: 0.8372 - val_loss: 0.4690 - val_accuracy: 0.8391\n",
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"Epoch 4/100\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.4883 - accuracy: 0.8372 - val_loss: 0.4536 - val_accuracy: 0.8391\n",
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"Epoch 5/100\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.4519 - accuracy: 0.8372 - val_loss: 0.4419 - val_accuracy: 0.8391\n",
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"Epoch 6/100\n",
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" 1/11 [=>............................] - ETA: 0s - loss: 0.3956 - accuracy: 0.8438Restoring model weights from the end of the best epoch: 1.\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.4211 - accuracy: 0.8372 - val_loss: 0.4367 - val_accuracy: 0.8391\n",
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"Epoch 6: early stopping\n",
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"Epoch 1/100\n",
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"11/11 [==============================] - 0s 16ms/step - loss: 0.7554 - accuracy: 0.4244 - val_loss: 0.6338 - val_accuracy: 0.6667\n",
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"Epoch 2/100\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.5835 - accuracy: 0.7733 - val_loss: 0.5112 - val_accuracy: 0.8391\n",
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"Epoch 3/100\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.5046 - accuracy: 0.8372 - val_loss: 0.4532 - val_accuracy: 0.8391\n",
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"Epoch 4/100\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.4713 - accuracy: 0.8372 - val_loss: 0.4223 - val_accuracy: 0.8391\n",
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"Epoch 5/100\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.4393 - accuracy: 0.8372 - val_loss: 0.4078 - val_accuracy: 0.8391\n",
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"Epoch 6/100\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.4144 - accuracy: 0.8372 - val_loss: 0.3960 - val_accuracy: 0.8391\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.4643 - accuracy: 0.8372 - val_loss: 0.4334 - val_accuracy: 0.8391\n",
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"Epoch 7/100\n",
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" 1/11 [=>............................] - ETA: 0s - loss: 0.3674 - accuracy: 0.8750Restoring model weights from the end of the best epoch: 2.\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.3898 - accuracy: 0.8401 - val_loss: 0.3847 - val_accuracy: 0.8391\n",
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" 1/11 [=>............................] - ETA: 0s - loss: 0.4852 - accuracy: 0.8125Restoring model weights from the end of the best epoch: 2.\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.4456 - accuracy: 0.8372 - val_loss: 0.4228 - val_accuracy: 0.8391\n",
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"Epoch 7: early stopping\n",
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"Epoch 1/100\n",
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"11/11 [==============================] - 0s 15ms/step - loss: 0.6324 - accuracy: 0.8227 - val_loss: 0.5557 - val_accuracy: 0.8391\n",
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"11/11 [==============================] - 0s 15ms/step - loss: 0.6956 - accuracy: 0.5959 - val_loss: 0.6155 - val_accuracy: 0.8391\n",
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"Epoch 2/100\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.5711 - accuracy: 0.8372 - val_loss: 0.5040 - val_accuracy: 0.8391\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.6004 - accuracy: 0.8256 - val_loss: 0.5349 - val_accuracy: 0.8391\n",
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"Epoch 3/100\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.5245 - accuracy: 0.8372 - val_loss: 0.4676 - val_accuracy: 0.8391\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.5545 - accuracy: 0.8401 - val_loss: 0.4919 - val_accuracy: 0.8391\n",
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"Epoch 4/100\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.4838 - accuracy: 0.8372 - val_loss: 0.4399 - val_accuracy: 0.8391\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.5145 - accuracy: 0.8401 - val_loss: 0.4642 - val_accuracy: 0.8391\n",
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"Epoch 5/100\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.4494 - accuracy: 0.8372 - val_loss: 0.4188 - val_accuracy: 0.8391\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.4748 - accuracy: 0.8459 - val_loss: 0.4483 - val_accuracy: 0.8391\n",
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"Epoch 6/100\n",
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" 1/11 [=>............................] - ETA: 0s - loss: 0.3685 - accuracy: 0.8750Restoring model weights from the end of the best epoch: 1.\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.4130 - accuracy: 0.8372 - val_loss: 0.4003 - val_accuracy: 0.8391\n",
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" 1/11 [=>............................] - ETA: 0s - loss: 0.4367 - accuracy: 0.8750Restoring model weights from the end of the best epoch: 1.\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.4351 - accuracy: 0.8459 - val_loss: 0.4269 - val_accuracy: 0.8391\n",
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"Epoch 6: early stopping\n",
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"Epoch 1/100\n",
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"11/11 [==============================] - 0s 15ms/step - loss: 0.8912 - accuracy: 0.1860 - val_loss: 0.7869 - val_accuracy: 0.2644\n",
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"11/11 [==============================] - 0s 16ms/step - loss: 0.7086 - accuracy: 0.4419 - val_loss: 0.6189 - val_accuracy: 0.8276\n",
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"Epoch 2/100\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.7306 - accuracy: 0.4070 - val_loss: 0.6774 - val_accuracy: 0.6207\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.5895 - accuracy: 0.8459 - val_loss: 0.5260 - val_accuracy: 0.8391\n",
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"Epoch 3/100\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.6506 - accuracy: 0.7936 - val_loss: 0.6167 - val_accuracy: 0.8506\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.5449 - accuracy: 0.8372 - val_loss: 0.4829 - val_accuracy: 0.8391\n",
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"Epoch 4/100\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.6128 - accuracy: 0.8401 - val_loss: 0.5752 - val_accuracy: 0.8391\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.5045 - accuracy: 0.8401 - val_loss: 0.4571 - val_accuracy: 0.8391\n",
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"Epoch 5/100\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.5760 - accuracy: 0.8401 - val_loss: 0.5514 - val_accuracy: 0.8391\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.4679 - accuracy: 0.8430 - val_loss: 0.4363 - val_accuracy: 0.8391\n",
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"Epoch 6/100\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.5444 - accuracy: 0.8401 - val_loss: 0.5272 - val_accuracy: 0.8391\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.4300 - accuracy: 0.8430 - val_loss: 0.4170 - val_accuracy: 0.8391\n",
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"Epoch 7/100\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.5119 - accuracy: 0.8459 - val_loss: 0.4959 - val_accuracy: 0.8391\n",
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"Epoch 8/100\n",
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" 1/11 [=>............................] - ETA: 0s - loss: 0.4723 - accuracy: 0.9062Restoring model weights from the end of the best epoch: 3.\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.4759 - accuracy: 0.8488 - val_loss: 0.4645 - val_accuracy: 0.8391\n",
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"Epoch 8: early stopping\n",
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"Epoch 1/100\n",
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"11/11 [==============================] - 0s 16ms/step - loss: 0.7030 - accuracy: 0.5262 - val_loss: 0.6235 - val_accuracy: 0.7701\n",
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"Epoch 2/100\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.6088 - accuracy: 0.8285 - val_loss: 0.5432 - val_accuracy: 0.8391\n",
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"Epoch 3/100\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.5630 - accuracy: 0.8372 - val_loss: 0.4939 - val_accuracy: 0.8391\n",
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"Epoch 4/100\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.5231 - accuracy: 0.8372 - val_loss: 0.4641 - val_accuracy: 0.8391\n",
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"Epoch 5/100\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.4851 - accuracy: 0.8372 - val_loss: 0.4443 - val_accuracy: 0.8391\n",
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"Epoch 6/100\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.4476 - accuracy: 0.8372 - val_loss: 0.4190 - val_accuracy: 0.8391\n",
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"Epoch 7/100\n",
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" 1/11 [=>............................] - ETA: 0s - loss: 0.4761 - accuracy: 0.7812Restoring model weights from the end of the best epoch: 2.\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.4080 - accuracy: 0.8401 - val_loss: 0.3984 - val_accuracy: 0.8391\n",
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" 1/11 [=>............................] - ETA: 0s - loss: 0.3338 - accuracy: 0.9062Restoring model weights from the end of the best epoch: 2.\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.3985 - accuracy: 0.8605 - val_loss: 0.3954 - val_accuracy: 0.8391\n",
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"Epoch 7: early stopping\n",
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"Epoch 1/100\n",
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"11/11 [==============================] - 0s 15ms/step - loss: 0.8484 - accuracy: 0.2122 - val_loss: 0.7485 - val_accuracy: 0.3218\n",
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"11/11 [==============================] - 0s 16ms/step - loss: 0.6396 - accuracy: 0.8081 - val_loss: 0.5624 - val_accuracy: 0.8391\n",
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"Epoch 2/100\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.7046 - accuracy: 0.5000 - val_loss: 0.6538 - val_accuracy: 0.7471\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.5573 - accuracy: 0.8372 - val_loss: 0.5018 - val_accuracy: 0.8391\n",
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"Epoch 3/100\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.6363 - accuracy: 0.7936 - val_loss: 0.5938 - val_accuracy: 0.8276\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.4990 - accuracy: 0.8372 - val_loss: 0.4580 - val_accuracy: 0.8391\n",
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"Epoch 4/100\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.5827 - accuracy: 0.8372 - val_loss: 0.5511 - val_accuracy: 0.8506\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.4496 - accuracy: 0.8343 - val_loss: 0.4293 - val_accuracy: 0.8391\n",
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"Epoch 5/100\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.5353 - accuracy: 0.8401 - val_loss: 0.5105 - val_accuracy: 0.8506\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.4051 - accuracy: 0.8459 - val_loss: 0.4132 - val_accuracy: 0.8391\n",
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"Epoch 6/100\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.4874 - accuracy: 0.8430 - val_loss: 0.4787 - val_accuracy: 0.8506\n",
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"11/11 [==============================] - 0s 4ms/step - loss: 0.3665 - accuracy: 0.8634 - val_loss: 0.4049 - val_accuracy: 0.8506\n",
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"Epoch 7/100\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.4375 - accuracy: 0.8459 - val_loss: 0.4518 - val_accuracy: 0.8506\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.3397 - accuracy: 0.8837 - val_loss: 0.3967 - val_accuracy: 0.8506\n",
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"Epoch 8/100\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.3944 - accuracy: 0.8488 - val_loss: 0.4320 - val_accuracy: 0.8506\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.3178 - accuracy: 0.9041 - val_loss: 0.3961 - val_accuracy: 0.8506\n",
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"Epoch 9/100\n",
|
||||
" 1/11 [=>............................] - ETA: 0s - loss: 0.4943 - accuracy: 0.7812Restoring model weights from the end of the best epoch: 4.\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.3565 - accuracy: 0.8547 - val_loss: 0.4221 - val_accuracy: 0.8506\n",
|
||||
"Epoch 9: early stopping\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.3018 - accuracy: 0.9099 - val_loss: 0.3948 - val_accuracy: 0.8506\n",
|
||||
"Epoch 10/100\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.2875 - accuracy: 0.9099 - val_loss: 0.3997 - val_accuracy: 0.8506\n",
|
||||
"Epoch 11/100\n",
|
||||
" 1/11 [=>............................] - ETA: 0s - loss: 0.3437 - accuracy: 0.9062Restoring model weights from the end of the best epoch: 6.\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.2751 - accuracy: 0.9099 - val_loss: 0.4019 - val_accuracy: 0.8506\n",
|
||||
"Epoch 11: early stopping\n",
|
||||
"Epoch 1/100\n",
|
||||
"11/11 [==============================] - 0s 15ms/step - loss: 0.6726 - accuracy: 0.6483 - val_loss: 0.5800 - val_accuracy: 0.8391\n",
|
||||
"11/11 [==============================] - 0s 16ms/step - loss: 0.8969 - accuracy: 0.2297 - val_loss: 0.7597 - val_accuracy: 0.4713\n",
|
||||
"Epoch 2/100\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.5425 - accuracy: 0.8430 - val_loss: 0.5210 - val_accuracy: 0.8391\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.7185 - accuracy: 0.5436 - val_loss: 0.6301 - val_accuracy: 0.7471\n",
|
||||
"Epoch 3/100\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.4813 - accuracy: 0.8430 - val_loss: 0.5047 - val_accuracy: 0.8391\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.6123 - accuracy: 0.7762 - val_loss: 0.5465 - val_accuracy: 0.7586\n",
|
||||
"Epoch 4/100\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.4377 - accuracy: 0.8517 - val_loss: 0.4948 - val_accuracy: 0.8391\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.5408 - accuracy: 0.8401 - val_loss: 0.4930 - val_accuracy: 0.8276\n",
|
||||
"Epoch 5/100\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.3982 - accuracy: 0.8547 - val_loss: 0.4875 - val_accuracy: 0.8391\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.4907 - accuracy: 0.8459 - val_loss: 0.4590 - val_accuracy: 0.8391\n",
|
||||
"Epoch 6/100\n",
|
||||
" 1/11 [=>............................] - ETA: 0s - loss: 0.3590 - accuracy: 0.8438Restoring model weights from the end of the best epoch: 1.\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.3683 - accuracy: 0.8663 - val_loss: 0.4850 - val_accuracy: 0.8391\n",
|
||||
"Epoch 6: early stopping\n",
|
||||
"best acc at index 6: 0.8505747318267822\n",
|
||||
"best loss at index 2: 0.3847053050994873\n",
|
||||
"{'units3': 32, 'units2': 32, 'units1': 16, 'optimizer': <class 'keras.optimizers.legacy.adam.Adam'>, 'learning_rate': 0.001, 'activation3': 'relu', 'activation2': 'relu', 'activation1': 'relu'}\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.4501 - accuracy: 0.8430 - val_loss: 0.4371 - val_accuracy: 0.8391\n",
|
||||
"Epoch 7/100\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.4202 - accuracy: 0.8459 - val_loss: 0.4190 - val_accuracy: 0.8391\n",
|
||||
"Epoch 8/100\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.3892 - accuracy: 0.8488 - val_loss: 0.4064 - val_accuracy: 0.8391\n",
|
||||
"Epoch 9/100\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.3649 - accuracy: 0.8576 - val_loss: 0.3965 - val_accuracy: 0.8391\n",
|
||||
"Epoch 10/100\n",
|
||||
" 1/11 [=>............................] - ETA: 0s - loss: 0.2900 - accuracy: 0.9375Restoring model weights from the end of the best epoch: 5.\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.3420 - accuracy: 0.8692 - val_loss: 0.3873 - val_accuracy: 0.8391\n",
|
||||
"Epoch 10: early stopping\n",
|
||||
"Epoch 1/100\n",
|
||||
"11/11 [==============================] - 0s 15ms/step - loss: 0.6807 - accuracy: 0.8372 - val_loss: 0.5413 - val_accuracy: 0.8391\n",
|
||||
"11/11 [==============================] - 0s 16ms/step - loss: 0.6335 - accuracy: 0.7849 - val_loss: 0.5528 - val_accuracy: 0.8391\n",
|
||||
"Epoch 2/100\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.6144 - accuracy: 0.8372 - val_loss: 0.5060 - val_accuracy: 0.8391\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.5477 - accuracy: 0.8372 - val_loss: 0.4920 - val_accuracy: 0.8391\n",
|
||||
"Epoch 3/100\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.5689 - accuracy: 0.8372 - val_loss: 0.4795 - val_accuracy: 0.8391\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.4883 - accuracy: 0.8401 - val_loss: 0.4514 - val_accuracy: 0.8391\n",
|
||||
"Epoch 4/100\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.5273 - accuracy: 0.8372 - val_loss: 0.4565 - val_accuracy: 0.8391\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.4435 - accuracy: 0.8430 - val_loss: 0.4220 - val_accuracy: 0.8391\n",
|
||||
"Epoch 5/100\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.4915 - accuracy: 0.8372 - val_loss: 0.4387 - val_accuracy: 0.8391\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.4014 - accuracy: 0.8459 - val_loss: 0.3975 - val_accuracy: 0.8391\n",
|
||||
"Epoch 6/100\n",
|
||||
" 1/11 [=>............................] - ETA: 0s - loss: 0.5601 - accuracy: 0.7812Restoring model weights from the end of the best epoch: 1.\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.4573 - accuracy: 0.8401 - val_loss: 0.4216 - val_accuracy: 0.8391\n",
|
||||
"Epoch 6: early stopping\n"
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.3657 - accuracy: 0.8488 - val_loss: 0.3775 - val_accuracy: 0.8506\n",
|
||||
"Epoch 7/100\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.3343 - accuracy: 0.8721 - val_loss: 0.3609 - val_accuracy: 0.8506\n",
|
||||
"Epoch 8/100\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.3069 - accuracy: 0.8837 - val_loss: 0.3547 - val_accuracy: 0.8506\n",
|
||||
"Epoch 9/100\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.2838 - accuracy: 0.8837 - val_loss: 0.3453 - val_accuracy: 0.8506\n",
|
||||
"Epoch 10/100\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.2615 - accuracy: 0.8924 - val_loss: 0.3440 - val_accuracy: 0.8621\n",
|
||||
"Epoch 11/100\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.2444 - accuracy: 0.9070 - val_loss: 0.3438 - val_accuracy: 0.8621\n",
|
||||
"Epoch 12/100\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.2285 - accuracy: 0.9128 - val_loss: 0.3447 - val_accuracy: 0.8621\n",
|
||||
"Epoch 13/100\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.2165 - accuracy: 0.9186 - val_loss: 0.3427 - val_accuracy: 0.8621\n",
|
||||
"Epoch 14/100\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.2012 - accuracy: 0.9331 - val_loss: 0.3522 - val_accuracy: 0.8621\n",
|
||||
"Epoch 15/100\n",
|
||||
" 1/11 [=>............................] - ETA: 0s - loss: 0.1392 - accuracy: 0.9688Restoring model weights from the end of the best epoch: 10.\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.1896 - accuracy: 0.9360 - val_loss: 0.3620 - val_accuracy: 0.8621\n",
|
||||
"Epoch 15: early stopping\n",
|
||||
"Epoch 1/100\n",
|
||||
"11/11 [==============================] - 0s 16ms/step - loss: 0.7918 - accuracy: 0.3140 - val_loss: 0.6742 - val_accuracy: 0.6437\n",
|
||||
"Epoch 2/100\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.6316 - accuracy: 0.7297 - val_loss: 0.5706 - val_accuracy: 0.8391\n",
|
||||
"Epoch 3/100\n",
|
||||
"11/11 [==============================] - 0s 5ms/step - loss: 0.5542 - accuracy: 0.8256 - val_loss: 0.5122 - val_accuracy: 0.8391\n",
|
||||
"Epoch 4/100\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.4967 - accuracy: 0.8401 - val_loss: 0.4750 - val_accuracy: 0.8391\n",
|
||||
"Epoch 5/100\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.4508 - accuracy: 0.8401 - val_loss: 0.4540 - val_accuracy: 0.8391\n",
|
||||
"Epoch 6/100\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.4100 - accuracy: 0.8430 - val_loss: 0.4411 - val_accuracy: 0.8391\n",
|
||||
"Epoch 7/100\n",
|
||||
" 1/11 [=>............................] - ETA: 0s - loss: 0.4157 - accuracy: 0.8125Restoring model weights from the end of the best epoch: 2.\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.3695 - accuracy: 0.8517 - val_loss: 0.4333 - val_accuracy: 0.8391\n",
|
||||
"Epoch 7: early stopping\n",
|
||||
"Epoch 1/100\n",
|
||||
"11/11 [==============================] - 0s 15ms/step - loss: 0.8143 - accuracy: 0.2994 - val_loss: 0.6333 - val_accuracy: 0.7586\n",
|
||||
"Epoch 2/100\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.5870 - accuracy: 0.8227 - val_loss: 0.5004 - val_accuracy: 0.8391\n",
|
||||
"Epoch 3/100\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.5142 - accuracy: 0.8372 - val_loss: 0.4455 - val_accuracy: 0.8391\n",
|
||||
"Epoch 4/100\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.4769 - accuracy: 0.8372 - val_loss: 0.4203 - val_accuracy: 0.8391\n",
|
||||
"Epoch 5/100\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.4411 - accuracy: 0.8372 - val_loss: 0.4046 - val_accuracy: 0.8391\n",
|
||||
"Epoch 6/100\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.4000 - accuracy: 0.8372 - val_loss: 0.3902 - val_accuracy: 0.8391\n",
|
||||
"Epoch 7/100\n",
|
||||
" 1/11 [=>............................] - ETA: 0s - loss: 0.2600 - accuracy: 0.9375Restoring model weights from the end of the best epoch: 2.\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.3656 - accuracy: 0.8430 - val_loss: 0.3776 - val_accuracy: 0.8391\n",
|
||||
"Epoch 7: early stopping\n",
|
||||
"best acc at index 5: 0.8620689511299133\n",
|
||||
"best loss at index 5: 0.3620067536830902\n",
|
||||
"{'units3': 64, 'units2': 16, 'units1': 16, 'optimizer': <class 'keras.optimizers.legacy.adam.Adam'>, 'learning_rate': 0.001, 'activation3': 'relu', 'activation2': 'relu', 'activation1': 'relu'}\n",
|
||||
"Epoch 1/100\n",
|
||||
"11/11 [==============================] - 0s 15ms/step - loss: 0.6786 - accuracy: 0.6453 - val_loss: 0.5829 - val_accuracy: 0.8276\n",
|
||||
"Epoch 2/100\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.5685 - accuracy: 0.8488 - val_loss: 0.5142 - val_accuracy: 0.8391\n",
|
||||
"Epoch 3/100\n",
|
||||
"11/11 [==============================] - 0s 5ms/step - loss: 0.5102 - accuracy: 0.8372 - val_loss: 0.4789 - val_accuracy: 0.8391\n",
|
||||
"Epoch 4/100\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.4728 - accuracy: 0.8401 - val_loss: 0.4561 - val_accuracy: 0.8391\n",
|
||||
"Epoch 5/100\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.4380 - accuracy: 0.8488 - val_loss: 0.4398 - val_accuracy: 0.8391\n",
|
||||
"Epoch 6/100\n",
|
||||
"11/11 [==============================] - 0s 3ms/step - loss: 0.4107 - accuracy: 0.8547 - val_loss: 0.4282 - val_accuracy: 0.8391\n",
|
||||
"Epoch 7/100\n",
|
||||
" 1/11 [=>............................] - ETA: 0s - loss: 0.3892 - accuracy: 0.8438Restoring model weights from the end of the best epoch: 2.\n",
|
||||
"11/11 [==============================] - 0s 4ms/step - loss: 0.3847 - accuracy: 0.8576 - val_loss: 0.4165 - val_accuracy: 0.8391\n",
|
||||
"Epoch 7: early stopping\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -496,14 +522,14 @@
|
||||
"preprocessor = build_preprocessing_pipeline(X_train)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Define model for the pipeline\n",
|
||||
"#model = RandomForestClassifier(n_estimators=100, random_state=22)\n",
|
||||
"#model = XGBClassifier(n_estimators=500)\n",
|
||||
"# Define models for the pipeline\n",
|
||||
"preprocessor.fit(X_train)\n",
|
||||
"X_preprocessed = preprocessor.transform(X_train)\n",
|
||||
"y_train_ohe = pd.get_dummies(y_train, columns = ['Class'])\n",
|
||||
"y_valid_ohe = pd.get_dummies(y_valid, columns = ['Class'])\n",
|
||||
"model = KerasClassifier(model=grid_search_tf_model(X_train=X_preprocessed, y_train=y_train_ohe), epochs=0)"
|
||||
"model_keras = KerasClassifier(model=grid_search_tf_model(X_train=X_preprocessed, y_train=y_train_ohe), epochs=0)\n",
|
||||
"model_rf = RandomForestClassifier(n_estimators=100, random_state=22)\n",
|
||||
"model_xgb = XGBClassifier(n_estimators=500)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -515,12 +541,12 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 170,
|
||||
"execution_count": 103,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Building Pipeline\n",
|
||||
"def fit_pipeline(X_train: pd.DataFrame, y_train: pd.DataFrame, preprocessor, model):\n",
|
||||
"def fit_pipeline(X_train: pd.DataFrame, y_train: pd.DataFrame, preprocessor, model) -> Pipeline:\n",
|
||||
" pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
|
||||
" ('model', model)\n",
|
||||
" ])\n",
|
||||
@@ -529,11 +555,11 @@
|
||||
" preds = pipeline.predict(X_valid)\n",
|
||||
" \"\"\" score = cross_val_score(pipeline, X_valid, y_valid, cv=5, scoring='accuracy')\n",
|
||||
" print(f\"Accuracy of {score}\") \"\"\"\n",
|
||||
" print(classification_report(y_train.to_numpy(), preds))\n",
|
||||
" correct_answers = 0\n",
|
||||
" for y_pred,y_true in zip(preds,y_valid_ohe.to_numpy()):\n",
|
||||
" if(y_pred[0] == y_true[0]):correct_answers+=1\n",
|
||||
" print(correct_answers/len(preds))"
|
||||
" print(correct_answers/len(preds))\n",
|
||||
" return pipeline"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -545,34 +571,65 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 171,
|
||||
"execution_count": 104,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"6/6 [==============================] - 0s 1ms/step\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "ValueError",
|
||||
"evalue": "Found input variables with inconsistent numbers of samples: [431, 186]",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[1;32mIn[171], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m fit_pipeline(X_train, y_train_ohe, preprocessor, model)\n",
|
||||
"Cell \u001b[1;32mIn[170], line 11\u001b[0m, in \u001b[0;36mfit_pipeline\u001b[1;34m(X_train, y_train, preprocessor, model)\u001b[0m\n\u001b[0;32m 8\u001b[0m preds \u001b[39m=\u001b[39m pipeline\u001b[39m.\u001b[39mpredict(X_valid)\n\u001b[0;32m 9\u001b[0m \u001b[39m\"\"\" score = cross_val_score(pipeline, X_valid, y_valid, cv=5, scoring='accuracy')\u001b[39;00m\n\u001b[0;32m 10\u001b[0m \u001b[39mprint(f\"Accuracy of {score}\") \"\"\"\u001b[39;00m\n\u001b[1;32m---> 11\u001b[0m \u001b[39mprint\u001b[39m(classification_report(y_train\u001b[39m.\u001b[39;49mto_numpy(), preds))\n\u001b[0;32m 12\u001b[0m correct_answers \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[0;32m 13\u001b[0m \u001b[39mfor\u001b[39;00m y_pred,y_true \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(preds,y_valid_ohe\u001b[39m.\u001b[39mto_numpy()):\n",
|
||||
"File \u001b[1;32mc:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:2310\u001b[0m, in \u001b[0;36mclassification_report\u001b[1;34m(y_true, y_pred, labels, target_names, sample_weight, digits, output_dict, zero_division)\u001b[0m\n\u001b[0;32m 2195\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mclassification_report\u001b[39m(\n\u001b[0;32m 2196\u001b[0m y_true,\n\u001b[0;32m 2197\u001b[0m y_pred,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 2204\u001b[0m zero_division\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mwarn\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m 2205\u001b[0m ):\n\u001b[0;32m 2206\u001b[0m \u001b[39m\"\"\"Build a text report showing the main classification metrics.\u001b[39;00m\n\u001b[0;32m 2207\u001b[0m \n\u001b[0;32m 2208\u001b[0m \u001b[39m Read more in the :ref:`User Guide <classification_report>`.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 2307\u001b[0m \u001b[39m <BLANKLINE>\u001b[39;00m\n\u001b[0;32m 2308\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 2310\u001b[0m y_type, y_true, y_pred \u001b[39m=\u001b[39m _check_targets(y_true, y_pred)\n\u001b[0;32m 2312\u001b[0m \u001b[39mif\u001b[39;00m labels \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 2313\u001b[0m labels \u001b[39m=\u001b[39m unique_labels(y_true, y_pred)\n",
|
||||
"File \u001b[1;32mc:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:86\u001b[0m, in \u001b[0;36m_check_targets\u001b[1;34m(y_true, y_pred)\u001b[0m\n\u001b[0;32m 59\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_check_targets\u001b[39m(y_true, y_pred):\n\u001b[0;32m 60\u001b[0m \u001b[39m\"\"\"Check that y_true and y_pred belong to the same classification task.\u001b[39;00m\n\u001b[0;32m 61\u001b[0m \n\u001b[0;32m 62\u001b[0m \u001b[39m This converts multiclass or binary types to a common shape, and raises a\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 84\u001b[0m \u001b[39m y_pred : array or indicator matrix\u001b[39;00m\n\u001b[0;32m 85\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m---> 86\u001b[0m check_consistent_length(y_true, y_pred)\n\u001b[0;32m 87\u001b[0m type_true \u001b[39m=\u001b[39m type_of_target(y_true, input_name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39my_true\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 88\u001b[0m type_pred \u001b[39m=\u001b[39m type_of_target(y_pred, input_name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39my_pred\u001b[39m\u001b[39m\"\u001b[39m)\n",
|
||||
"File \u001b[1;32mc:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:397\u001b[0m, in \u001b[0;36mcheck_consistent_length\u001b[1;34m(*arrays)\u001b[0m\n\u001b[0;32m 395\u001b[0m uniques \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39munique(lengths)\n\u001b[0;32m 396\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(uniques) \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m--> 397\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 398\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mFound input variables with inconsistent numbers of samples: \u001b[39m\u001b[39m%r\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[0;32m 399\u001b[0m \u001b[39m%\u001b[39m [\u001b[39mint\u001b[39m(l) \u001b[39mfor\u001b[39;00m l \u001b[39min\u001b[39;00m lengths]\n\u001b[0;32m 400\u001b[0m )\n",
|
||||
"\u001b[1;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [431, 186]"
|
||||
"6/6 [==============================] - 0s 1ms/step\n",
|
||||
"0.7849462365591398\n",
|
||||
"0.9354838709677419\n",
|
||||
"0.946236559139785\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fit_pipeline(X_train, y_train_ohe, preprocessor, model)"
|
||||
"pipeline_keras = fit_pipeline(X_train, y_train_ohe, preprocessor, model_keras)\n",
|
||||
"pipeline_rf = fit_pipeline(X_train, y_train_ohe, preprocessor, model_rf)\n",
|
||||
"pipeline_xgb = fit_pipeline(X_train, y_train_ohe, preprocessor, model_xgb)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Combine the fitted models to look if the accuracy improves"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 108,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"6/6 [==============================] - 0s 799us/step\n",
|
||||
"0.9247311827956989\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"preds1 = pipeline_keras.predict(X_valid)\n",
|
||||
"preds2 = pipeline_rf.predict(X_valid)\n",
|
||||
"preds3 = pipeline_xgb.predict(X_valid)\n",
|
||||
"correct_answers = 0\n",
|
||||
"preds = [[0,0] for _ in range(len(preds1))]\n",
|
||||
"for y_pred1,y_pred2,y_pred3, i in zip(preds1,preds2,preds3, range(len(preds1))):\n",
|
||||
" count_class1 = y_pred1[0] + y_pred2[0] + y_pred3[0]\n",
|
||||
" count_class2 = y_pred1[1] + y_pred2[1] + y_pred3[1]\n",
|
||||
" if(count_class1 > count_class2):\n",
|
||||
" preds[i][0] = 1\n",
|
||||
" preds[i][1] = 0\n",
|
||||
" else:\n",
|
||||
" preds[i][0] = 0\n",
|
||||
" preds[i][1] = 1\n",
|
||||
"for y_pred,y_true in zip(preds,y_valid_ohe.to_numpy()):\n",
|
||||
" if(y_pred[0] == y_true[0]):correct_answers+=1\n",
|
||||
"print(correct_answers/len(preds))"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -593,7 +650,7 @@
|
||||
"' submission = pd.DataFrame()\\nprediction = model.predict(x_test)\\nsubmission.insert(0, \"Id\", id_number, False)\\nsubmission.insert(1, \"class_0\", [round(1-i[0],2) for i in prediction], True)\\nsubmission.insert(2, \"class_1\", [round(i[0],2) for i in prediction], True)\\nsubmission.to_csv(\"/kaggle/working/submission.csv\",index = False) '"
|
||||
]
|
||||
},
|
||||
"execution_count": 154,
|
||||
"execution_count": 71,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user