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Giuseppe Nucifora 2024-10-22 22:42:54 +02:00
parent 2db7654f06
commit e0687f8248
3 changed files with 183 additions and 357935 deletions
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olive-oil-production-analysis-notebook.ipynb
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{
"metadata": {
"ExecuteTime": {
"end_time": "2024-10-21T23:24:45.591951Z",
"start_time": "2024-10-21T23:23:57.902382Z"
"end_time": "2024-10-22T20:24:31.977425Z",
"start_time": "2024-10-22T20:24:21.439517Z"
}
},
"cell_type": "code",
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"name": "stdout",
"output_type": "stream",
"text": [
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"Collecting pysolar\r\n",
" Downloading pysolar-0.11-py3-none-any.whl.metadata (331 bytes)\r\n",
"Requirement already satisfied: numpy in /usr/local/anaconda3/lib/python3.12/site-packages (from pysolar) (1.26.4)\r\n",
"Requirement already satisfied: numpy in /opt/homebrew/anaconda3/envs/ml_env/lib/python3.9/site-packages (from pysolar) (1.23.5)\r\n",
"Downloading pysolar-0.11-py3-none-any.whl (47 kB)\r\n",
"\u001B[2K \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m47.1/47.1 kB\u001B[0m \u001B[31m599.8 kB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0ma \u001B[36m0:00:01\u001B[0m\r\n",
"\u001B[?25hInstalling collected packages: pysolar\r\n",
"Installing collected packages: pysolar\r\n",
"Successfully installed pysolar-0.11\r\n"
]
}
],
"execution_count": 244
"execution_count": 1
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2024-10-22T00:12:54.872744Z",
"start_time": "2024-10-22T00:12:54.868179Z"
"end_time": "2024-10-22T20:25:28.333939Z",
"start_time": "2024-10-22T20:24:57.662497Z"
}
},
"source": [
@ -185,7 +191,7 @@
"random_state_value = 42"
],
"outputs": [],
"execution_count": 3
"execution_count": 2
},
{
"cell_type": "markdown",
@ -196,8 +202,8 @@
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2024-10-22T00:13:13.482056Z",
"start_time": "2024-10-22T00:12:57.210546Z"
"end_time": "2024-10-22T20:25:50.991904Z",
"start_time": "2024-10-22T20:25:35.928238Z"
}
},
"source": [
@ -303,9 +309,7 @@
"weather_data['day'] = weather_data['datetime'].dt.day\n",
"weather_data.head()\n",
"\n",
"weather_data.to_csv('./data/weather_data.csv')\n",
"\n",
"csv_to_parquet('./data/weather_data.csv', './data/weather_data.parquet')"
"weather_data.to_parquet('./data/weather_data.parquet')"
],
"outputs": [
{
@ -316,7 +320,7 @@
]
}
],
"execution_count": 4
"execution_count": 3
},
{
"metadata": {},
@ -326,8 +330,8 @@
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-10-22T17:50:50.867820Z",
"start_time": "2024-10-22T16:34:54.050578Z"
"end_time": "2024-10-22T20:31:08.168598Z",
"start_time": "2024-10-22T20:25:54.310192Z"
}
},
"cell_type": "code",
@ -427,7 +431,7 @@
" return df\n",
"\n",
"# Carica il dataset\n",
"weather_data = pd.read_csv('./data/weather_data.csv')\n",
"weather_data = pd.read_parquet('./data/weather_data.parquet')\n",
"\n",
"# Aggiungi le caratteristiche temporali\n",
"weather_data = add_time_features(weather_data)\n",
@ -435,6 +439,8 @@
"# Encoding delle variabili categoriali\n",
"weather_data = pd.get_dummies(weather_data, columns=['season', 'time_period'], drop_first=True)\n",
"\n",
"weather_data.to_parquet('./data/weather_data_extended.parquet')\n",
"\n",
"# Dividi i dati in quelli dopo il 2010 e quelli prima del 2010\n",
"data_after_2010 = weather_data[weather_data['year'] >= 2010].copy()\n",
"data_before_2010 = weather_data[weather_data['year'] < 2010].copy()\n",
@ -561,114 +567,72 @@
"\n",
"# Salva il dataset completo\n",
"weather_data_complete.reset_index(inplace=True)\n",
"weather_data_complete.to_csv('weather_data_complete.csv', index=False)\n"
"weather_data_complete.to_parquet('./data/weather_data_complete.parquet', index=False)\n"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Addestramento del modello per: solarradiation\n",
"Epoch 1/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m104s\u001B[0m 71ms/step - loss: 0.3627 - val_loss: 0.0391\n",
"Epoch 2/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m73s\u001B[0m 56ms/step - loss: 0.0305 - val_loss: 0.0180\n",
"Epoch 3/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m62s\u001B[0m 48ms/step - loss: 0.0120 - val_loss: 0.0094\n",
"Epoch 4/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m75s\u001B[0m 58ms/step - loss: 0.0095 - val_loss: 0.0086\n",
"Epoch 5/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m64s\u001B[0m 49ms/step - loss: 0.0091 - val_loss: 0.0156\n",
"Epoch 6/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m71s\u001B[0m 55ms/step - loss: 0.0091 - val_loss: 0.0103\n",
"Epoch 7/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m91s\u001B[0m 70ms/step - loss: 0.0091 - val_loss: 0.0091\n",
"Epoch 8/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m98s\u001B[0m 75ms/step - loss: 0.0088 - val_loss: 0.0125\n",
"Epoch 9/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m96s\u001B[0m 74ms/step - loss: 0.0088 - val_loss: 0.0123\n",
"\u001B[1m810/810\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m23s\u001B[0m 29ms/step - loss: 0.0080\n",
"Test MAE per solarradiation: 0.0090\n",
"Addestramento del modello per: solarenergy\n",
"Epoch 1/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m122s\u001B[0m 85ms/step - loss: 0.3818 - val_loss: 0.0431\n",
"Epoch 2/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m98s\u001B[0m 75ms/step - loss: 0.0338 - val_loss: 0.0147\n",
"Epoch 3/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m107s\u001B[0m 82ms/step - loss: 0.0123 - val_loss: 0.0116\n",
"Epoch 4/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m98s\u001B[0m 76ms/step - loss: 0.0095 - val_loss: 0.0088\n",
"Epoch 5/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m104s\u001B[0m 80ms/step - loss: 0.0093 - val_loss: 0.0231\n",
"Epoch 6/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m101s\u001B[0m 78ms/step - loss: 0.0091 - val_loss: 0.0134\n",
"Epoch 7/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m106s\u001B[0m 82ms/step - loss: 0.0089 - val_loss: 0.0115\n",
"Epoch 8/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m110s\u001B[0m 85ms/step - loss: 0.0091 - val_loss: 0.0140\n",
"Epoch 9/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m114s\u001B[0m 88ms/step - loss: 0.0088 - val_loss: 0.0087\n",
"Epoch 10/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m98s\u001B[0m 76ms/step - loss: 0.0087 - val_loss: 0.0138\n",
"Epoch 11/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m101s\u001B[0m 78ms/step - loss: 0.0087 - val_loss: 0.0330\n",
"Epoch 12/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m119s\u001B[0m 91ms/step - loss: 0.0086 - val_loss: 0.0101\n",
"Epoch 13/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m125s\u001B[0m 97ms/step - loss: 0.0082 - val_loss: 0.0090\n",
"Epoch 14/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m115s\u001B[0m 88ms/step - loss: 0.0082 - val_loss: 0.0069\n",
"Epoch 15/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m119s\u001B[0m 92ms/step - loss: 0.0082 - val_loss: 0.0106\n",
"Epoch 16/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m98s\u001B[0m 75ms/step - loss: 0.0080 - val_loss: 0.0098\n",
"Epoch 17/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m128s\u001B[0m 99ms/step - loss: 0.0081 - val_loss: 0.0076\n",
"Epoch 18/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m114s\u001B[0m 88ms/step - loss: 0.0081 - val_loss: 0.0082\n",
"Epoch 19/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m114s\u001B[0m 88ms/step - loss: 0.0081 - val_loss: 0.0084\n",
"\u001B[1m810/810\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m29s\u001B[0m 36ms/step - loss: 0.0075\n",
"Test MAE per solarenergy: 0.0081\n",
"Addestramento del modello per: uvindex\n",
"Epoch 1/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m130s\u001B[0m 89ms/step - loss: 0.5318 - val_loss: 0.0700\n",
"Epoch 2/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m103s\u001B[0m 79ms/step - loss: 0.0618 - val_loss: 0.0311\n",
"Epoch 3/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m103s\u001B[0m 80ms/step - loss: 0.0251 - val_loss: 0.0149\n",
"Epoch 4/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m98s\u001B[0m 75ms/step - loss: 0.0139 - val_loss: 0.0101\n",
"Epoch 5/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m95s\u001B[0m 73ms/step - loss: 0.0121 - val_loss: 0.0170\n",
"Epoch 6/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m99s\u001B[0m 76ms/step - loss: 0.0118 - val_loss: 0.0102\n",
"Epoch 7/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m107s\u001B[0m 82ms/step - loss: 0.0117 - val_loss: 0.0124\n",
"Epoch 8/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m118s\u001B[0m 91ms/step - loss: 0.0116 - val_loss: 0.0148\n",
"Epoch 9/50\n",
"\u001B[1m1297/1297\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m97s\u001B[0m 75ms/step - loss: 0.0116 - val_loss: 0.0471\n",
"\u001B[1m810/810\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m25s\u001B[0m 30ms/step - loss: 0.0089\n",
"Test MAE per uvindex: 0.0103\n",
"Previsione di solarradiation per data_before_2010\n",
"\u001B[1m7122/7122\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m228s\u001B[0m 32ms/step\n",
"Previsione di solarenergy per data_before_2010\n",
"\u001B[1m7122/7122\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m255s\u001B[0m 36ms/step\n",
"Previsione di uvindex per data_before_2010\n",
"\u001B[1m7122/7122\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m200s\u001B[0m 28ms/step\n"
"Addestramento del modello per: solarradiation\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/j4/dltmqwjj1438ftthspk8_knm0000gn/T/ipykernel_71420/3110393909.py:222: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
" data_before_2010[target_variables] = data_before_2010[target_variables].fillna(method='bfill')\n"
"2024-10-22 22:25:57.972773: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1\n",
"2024-10-22 22:25:57.972869: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 8.00 GB\n",
"2024-10-22 22:25:57.972885: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 2.67 GB\n",
"2024-10-22 22:25:57.973640: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
"2024-10-22 22:25:57.974352: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/50\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-10-22 22:26:02.745785: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type GPU is enabled.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[1m 454/1297\u001B[0m \u001B[32m━━━━━━━\u001B[0m\u001B[37m━━━━━━━━━━━━━\u001B[0m \u001B[1m8:52\u001B[0m 631ms/step - loss: 0.5095"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[0;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)",
"Cell \u001B[0;32mIn[4], line 177\u001B[0m\n\u001B[1;32m 175\u001B[0m model\u001B[38;5;241m.\u001B[39mcompile(optimizer\u001B[38;5;241m=\u001B[39moptimizer, loss\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mmean_squared_error\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[1;32m 176\u001B[0m early_stopping \u001B[38;5;241m=\u001B[39m EarlyStopping(monitor\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mval_loss\u001B[39m\u001B[38;5;124m'\u001B[39m, patience\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m5\u001B[39m, restore_best_weights\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[0;32m--> 177\u001B[0m history \u001B[38;5;241m=\u001B[39m \u001B[43mmodel\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 178\u001B[0m \u001B[43m \u001B[49m\u001B[43mX_train_seq\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my_train_seq\u001B[49m\u001B[43m[\u001B[49m\u001B[43m:\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mi\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 179\u001B[0m \u001B[43m \u001B[49m\u001B[43mvalidation_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mX_val_seq\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my_val_seq\u001B[49m\u001B[43m[\u001B[49m\u001B[43m:\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mi\u001B[49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 180\u001B[0m \u001B[43m \u001B[49m\u001B[43mepochs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m50\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 181\u001B[0m \u001B[43m \u001B[49m\u001B[43mbatch_size\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m64\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 182\u001B[0m \u001B[43m \u001B[49m\u001B[43mcallbacks\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\u001B[43mearly_stopping\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 183\u001B[0m \u001B[43m \u001B[49m\u001B[43mverbose\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m1\u001B[39;49m\n\u001B[1;32m 184\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 185\u001B[0m test_loss \u001B[38;5;241m=\u001B[39m model\u001B[38;5;241m.\u001B[39mevaluate(X_test_seq, y_test_seq[:, i])\n\u001B[1;32m 186\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mTest MAE per \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mtarget\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mtest_loss\u001B[38;5;132;01m:\u001B[39;00m\u001B[38;5;124m.4f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n",
"File \u001B[0;32m/opt/homebrew/anaconda3/envs/ml_env/lib/python3.9/site-packages/keras/src/utils/traceback_utils.py:117\u001B[0m, in \u001B[0;36mfilter_traceback.<locals>.error_handler\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m 115\u001B[0m filtered_tb \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m 116\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 117\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfn\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 118\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m 119\u001B[0m filtered_tb \u001B[38;5;241m=\u001B[39m _process_traceback_frames(e\u001B[38;5;241m.\u001B[39m__traceback__)\n",
"File \u001B[0;32m/opt/homebrew/anaconda3/envs/ml_env/lib/python3.9/site-packages/keras/src/backend/tensorflow/trainer.py:320\u001B[0m, in \u001B[0;36mTensorFlowTrainer.fit\u001B[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001B[0m\n\u001B[1;32m 318\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m step, iterator \u001B[38;5;129;01min\u001B[39;00m epoch_iterator\u001B[38;5;241m.\u001B[39menumerate_epoch():\n\u001B[1;32m 319\u001B[0m callbacks\u001B[38;5;241m.\u001B[39mon_train_batch_begin(step)\n\u001B[0;32m--> 320\u001B[0m logs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtrain_function\u001B[49m\u001B[43m(\u001B[49m\u001B[43miterator\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 321\u001B[0m logs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_pythonify_logs(logs)\n\u001B[1;32m 322\u001B[0m callbacks\u001B[38;5;241m.\u001B[39mon_train_batch_end(step, logs)\n",
"File \u001B[0;32m/opt/homebrew/anaconda3/envs/ml_env/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py:150\u001B[0m, in \u001B[0;36mfilter_traceback.<locals>.error_handler\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m 148\u001B[0m filtered_tb \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m 149\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 150\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfn\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 151\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m 152\u001B[0m filtered_tb \u001B[38;5;241m=\u001B[39m _process_traceback_frames(e\u001B[38;5;241m.\u001B[39m__traceback__)\n",
"File \u001B[0;32m/opt/homebrew/anaconda3/envs/ml_env/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:833\u001B[0m, in \u001B[0;36mFunction.__call__\u001B[0;34m(self, *args, **kwds)\u001B[0m\n\u001B[1;32m 830\u001B[0m compiler \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mxla\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_jit_compile \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnonXla\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 832\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m OptionalXlaContext(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_jit_compile):\n\u001B[0;32m--> 833\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwds\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 835\u001B[0m new_tracing_count \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mexperimental_get_tracing_count()\n\u001B[1;32m 836\u001B[0m without_tracing \u001B[38;5;241m=\u001B[39m (tracing_count \u001B[38;5;241m==\u001B[39m new_tracing_count)\n",
"File \u001B[0;32m/opt/homebrew/anaconda3/envs/ml_env/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:878\u001B[0m, in \u001B[0;36mFunction._call\u001B[0;34m(self, *args, **kwds)\u001B[0m\n\u001B[1;32m 875\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_lock\u001B[38;5;241m.\u001B[39mrelease()\n\u001B[1;32m 876\u001B[0m \u001B[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001B[39;00m\n\u001B[1;32m 877\u001B[0m \u001B[38;5;66;03m# run the first trace but we should fail if variables are created.\u001B[39;00m\n\u001B[0;32m--> 878\u001B[0m results \u001B[38;5;241m=\u001B[39m \u001B[43mtracing_compilation\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcall_function\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 879\u001B[0m \u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkwds\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_variable_creation_config\u001B[49m\n\u001B[1;32m 880\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 881\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_created_variables:\n\u001B[1;32m 882\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCreating variables on a non-first call to a function\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 883\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m decorated with tf.function.\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
"File \u001B[0;32m/opt/homebrew/anaconda3/envs/ml_env/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py:139\u001B[0m, in \u001B[0;36mcall_function\u001B[0;34m(args, kwargs, tracing_options)\u001B[0m\n\u001B[1;32m 137\u001B[0m bound_args \u001B[38;5;241m=\u001B[39m function\u001B[38;5;241m.\u001B[39mfunction_type\u001B[38;5;241m.\u001B[39mbind(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[1;32m 138\u001B[0m flat_inputs \u001B[38;5;241m=\u001B[39m function\u001B[38;5;241m.\u001B[39mfunction_type\u001B[38;5;241m.\u001B[39munpack_inputs(bound_args)\n\u001B[0;32m--> 139\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunction\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call_flat\u001B[49m\u001B[43m(\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;66;43;03m# pylint: disable=protected-access\u001B[39;49;00m\n\u001B[1;32m 140\u001B[0m \u001B[43m \u001B[49m\u001B[43mflat_inputs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcaptured_inputs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfunction\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcaptured_inputs\u001B[49m\n\u001B[1;32m 141\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m/opt/homebrew/anaconda3/envs/ml_env/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py:1322\u001B[0m, in \u001B[0;36mConcreteFunction._call_flat\u001B[0;34m(self, tensor_inputs, captured_inputs)\u001B[0m\n\u001B[1;32m 1318\u001B[0m possible_gradient_type \u001B[38;5;241m=\u001B[39m gradients_util\u001B[38;5;241m.\u001B[39mPossibleTapeGradientTypes(args)\n\u001B[1;32m 1319\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m (possible_gradient_type \u001B[38;5;241m==\u001B[39m gradients_util\u001B[38;5;241m.\u001B[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001B[1;32m 1320\u001B[0m \u001B[38;5;129;01mand\u001B[39;00m executing_eagerly):\n\u001B[1;32m 1321\u001B[0m \u001B[38;5;66;03m# No tape is watching; skip to running the function.\u001B[39;00m\n\u001B[0;32m-> 1322\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_inference_function\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcall_preflattened\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1323\u001B[0m forward_backward \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_select_forward_and_backward_functions(\n\u001B[1;32m 1324\u001B[0m args,\n\u001B[1;32m 1325\u001B[0m possible_gradient_type,\n\u001B[1;32m 1326\u001B[0m executing_eagerly)\n\u001B[1;32m 1327\u001B[0m forward_function, args_with_tangents \u001B[38;5;241m=\u001B[39m forward_backward\u001B[38;5;241m.\u001B[39mforward()\n",
"File \u001B[0;32m/opt/homebrew/anaconda3/envs/ml_env/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:216\u001B[0m, in \u001B[0;36mAtomicFunction.call_preflattened\u001B[0;34m(self, args)\u001B[0m\n\u001B[1;32m 214\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcall_preflattened\u001B[39m(\u001B[38;5;28mself\u001B[39m, args: Sequence[core\u001B[38;5;241m.\u001B[39mTensor]) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Any:\n\u001B[1;32m 215\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001B[39;00m\n\u001B[0;32m--> 216\u001B[0m flat_outputs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcall_flat\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 217\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfunction_type\u001B[38;5;241m.\u001B[39mpack_output(flat_outputs)\n",
"File \u001B[0;32m/opt/homebrew/anaconda3/envs/ml_env/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:251\u001B[0m, in \u001B[0;36mAtomicFunction.call_flat\u001B[0;34m(self, *args)\u001B[0m\n\u001B[1;32m 249\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m record\u001B[38;5;241m.\u001B[39mstop_recording():\n\u001B[1;32m 250\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_bound_context\u001B[38;5;241m.\u001B[39mexecuting_eagerly():\n\u001B[0;32m--> 251\u001B[0m outputs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_bound_context\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcall_function\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 252\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mname\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 253\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mlist\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 254\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mlen\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfunction_type\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mflat_outputs\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 255\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 256\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 257\u001B[0m outputs \u001B[38;5;241m=\u001B[39m make_call_op_in_graph(\n\u001B[1;32m 258\u001B[0m \u001B[38;5;28mself\u001B[39m,\n\u001B[1;32m 259\u001B[0m \u001B[38;5;28mlist\u001B[39m(args),\n\u001B[1;32m 260\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_bound_context\u001B[38;5;241m.\u001B[39mfunction_call_options\u001B[38;5;241m.\u001B[39mas_attrs(),\n\u001B[1;32m 261\u001B[0m )\n",
"File \u001B[0;32m/opt/homebrew/anaconda3/envs/ml_env/lib/python3.9/site-packages/tensorflow/python/eager/context.py:1500\u001B[0m, in \u001B[0;36mContext.call_function\u001B[0;34m(self, name, tensor_inputs, num_outputs)\u001B[0m\n\u001B[1;32m 1498\u001B[0m cancellation_context \u001B[38;5;241m=\u001B[39m cancellation\u001B[38;5;241m.\u001B[39mcontext()\n\u001B[1;32m 1499\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m cancellation_context \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m-> 1500\u001B[0m outputs \u001B[38;5;241m=\u001B[39m \u001B[43mexecute\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 1501\u001B[0m \u001B[43m \u001B[49m\u001B[43mname\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdecode\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mutf-8\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1502\u001B[0m \u001B[43m \u001B[49m\u001B[43mnum_outputs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mnum_outputs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1503\u001B[0m \u001B[43m \u001B[49m\u001B[43minputs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtensor_inputs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1504\u001B[0m \u001B[43m \u001B[49m\u001B[43mattrs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mattrs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1505\u001B[0m \u001B[43m \u001B[49m\u001B[43mctx\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1506\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1507\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 1508\u001B[0m outputs \u001B[38;5;241m=\u001B[39m execute\u001B[38;5;241m.\u001B[39mexecute_with_cancellation(\n\u001B[1;32m 1509\u001B[0m name\u001B[38;5;241m.\u001B[39mdecode(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mutf-8\u001B[39m\u001B[38;5;124m\"\u001B[39m),\n\u001B[1;32m 1510\u001B[0m num_outputs\u001B[38;5;241m=\u001B[39mnum_outputs,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 1514\u001B[0m cancellation_manager\u001B[38;5;241m=\u001B[39mcancellation_context,\n\u001B[1;32m 1515\u001B[0m )\n",
"File \u001B[0;32m/opt/homebrew/anaconda3/envs/ml_env/lib/python3.9/site-packages/tensorflow/python/eager/execute.py:53\u001B[0m, in \u001B[0;36mquick_execute\u001B[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001B[0m\n\u001B[1;32m 51\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m 52\u001B[0m ctx\u001B[38;5;241m.\u001B[39mensure_initialized()\n\u001B[0;32m---> 53\u001B[0m tensors \u001B[38;5;241m=\u001B[39m \u001B[43mpywrap_tfe\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mTFE_Py_Execute\u001B[49m\u001B[43m(\u001B[49m\u001B[43mctx\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_handle\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdevice_name\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mop_name\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 54\u001B[0m \u001B[43m \u001B[49m\u001B[43minputs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mattrs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnum_outputs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 55\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m core\u001B[38;5;241m.\u001B[39m_NotOkStatusException \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m 56\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m name \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
"\u001B[0;31mKeyboardInterrupt\u001B[0m: "
]
}
],
"execution_count": 14
"execution_count": 4
},
{
"cell_type": "markdown",
@ -685,7 +649,7 @@
}
},
"cell_type": "code",
"source": "weather_data = pd.read_csv('data/weather_data_complete.csv')",
"source": "weather_data = pd.read_parquet('./data/weather_data_complete.parquet')",
"outputs": [],
"execution_count": 15
},
@ -738,7 +702,7 @@
"source": [
"\n",
"# Esempio di utilizzo\n",
"olive_varieties = pd.read_csv('data/variety_olive_oil_production.csv')\n",
"olive_varieties = pd.read_csv('./data/variety_olive_oil_production.csv')\n",
"\n",
"\n",
"def add_olive_water_consumption_correlation(dataset):\n",
@ -775,7 +739,7 @@
"\n",
"olive_varieties = add_olive_water_consumption_correlation(olive_varieties)\n",
"\n",
"olive_varieties.to_csv(\"./data/olive_varieties.csv\")"
"olive_varieties.to_parquet(\"./data/olive_varieties.parquet\")"
],
"outputs": [],
"execution_count": 9
@ -1095,7 +1059,7 @@
"\n",
"simulated_data = simulate_olive_production(weather_data, olive_varieties, 100, random_state_value)\n",
"\n",
"simulated_data.to_csv(\"./data/simulated_data.csv\")\n",
"simulated_data.to_parquet(\"./data/simulated_data.parquet\")\n",
"\n",
"\n",
"# Funzione per visualizzare il mapping delle tecniche\n",
@ -1136,7 +1100,7 @@
},
"cell_type": "code",
"source": [
"simulated_data = pd.read_csv(\"./data/simulated_data.csv\")\n",
"simulated_data = pd.read_parquet(\"./data/simulated_data.parquet\")\n",
"\n",
"\n",
"def clean_column_names(df):\n",
@ -1681,8 +1645,6 @@
}
},
"source": [
"\n",
"\n",
"X = full_data[['temp', 'humidity', 'cloudcover', 'solarenergy', 'uvindex']]\n",
"y = full_data['olive_prod']\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=random_state_value)\n",
@ -1761,40 +1723,6 @@
"## 6. Previsioni Meteo con ARIMA"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"source": [
"def train_weather_models(weather_data):\n",
" models = {}\n",
" for column in ['temp', 'humidity', 'cloudcover', 'solarenergy', 'uvindex']:\n",
" model = ARIMA(weather_data[column], order=(1, 1, 1))\n",
" models[column] = model.fit()\n",
" return models\n",
"\n",
"\n",
"def forecast_weather(models, steps=365):\n",
" forecasts = {}\n",
" for column, model in models.items():\n",
" forecast = model.forecast(steps=steps)\n",
" forecasts[column] = forecast\n",
" return pd.DataFrame(forecasts)\n",
"\n",
"\n",
"weather_models = train_weather_models(weather_data)\n",
"next_year_weather = forecast_weather(weather_models)\n",
"\n",
"# Visualizzazione delle previsioni\n",
"fig, axes = plt.subplots(3, 1, figsize=(15, 20))\n",
"next_year_weather['temp'].plot(ax=axes[0], title='Previsione Temperatura')\n",
"next_year_weather['humidity'].plot(ax=axes[1], title='Previsione Umidità')\n",
"next_year_weather['solarenergy'].plot(ax=axes[2], title='Previsione Energia Solare')\n",
"plt.tight_layout()\n",
"plt.show()"
],
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},