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giuseppenuc committed 2024-10-29 00:31:18 +01:00
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{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":9725208,"sourceType":"datasetVersion","datasetId":5950719},{"sourceId":9730815,"sourceType":"datasetVersion","datasetId":5954901}],"dockerImageVersionId":30787,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Analisi e Previsione della Produzione di Olio d'Oliva\n\nQuesto notebook esplora la relazione tra i dati meteorologici e la produzione annuale di olio d'oliva, con l'obiettivo di creare un modello predittivo.","metadata":{}},{"cell_type":"code","source":"#!zip -r output.zip /kaggle/working/","metadata":{"execution":{"iopub.status.busy":"2024-10-28T06:41:20.895051Z","iopub.execute_input":"2024-10-28T06:41:20.895739Z","iopub.status.idle":"2024-10-28T06:41:20.899608Z","shell.execute_reply.started":"2024-10-28T06:41:20.895699Z","shell.execute_reply":"2024-10-28T06:41:20.898722Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"import tensorflow as tf\n\nprint(f\"Keras version: {tf.keras.__version__}\")\nprint(f\"TensorFlow version: {tf.__version__}\")\n\n# GPU configuration\ngpus = tf.config.experimental.list_physical_devices('GPU')\nif gpus:\n try:\n for gpu in gpus:\n tf.config.experimental.set_memory_growth(gpu, True)\n logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\n except RuntimeError as e:\n print(e)","metadata":{"ExecuteTime":{"end_time":"2024-10-25T21:05:00.337046Z","start_time":"2024-10-25T21:04:03.960543Z"},"execution":{"iopub.status.busy":"2024-10-28T18:17:11.580753Z","iopub.execute_input":"2024-10-28T18:17:11.581122Z","iopub.status.idle":"2024-10-28T18:17:11.883020Z","shell.execute_reply.started":"2024-10-28T18:17:11.581083Z","shell.execute_reply":"2024-10-28T18:17:11.881838Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Keras version: 3.3.3\nTensorFlow version: 2.16.1\n1 Physical GPUs, 1 Logical GPUs\n","output_type":"stream"}]},{"cell_type":"code","source":"# Test semplice per verificare che la GPU funzioni\ndef test_gpu():\n print(\"TensorFlow version:\", tf.__version__)\n print(\"\\nDispositivi disponibili:\")\n print(tf.config.list_physical_devices())\n\n # Creiamo e moltiplichiamo due tensori sulla GPU\n with tf.device('/GPU:0'):\n a = tf.random.normal([10000, 10000])\n b = tf.random.normal([10000, 10000])\n c = tf.matmul(a, b)\n\n print(\"\\nShape del risultato:\", c.shape)\n print(\"Device del tensore:\", c.device)\n return \"Test completato con successo!\"\n\n\ntest_gpu()","metadata":{"ExecuteTime":{"end_time":"2024-10-25T21:05:14.642072Z","start_time":"2024-10-25T21:05:11.794331Z"},"execution":{"iopub.status.busy":"2024-10-28T18:17:14.607427Z","iopub.execute_input":"2024-10-28T18:17:14.608081Z","iopub.status.idle":"2024-10-28T18:17:14.758117Z","shell.execute_reply.started":"2024-10-28T18:17:14.608043Z","shell.execute_reply":"2024-10-28T18:17:14.757247Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"TensorFlow version: 2.16.1\n\nDispositivi disponibili:\n[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n\nShape del risultato: (10000, 10000)\nDevice del tensore: /job:localhost/replica:0/task:0/device:GPU:0\n","output_type":"stream"},{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"'Test completato con successo!'"},"metadata":{}}]},{"cell_type":"code","source":"!pip install numpy\n!pip install pandas\n\n!pip install keras\n!pip install scikit-learn\n!pip install matplotlib\n!pip install joblib\n!pip install pyarrow\n!pip install fastparquet\n!pip install scipy\n!pip install seaborn\n!pip install tqdm","metadata":{"ExecuteTime":{"end_time":"2024-10-25T21:05:34.003058Z","start_time":"2024-10-25T21:05:20.138514Z"},"execution":{"iopub.status.busy":"2024-10-28T06:41:20.930792Z","iopub.execute_input":"2024-10-28T06:41:20.931152Z","iopub.status.idle":"2024-10-28T06:43:20.309151Z","shell.execute_reply.started":"2024-10-28T06:41:20.931111Z","shell.execute_reply":"2024-10-28T06:43:20.308064Z"},"trusted":true},"execution_count":31,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/pty.py:89: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n pid, fd = os.forkpty()\n","output_type":"stream"},{"name":"stdout","text":"^C\n\u001b[31mERROR: OLine truncated {"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":9725208,"sourceType":"datasetVersion","datasetId":5950719},{"sourceId":9730815,"sourceType":"datasetVersion","datasetId":5954901}],"dockerImageVersionId":30787,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Analisi e Previsione della Produzione di Olio d'Oliva\n\nQuesto notebook esplora la relazione tra i dati meteorologici e la produzione annuale di olio d'oliva, con l'obiettivo di creare un modello predittivo.","metadata":{}},{"cell_type":"code","source":"import os\n\n# Rimuove il file se esiste\nif os.path.exists('output.zip'):\n os.remove('output.zip')\n \n!zip -r output.zip /kaggle/working/","metadata":{"execution":{"iopub.status.busy":"2024-10-28T22:02:37.712657Z","iopub.execute_input":"2024-10-28T22:02:37.713572Z","iopub.status.idle":"2024-10-28T22:05:13.962589Z","shell.execute_reply.started":"2024-10-28T22:02:37.713526Z","shell.execute_reply":"2024-10-28T22:05:13.961548Z"},"trusted":true},"execution_count":35,"outputs":[{"name":"stdout","text":" adding: kaggle/working/ (stored 0%)\n adding: kaggle/working/best_model_01_0.0196.keras (deflated 9%)\n adding: kaggle/working/weather_data_extended.parquet (deflated 32%)\n adding: kaggle/working/models/ (stored 0%)\n adding: kaggle/working/models/scaler_x.joblib (deflated 52%)\n adding: kaggle/working/models/solarenergy/ (stored 0%)\n adding: kaggle/working/models/solarenergy/scaler_y.joblib (deflated 44%)\n adding: kaggle/working/models/solarenergy/model.joblib (deflated 12%)\n adding: kaggle/working/models/oli_transformer/ (stored 0%)\n adding: kaggle/working/models/oli_transformer/scaler_y.joblib (deflated 23%)\n adding: kaggle/working/models/oli_transformer/olive_transformer.keras (deflated 10%)\n adding: kaggle/working/models/oli_transformer/scaler_static.joblib (deflated 64%)\n adding: kaggle/working/models/oli_transformer/scaler_temporal.joblib (deflated 25%)\n adding: kaggle/working/models/technique_mapping.joblib (deflated 17%)\n adding: kaggle/working/models/solarradiation/ (stored 0%)\n adding: kaggle/working/models/solarradiation/scaler_y.joblib (deflated 43%)\n adding: kaggle/working/models/solarradiation/model.joblib (deflated 12%)\n adding: kaggle/working/models/uvindex/ (stored 0%)\n adding: kaggle/working/models/uvindex/scaler_y.joblib (deflated 44%)\n adding: kaggle/working/models/uvindex/model.joblib (deflated 12%)\n adding: kaggle/working/models/target_variables.joblib (deflated 9%)\n adding: kaggle/working/models/model_types.joblib (deflated 9%)\n adding: kaggle/working/best_model_01_0.0114.keras (deflated 9%)\n adding: kaggle/working/best_model_03_0.0186.keras (deflated 10%)\n adding: kaggle/working/logs/ (stored 0%)\n adding: kaggle/working/logs/train/ (stored 0%)\n adding: kaggle/working/logs/train/events.out.tfevents.1730145102.aad4d7321334.30.4.v2 (deflated 5%)\n adding: kaggle/working/logs/train/events.out.tfevents.1730146132.aad4d7321334.30.9.v2 (deflated 92%)\n adding: kaggle/working/logs/train/events.out.tfevents.1730144491.aad4d7321334.30.2.v2 (deflated 5%)\n adding: kaggle/working/logs/train/events.out.tfevents.1730147779.aad4d7321334.30.15.v2 (deflated 73%)\n adding: kaggle/working/logs/train/events.out.tfevents.1730145205.aad4d7321334.30.5.v2 (deflated 92%)\n adding: kaggle/working/logs/train/events.out.tfevents.1730144772.aad4d7321334.30.3.v2 (deflated 6%)\n adding: kaggle/working/logs/train/events.out.tfevents.1730144354.aad4d7321334.30.0.v2 (deflated 5%)\n adding: kaggle/working/logs/train/events.out.tfevents.1730147427.aad4d7321334.30.14.v2 (deflated 92%)\n adding: kaggle/working/logs/train/events.out.tfevents.1730147347.aad4d7321334.30.13.v2 (deflated 92%)\n adding: kaggle/working/logs/train/events.out.tfevents.1730144426.aad4d7321334.30.1.v2 (deflated 5%)\n adding: kaggle/working/logs/train/events.out.tfevents.1730147042.aad4d7321334.30.11.v2 (deflated 92%)\n adding: kaggle/working/logs/train/events.out.tfevents.1730145698.aad4d7321334.30.7.v2 (deflated 92%)\n adding: kaggle/working/logs/validation/ (stored 0%)\n adding: kaggle/working/logs/validation/events.out.tfevents.1730147252.aad4d7321334.30.12.v2 (deflated 26%)\n adding: kaggle/working/logs/validation/events.out.tfevents.1730145415.aad4d7321334.30.6.v2 (deflated 26%)\n adding: kaggle/working/logs/validation/events.out.tfevents.1730147990.aad4d7321334.30.16.v2 (deflated 77%)\n adding: kaggle/working/logs/validation/events.out.tfevents.1730146342.aad4d7321334.30.10.v2 (deflated 26%)\n adding: kaggle/working/logs/validation/events.out.tfeventLine truncated