1859 lines
1.1 MiB
1859 lines
1.1 MiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Analisi e Previsione della Produzione di Olio d'Oliva\n",
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"\n",
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"Questo notebook esplora la relazione tra i dati meteorologici e la produzione annuale di olio d'oliva, con l'obiettivo di creare un modello predittivo."
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]
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-10-21T23:24:45.591951Z",
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"start_time": "2024-10-21T23:23:57.902382Z"
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}
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},
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"cell_type": "code",
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"source": [
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"!pip install numpy\n",
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"!pip install pandas\n",
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"!pip install tensorflow\n",
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"!pip install keras\n",
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"!pip install scikit-learn\n",
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"!pip install matplotlib\n",
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"!pip install joblib\n",
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"!pip install pyarrow\n",
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"!pip install fastparquet\n",
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"!pip install scipy\n",
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"!pip install seaborn\n",
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"!pip install pysolar"
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],
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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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"Requirement already satisfied: numpy in /usr/local/anaconda3/lib/python3.12/site-packages (1.26.4)\r\n",
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"Requirement already satisfied: pandas in /usr/local/anaconda3/lib/python3.12/site-packages (2.2.2)\r\n",
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"Requirement already satisfied: numpy>=1.26.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from pandas) (1.26.4)\r\n",
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"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/anaconda3/lib/python3.12/site-packages (from pandas) (2.9.0.post0)\r\n",
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"Requirement already satisfied: pytz>=2020.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from pandas) (2024.1)\r\n",
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"Requirement already satisfied: tensorflow in /usr/local/anaconda3/lib/python3.12/site-packages (2.16.2)\r\n",
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"Requirement already satisfied: absl-py>=1.0.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (2.1.0)\r\n",
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"Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorflow) (0.6.0)\r\n",
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"Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorboard<2.17,>=2.16->tensorflow) (0.7.2)\r\n",
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"Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from tensorboard<2.17,>=2.16->tensorflow) (3.0.3)\r\n",
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"Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from werkzeug>=1.0.1->tensorboard<2.17,>=2.16->tensorflow) (2.1.3)\r\n",
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"Requirement already satisfied: markdown-it-py<3.0.0,>=2.2.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from rich->keras>=3.0.0->tensorflow) (2.2.0)\r\n",
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"Requirement already satisfied: mdurl~=0.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from markdown-it-py<3.0.0,>=2.2.0->rich->keras>=3.0.0->tensorflow) (0.1.0)\r\n",
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"Requirement already satisfied: keras in /usr/local/anaconda3/lib/python3.12/site-packages (3.6.0)\r\n",
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"Requirement already satisfied: absl-py in /usr/local/anaconda3/lib/python3.12/site-packages (from keras) (2.1.0)\r\n",
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"Requirement already satisfied: numpy in /usr/local/anaconda3/lib/python3.12/site-packages (from keras) (1.26.4)\r\n",
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"Requirement already satisfied: rich in /usr/local/anaconda3/lib/python3.12/site-packages (from keras) (13.3.5)\r\n",
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"Requirement already satisfied: namex in /usr/local/anaconda3/lib/python3.12/site-packages (from keras) (0.0.8)\r\n",
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"Requirement already satisfied: typing-extensions>=4.5.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from optree->keras) (4.11.0)\r\n",
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"Requirement already satisfied: mdurl~=0.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from markdown-it-py<3.0.0,>=2.2.0->rich->keras) (0.1.0)\r\n",
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"Requirement already satisfied: numpy>=1.19.5 in /usr/local/anaconda3/lib/python3.12/site-packages (from scikit-learn) (1.26.4)\r\n",
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"Requirement already satisfied: six>=1.5 in /usr/local/anaconda3/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\r\n",
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"Requirement already satisfied: pandas>=1.2 in /usr/local/anaconda3/lib/python3.12/site-packages (from seaborn) (2.2.2)\r\n",
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"Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/anaconda3/lib/python3.12/site-packages (from seaborn) (3.9.2)\r\n",
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"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.0)\r\n",
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"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.51.0)\r\n",
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"Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.4)\r\n",
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"Requirement already satisfied: pillow>=8 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.3.0)\r\n",
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"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.0.9)\r\n",
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"Requirement already satisfied: python-dateutil>=2.7 in /usr/local/anaconda3/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\r\n",
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"Requirement already satisfied: six>=1.5 in /usr/local/anaconda3/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\r\n",
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"Collecting pysolar\r\n",
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" Downloading pysolar-0.11-py3-none-any.whl.metadata (331 bytes)\r\n",
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"Requirement already satisfied: numpy in /usr/local/anaconda3/lib/python3.12/site-packages (from pysolar) (1.26.4)\r\n",
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"Downloading pysolar-0.11-py3-none-any.whl (47 kB)\r\n",
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"\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",
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"\u001B[?25hInstalling collected packages: pysolar\r\n",
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"Successfully installed pysolar-0.11\r\n"
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]
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}
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],
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"execution_count": 244
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},
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{
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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-10-22T00:12:54.872744Z",
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"start_time": "2024-10-22T00:12:54.868179Z"
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}
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},
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from sklearn.ensemble import RandomForestRegressor\n",
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"from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import MinMaxScaler\n",
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"from tensorflow.keras.layers import Input, Dense, Dropout, Bidirectional, LSTM, LayerNormalization, Add, GlobalAveragePooling1D, Activation, BatchNormalization, MultiHeadAttention\n",
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"from tensorflow.keras.models import Model\n",
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"from tensorflow.keras.regularizers import l2\n",
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"from tensorflow.keras.optimizers import Adam\n",
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"from tensorflow.keras.callbacks import EarlyStopping\n",
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"from datetime import datetime\n",
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"import os\n",
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"import json\n",
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"import joblib\n",
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"import re\n",
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"import pyarrow as pa\n",
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"import pyarrow.parquet as pq\n",
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"\n",
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"\n",
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"random_state_value = 42"
|
|
],
|
|
"outputs": [],
|
|
"execution_count": 3
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": "## 1. Caricamento e preparazione dei Dati Meteo"
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-22T00:13:13.482056Z",
|
|
"start_time": "2024-10-22T00:12:57.210546Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"# Function to convert csv to parquet\n",
|
|
"def csv_to_parquet(csv_file, parquet_file, chunksize=100000):\n",
|
|
" writer = None\n",
|
|
"\n",
|
|
" for chunk in pd.read_csv(csv_file, chunksize=chunksize):\n",
|
|
" if writer is None:\n",
|
|
"\n",
|
|
" table = pa.Table.from_pandas(chunk)\n",
|
|
" writer = pq.ParquetWriter(parquet_file, table.schema)\n",
|
|
" else:\n",
|
|
" table = pa.Table.from_pandas(chunk)\n",
|
|
"\n",
|
|
" writer.write_table(table)\n",
|
|
"\n",
|
|
" if writer:\n",
|
|
" writer.close()\n",
|
|
"\n",
|
|
" print(f\"File conversion completed : {csv_file} -> {parquet_file}\")\n",
|
|
"\n",
|
|
"\n",
|
|
"def read_json_files(folder_path):\n",
|
|
" all_data = []\n",
|
|
"\n",
|
|
" file_list = sorted(os.listdir(folder_path))\n",
|
|
"\n",
|
|
" for filename in file_list:\n",
|
|
" if filename.endswith('.json'):\n",
|
|
" file_path = os.path.join(folder_path, filename)\n",
|
|
" try:\n",
|
|
" with open(file_path, 'r') as file:\n",
|
|
" data = json.load(file)\n",
|
|
" all_data.extend(data['days'])\n",
|
|
" except Exception as e:\n",
|
|
" print(f\"Error processing file '{filename}': {str(e)}\")\n",
|
|
"\n",
|
|
" return all_data\n",
|
|
"\n",
|
|
"\n",
|
|
"def create_weather_dataset(data):\n",
|
|
" dataset = []\n",
|
|
" seen_datetimes = set()\n",
|
|
"\n",
|
|
" for day in data:\n",
|
|
" date = day['datetime']\n",
|
|
" for hour in day['hours']:\n",
|
|
" datetime_str = f\"{date} {hour['datetime']}\"\n",
|
|
"\n",
|
|
" # Verifico se questo datetime è già stato visto\n",
|
|
" if datetime_str in seen_datetimes:\n",
|
|
" continue\n",
|
|
"\n",
|
|
" seen_datetimes.add(datetime_str)\n",
|
|
"\n",
|
|
" if isinstance(hour['preciptype'], list):\n",
|
|
" preciptype = \"__\".join(hour['preciptype'])\n",
|
|
" else:\n",
|
|
" preciptype = hour['preciptype'] if hour['preciptype'] else \"\"\n",
|
|
"\n",
|
|
" conditions = hour['conditions'].replace(', ', '__').replace(' ', '_').lower()\n",
|
|
"\n",
|
|
" row = {\n",
|
|
" 'datetime': datetime_str,\n",
|
|
" 'temp': hour['temp'],\n",
|
|
" 'feelslike': hour['feelslike'],\n",
|
|
" 'humidity': hour['humidity'],\n",
|
|
" 'dew': hour['dew'],\n",
|
|
" 'precip': hour['precip'],\n",
|
|
" 'snow': hour['snow'],\n",
|
|
" 'preciptype': preciptype.lower(),\n",
|
|
" 'windspeed': hour['windspeed'],\n",
|
|
" 'winddir': hour['winddir'],\n",
|
|
" 'pressure': hour['pressure'],\n",
|
|
" 'cloudcover': hour['cloudcover'],\n",
|
|
" 'visibility': hour['visibility'],\n",
|
|
" 'solarradiation': hour['solarradiation'],\n",
|
|
" 'solarenergy': hour['solarenergy'],\n",
|
|
" 'uvindex': hour['uvindex'],\n",
|
|
" 'conditions': conditions,\n",
|
|
" 'tempmax': day['tempmax'],\n",
|
|
" 'tempmin': day['tempmin'],\n",
|
|
" 'precipprob': day['precipprob'],\n",
|
|
" 'precipcover': day['precipcover']\n",
|
|
" }\n",
|
|
" dataset.append(row)\n",
|
|
"\n",
|
|
" dataset.sort(key=lambda x: datetime.strptime(x['datetime'], \"%Y-%m-%d %H:%M:%S\"))\n",
|
|
"\n",
|
|
" return pd.DataFrame(dataset)\n",
|
|
"\n",
|
|
"\n",
|
|
"folder_path = './data/weather'\n",
|
|
"raw_data = read_json_files(folder_path)\n",
|
|
"weather_data = create_weather_dataset(raw_data)\n",
|
|
"weather_data['datetime'] = pd.to_datetime(weather_data['datetime'], errors='coerce')\n",
|
|
"weather_data['date'] = weather_data['datetime'].dt.date\n",
|
|
"weather_data = weather_data.dropna(subset=['datetime'])\n",
|
|
"weather_data['datetime'] = pd.to_datetime(weather_data['datetime'])\n",
|
|
"weather_data['year'] = weather_data['datetime'].dt.year\n",
|
|
"weather_data['month'] = weather_data['datetime'].dt.month\n",
|
|
"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')"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"File conversion completed : ./data/weather_data.csv -> ./data/weather_data.parquet\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 4
|
|
},
|
|
{
|
|
"metadata": {},
|
|
"cell_type": "markdown",
|
|
"source": ""
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-22T17:50:50.867820Z",
|
|
"start_time": "2024-10-22T16:34:54.050578Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"# Funzione per creare sequenze per LSTM\n",
|
|
"def create_sequences(X, y, timesteps):\n",
|
|
" Xs, ys = [], []\n",
|
|
" for i in range(len(X) - timesteps):\n",
|
|
" Xs.append(X[i:i+timesteps])\n",
|
|
" ys.append(y[i+timesteps])\n",
|
|
" return np.array(Xs), np.array(ys)\n",
|
|
"\n",
|
|
"# Funzioni per costruire il modello LSTM avanzato\n",
|
|
"def create_residual_lstm_layer(x, units, dropout_rate, l2_reg=0.01):\n",
|
|
" residual = x\n",
|
|
" x = Bidirectional(LSTM(units, return_sequences=True, kernel_regularizer=l2(l2_reg)))(x)\n",
|
|
" x = LayerNormalization()(x)\n",
|
|
" x = Dropout(dropout_rate)(x)\n",
|
|
" # Adjust residual dimension\n",
|
|
" if int(residual.shape[-1]) != 2 * units:\n",
|
|
" residual = Dense(2 * units, activation='linear')(residual)\n",
|
|
" x = Add()([x, residual])\n",
|
|
" return x\n",
|
|
"\n",
|
|
"def attention_block(x, units, num_heads=8):\n",
|
|
" attention = MultiHeadAttention(num_heads=num_heads, key_dim=units)(x, x)\n",
|
|
" x = Add()([x, attention])\n",
|
|
" x = LayerNormalization()(x)\n",
|
|
" return x\n",
|
|
"\n",
|
|
"def build_advanced_model(input_shape, l2_lambda=0.005):\n",
|
|
" inputs = Input(shape=input_shape)\n",
|
|
" x = create_residual_lstm_layer(inputs, 64, 0.3, l2_lambda)\n",
|
|
" x = create_residual_lstm_layer(x, 32, 0.3, l2_lambda)\n",
|
|
" x = create_residual_lstm_layer(x, 16, 0.2, l2_lambda)\n",
|
|
" x = attention_block(x, 16, num_heads=8)\n",
|
|
" x = GlobalAveragePooling1D()(x)\n",
|
|
" x = Dense(32, kernel_regularizer=l2(l2_lambda))(x)\n",
|
|
" x = BatchNormalization()(x)\n",
|
|
" x = Activation('swish')(x)\n",
|
|
" x = Dropout(0.3)(x)\n",
|
|
" x = Dense(16, kernel_regularizer=l2(l2_lambda))(x)\n",
|
|
" x = BatchNormalization()(x)\n",
|
|
" x = Activation('swish')(x)\n",
|
|
" x = Dropout(0.2)(x)\n",
|
|
" outputs = Dense(1, kernel_regularizer=l2(l2_lambda))(x)\n",
|
|
" model = Model(inputs=inputs, outputs=outputs)\n",
|
|
" return model\n",
|
|
"\n",
|
|
"def get_season(date):\n",
|
|
" month = date.month\n",
|
|
" day = date.day\n",
|
|
" if (month == 12 and day >= 21) or (month <= 3 and day < 20):\n",
|
|
" return 'Winter'\n",
|
|
" elif (month == 3 and day >= 20) or (month <= 6 and day < 21):\n",
|
|
" return 'Spring'\n",
|
|
" elif (month == 6 and day >= 21) or (month <= 9 and day < 23):\n",
|
|
" return 'Summer'\n",
|
|
" elif (month == 9 and day >= 23) or (month <= 12 and day < 21):\n",
|
|
" return 'Autumn'\n",
|
|
" else:\n",
|
|
" return 'Unknown'\n",
|
|
"\n",
|
|
"def get_time_period(hour):\n",
|
|
" if 5 <= hour < 12:\n",
|
|
" return 'Morning'\n",
|
|
" elif 12 <= hour < 17:\n",
|
|
" return 'Afternoon'\n",
|
|
" elif 17 <= hour < 21:\n",
|
|
" return 'Evening'\n",
|
|
" else:\n",
|
|
" return 'Night'\n",
|
|
"\n",
|
|
"def add_time_features(df):\n",
|
|
" df['datetime'] = pd.to_datetime(df['datetime'])\n",
|
|
" df['timestamp'] = df['datetime'].astype(np.int64) // 10 ** 9\n",
|
|
" df['year'] = df['datetime'].dt.year\n",
|
|
" df['month'] = df['datetime'].dt.month\n",
|
|
" df['day'] = df['datetime'].dt.day\n",
|
|
" df['hour'] = df['datetime'].dt.hour\n",
|
|
" df['minute'] = df['datetime'].dt.minute\n",
|
|
" df['hour_sin'] = np.sin(df['hour'] * (2 * np.pi / 24))\n",
|
|
" df['hour_cos'] = np.cos(df['hour'] * (2 * np.pi / 24))\n",
|
|
" df['day_of_week'] = df['datetime'].dt.dayofweek\n",
|
|
" df['day_of_year'] = df['datetime'].dt.dayofyear\n",
|
|
" df['week_of_year'] = df['datetime'].dt.isocalendar().week.astype(int)\n",
|
|
" df['quarter'] = df['datetime'].dt.quarter\n",
|
|
" df['is_month_end'] = df['datetime'].dt.is_month_end.astype(int)\n",
|
|
" df['is_quarter_end'] = df['datetime'].dt.is_quarter_end.astype(int)\n",
|
|
" df['is_year_end'] = df['datetime'].dt.is_year_end.astype(int)\n",
|
|
" df['month_sin'] = np.sin(df['month'] * (2 * np.pi / 12))\n",
|
|
" df['month_cos'] = np.cos(df['month'] * (2 * np.pi / 12))\n",
|
|
" df['day_of_year_sin'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25))\n",
|
|
" df['day_of_year_cos'] = np.cos(df['day_of_year'] * (2 * np.pi / 365.25))\n",
|
|
" df['season'] = df['datetime'].apply(get_season)\n",
|
|
" df['time_period'] = df['hour'].apply(get_time_period)\n",
|
|
" return df\n",
|
|
"\n",
|
|
"# Carica il dataset\n",
|
|
"weather_data = pd.read_csv('./data/weather_data.csv')\n",
|
|
"\n",
|
|
"# Aggiungi le caratteristiche temporali\n",
|
|
"weather_data = add_time_features(weather_data)\n",
|
|
"\n",
|
|
"# Encoding delle variabili categoriali\n",
|
|
"weather_data = pd.get_dummies(weather_data, columns=['season', 'time_period'], drop_first=True)\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",
|
|
"\n",
|
|
"# Aggiorna le target variables se necessario\n",
|
|
"target_variables = ['solarradiation', 'solarenergy', 'uvindex']\n",
|
|
"\n",
|
|
"# Seleziona le features\n",
|
|
"features = [\n",
|
|
" 'temp', 'tempmin', 'tempmax', 'humidity', 'cloudcover', 'windspeed', 'pressure', 'visibility',\n",
|
|
" 'hour_sin', 'hour_cos', 'month_sin', 'month_cos', 'day_of_year_sin', 'day_of_year_cos',\n",
|
|
" ] + [col for col in weather_data.columns if 'season_' in col or 'time_period_' in col]\n",
|
|
"\n",
|
|
"# Prepara data_after_2010\n",
|
|
"data_after_2010 = data_after_2010.sort_values('datetime')\n",
|
|
"data_after_2010.set_index('datetime', inplace=True)\n",
|
|
"\n",
|
|
"# Interpola eventuali valori mancanti nelle variabili target\n",
|
|
"columns_to_interpolate = target_variables\n",
|
|
"for column in columns_to_interpolate:\n",
|
|
" data_after_2010[column] = data_after_2010[column].interpolate(method='time')\n",
|
|
"\n",
|
|
"# Rimuovi eventuali valori mancanti residui\n",
|
|
"data_after_2010.dropna(subset=features + target_variables, inplace=True)\n",
|
|
"\n",
|
|
"# Crea X e y\n",
|
|
"X = data_after_2010[features].values\n",
|
|
"y = data_after_2010[target_variables].values\n",
|
|
"\n",
|
|
"# Normalizza le features\n",
|
|
"scaler_X = MinMaxScaler()\n",
|
|
"X_scaled = scaler_X.fit_transform(X)\n",
|
|
"\n",
|
|
"# Normalizza le target variables\n",
|
|
"scalers_y = {}\n",
|
|
"y_scaled = np.zeros_like(y)\n",
|
|
"\n",
|
|
"for i, target in enumerate(target_variables):\n",
|
|
" scaler = MinMaxScaler()\n",
|
|
" y_scaled[:, i] = scaler.fit_transform(y[:, i].reshape(-1, 1)).flatten()\n",
|
|
" scalers_y[target] = scaler\n",
|
|
"\n",
|
|
"# Suddividi i dati in training e test\n",
|
|
"X_train_full, X_test, y_train_full, y_test = train_test_split(X_scaled, y_scaled, test_size=0.2, shuffle=False)\n",
|
|
"\n",
|
|
"# Suddividi ulteriormente in training e validation\n",
|
|
"X_train, X_val, y_train, y_val = train_test_split(X_train_full, y_train_full, test_size=0.2, shuffle=False)\n",
|
|
"\n",
|
|
"# Definisci il numero di timesteps (ad esempio, utilizziamo le ultime 24 ore)\n",
|
|
"timesteps = 24\n",
|
|
"num_features = X_train.shape[1]\n",
|
|
"\n",
|
|
"# Crea le sequenze per LSTM\n",
|
|
"def create_sequences(X, y, timesteps):\n",
|
|
" Xs, ys = [], []\n",
|
|
" for i in range(len(X) - timesteps):\n",
|
|
" Xs.append(X[i:i+timesteps])\n",
|
|
" ys.append(y[i+timesteps])\n",
|
|
" return np.array(Xs), np.array(ys)\n",
|
|
"\n",
|
|
"X_train_seq, y_train_seq = create_sequences(X_train, y_train, timesteps)\n",
|
|
"X_val_seq, y_val_seq = create_sequences(X_val, y_val, timesteps)\n",
|
|
"X_test_seq, y_test_seq = create_sequences(X_test, y_test, timesteps)\n",
|
|
"\n",
|
|
"# Costruisci il modello per ogni variabile target\n",
|
|
"models = {}\n",
|
|
"histories = {}\n",
|
|
"for i, target in enumerate(target_variables):\n",
|
|
" print(f\"Addestramento del modello per: {target}\")\n",
|
|
" model = build_advanced_model((timesteps, num_features), l2_lambda=0.001)\n",
|
|
" optimizer = Adam(learning_rate=0.001, clipnorm=1.0)\n",
|
|
" model.compile(optimizer=optimizer, loss='mean_squared_error')\n",
|
|
" early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
|
|
" history = model.fit(\n",
|
|
" X_train_seq, y_train_seq[:, i],\n",
|
|
" validation_data=(X_val_seq, y_val_seq[:, i]),\n",
|
|
" epochs=50,\n",
|
|
" batch_size=64,\n",
|
|
" callbacks=[early_stopping],\n",
|
|
" verbose=1\n",
|
|
" )\n",
|
|
" test_loss = model.evaluate(X_test_seq, y_test_seq[:, i])\n",
|
|
" print(f'Test MAE per {target}: {test_loss:.4f}')\n",
|
|
" models[target] = model\n",
|
|
" histories[target] = history\n",
|
|
"\n",
|
|
"# Previsione delle variabili mancanti per data_before_2010\n",
|
|
"# Prepara data_before_2010\n",
|
|
"data_before_2010 = data_before_2010.sort_values('datetime')\n",
|
|
"data_before_2010.set_index('datetime', inplace=True)\n",
|
|
"\n",
|
|
"# Assicurati che le features non abbiano valori mancanti\n",
|
|
"data_before_2010[features] = data_before_2010[features].ffill()\n",
|
|
"data_before_2010[features] = data_before_2010[features].bfill()\n",
|
|
"\n",
|
|
"# Crea X per data_before_2010\n",
|
|
"X_before = data_before_2010[features].values\n",
|
|
"X_before_scaled = scaler_X.transform(X_before)\n",
|
|
"\n",
|
|
"# Crea le sequenze per LSTM\n",
|
|
"X_before_seq = []\n",
|
|
"for i in range(len(X_before_scaled) - timesteps):\n",
|
|
" X_before_seq.append(X_before_scaled[i:i+timesteps])\n",
|
|
"X_before_seq = np.array(X_before_seq)\n",
|
|
"\n",
|
|
"# Prevedi le variabili mancanti\n",
|
|
"for i, target in enumerate(target_variables):\n",
|
|
" print(f\"Previsione di {target} per data_before_2010\")\n",
|
|
" y_pred_scaled = models[target].predict(X_before_seq)\n",
|
|
" # Ricostruisci i valori originali\n",
|
|
" scaler = scalers_y[target]\n",
|
|
" y_pred = scaler.inverse_transform(y_pred_scaled)\n",
|
|
"\n",
|
|
" # Allinea le previsioni con le date corrette\n",
|
|
" dates = data_before_2010.index[timesteps:]\n",
|
|
" data_before_2010.loc[dates, target] = y_pred\n",
|
|
"\n",
|
|
"# Gestisci eventuali valori iniziali mancanti\n",
|
|
"data_before_2010[target_variables] = data_before_2010[target_variables].bfill()\n",
|
|
"\n",
|
|
"# Combina data_before_2010 e data_after_2010\n",
|
|
"weather_data_complete = pd.concat([data_before_2010, data_after_2010], axis=0)\n",
|
|
"weather_data_complete = weather_data_complete.sort_index()\n",
|
|
"\n",
|
|
"# Salva il dataset completo\n",
|
|
"weather_data_complete.reset_index(inplace=True)\n",
|
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"weather_data_complete.to_csv('weather_data_complete.csv', index=False)\n"
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],
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"outputs": [
|
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{
|
|
"name": "stdout",
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"output_type": "stream",
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"text": [
|
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"Addestramento del modello per: solarradiation\n",
|
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"Epoch 1/50\n",
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"\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",
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"Epoch 2/50\n",
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"\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",
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"Epoch 3/50\n",
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"\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",
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"Epoch 4/50\n",
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"\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",
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"Epoch 5/50\n",
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"\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",
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"Epoch 6/50\n",
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"\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",
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"Epoch 7/50\n",
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"\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",
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"Epoch 8/50\n",
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"\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",
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"Epoch 9/50\n",
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"\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",
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"\u001B[1m810/810\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m23s\u001B[0m 29ms/step - loss: 0.0080\n",
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"Test MAE per solarradiation: 0.0090\n",
|
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"Addestramento del modello per: solarenergy\n",
|
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"Epoch 1/50\n",
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|
"\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",
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"Epoch 2/50\n",
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"\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",
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"Epoch 3/50\n",
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"\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",
|
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"Epoch 4/50\n",
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|
"\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",
|
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"Epoch 5/50\n",
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"\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",
|
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"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",
|
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"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",
|
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"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",
|
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"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",
|
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"Epoch 10/50\n",
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|
"\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",
|
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"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",
|
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"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",
|
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"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",
|
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"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",
|
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"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",
|
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"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",
|
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"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",
|
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"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",
|
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"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",
|
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"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",
|
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"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",
|
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"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",
|
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"Epoch 5/50\n",
|
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"\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",
|
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"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",
|
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"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",
|
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"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",
|
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"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",
|
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"\u001B[1m810/810\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m25s\u001B[0m 30ms/step - loss: 0.0089\n",
|
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"Test MAE per uvindex: 0.0103\n",
|
|
"Previsione di solarradiation per data_before_2010\n",
|
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"\u001B[1m7122/7122\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m228s\u001B[0m 32ms/step\n",
|
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"Previsione di solarenergy per data_before_2010\n",
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|
"\u001B[1m7122/7122\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m255s\u001B[0m 36ms/step\n",
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"Previsione di uvindex per data_before_2010\n",
|
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"\u001B[1m7122/7122\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m200s\u001B[0m 28ms/step\n"
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]
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},
|
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{
|
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"name": "stderr",
|
|
"output_type": "stream",
|
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"text": [
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"/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",
|
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" data_before_2010[target_variables] = data_before_2010[target_variables].fillna(method='bfill')\n"
|
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]
|
|
}
|
|
],
|
|
"execution_count": 14
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
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"## 2. Esplorazione dei Dati Meteo"
|
|
]
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-22T18:55:36.453530Z",
|
|
"start_time": "2024-10-22T18:55:34.601858Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": "weather_data = pd.read_csv('data/weather_data_complete.csv')",
|
|
"outputs": [],
|
|
"execution_count": 15
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-22T18:55:42.090691Z",
|
|
"start_time": "2024-10-22T18:55:37.944495Z"
|
|
}
|
|
},
|
|
"source": [
|
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"# Visualizzazione delle tendenze temporali\n",
|
|
"fig, axes = plt.subplots(5, 1, figsize=(15, 20))\n",
|
|
"weather_data.set_index('date')['temp'].plot(ax=axes[0], title='Temperatura Media Giornaliera')\n",
|
|
"weather_data.set_index('date')['humidity'].plot(ax=axes[1], title='Umidità Media Giornaliera')\n",
|
|
"weather_data.set_index('date')['solarradiation'].plot(ax=axes[2], title='Radiazione Solare Giornaliera')\n",
|
|
"weather_data.set_index('date')['solarenergy'].plot(ax=axes[3], title='Radiazione Solare Giornaliera')\n",
|
|
"weather_data.set_index('date')['precip'].plot(ax=axes[4], title='Precipitazioni Giornaliere')\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"text/plain": [
|
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"<Figure size 1500x2000 with 5 Axes>"
|
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],
|
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"execution_count": 16
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": "## 3. Simulazione dei Dati di Produzione Annuale"
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-22T16:01:48.994672Z",
|
|
"start_time": "2024-10-22T16:01:48.967065Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"\n",
|
|
"# Esempio di utilizzo\n",
|
|
"olive_varieties = pd.read_csv('data/variety_olive_oil_production.csv')\n",
|
|
"\n",
|
|
"\n",
|
|
"def add_olive_water_consumption_correlation(dataset):\n",
|
|
" # Dati simulati per il fabbisogno d'acqua e la correlazione con la temperatura\n",
|
|
" fabbisogno_acqua = {\n",
|
|
" \"Nocellara dell'Etna\": {\"Primavera\": 1200, \"Estate\": 2000, \"Autunno\": 1000, \"Inverno\": 500, \"Temperatura Ottimale\": 18, \"Resistenza\": \"Media\"},\n",
|
|
" \"Leccino\": {\"Primavera\": 1000, \"Estate\": 1800, \"Autunno\": 800, \"Inverno\": 400, \"Temperatura Ottimale\": 20, \"Resistenza\": \"Alta\"},\n",
|
|
" \"Frantoio\": {\"Primavera\": 1100, \"Estate\": 1900, \"Autunno\": 900, \"Inverno\": 450, \"Temperatura Ottimale\": 19, \"Resistenza\": \"Alta\"},\n",
|
|
" \"Coratina\": {\"Primavera\": 1300, \"Estate\": 2200, \"Autunno\": 1100, \"Inverno\": 550, \"Temperatura Ottimale\": 17, \"Resistenza\": \"Media\"},\n",
|
|
" \"Moraiolo\": {\"Primavera\": 1150, \"Estate\": 2100, \"Autunno\": 900, \"Inverno\": 480, \"Temperatura Ottimale\": 18, \"Resistenza\": \"Media\"},\n",
|
|
" \"Pendolino\": {\"Primavera\": 1050, \"Estate\": 1850, \"Autunno\": 850, \"Inverno\": 430, \"Temperatura Ottimale\": 20, \"Resistenza\": \"Alta\"},\n",
|
|
" \"Taggiasca\": {\"Primavera\": 1000, \"Estate\": 1750, \"Autunno\": 800, \"Inverno\": 400, \"Temperatura Ottimale\": 19, \"Resistenza\": \"Alta\"},\n",
|
|
" \"Canino\": {\"Primavera\": 1100, \"Estate\": 1900, \"Autunno\": 900, \"Inverno\": 450, \"Temperatura Ottimale\": 18, \"Resistenza\": \"Media\"},\n",
|
|
" \"Itrana\": {\"Primavera\": 1200, \"Estate\": 2000, \"Autunno\": 1000, \"Inverno\": 500, \"Temperatura Ottimale\": 17, \"Resistenza\": \"Media\"},\n",
|
|
" \"Ogliarola\": {\"Primavera\": 1150, \"Estate\": 1950, \"Autunno\": 900, \"Inverno\": 480, \"Temperatura Ottimale\": 18, \"Resistenza\": \"Media\"},\n",
|
|
" \"Biancolilla\": {\"Primavera\": 1050, \"Estate\": 1800, \"Autunno\": 850, \"Inverno\": 430, \"Temperatura Ottimale\": 19, \"Resistenza\": \"Alta\"}\n",
|
|
" }\n",
|
|
"\n",
|
|
" # Calcola il fabbisogno idrico annuale per ogni varietà\n",
|
|
" for varieta in fabbisogno_acqua:\n",
|
|
" fabbisogno_acqua[varieta][\"Annuale\"] = sum([fabbisogno_acqua[varieta][stagione] for stagione in [\"Primavera\", \"Estate\", \"Autunno\", \"Inverno\"]])\n",
|
|
"\n",
|
|
" # Aggiungiamo le nuove colonne al dataset\n",
|
|
" dataset[\"Fabbisogno Acqua Primavera (m³/ettaro)\"] = dataset[\"Varietà di Olive\"].apply(lambda x: fabbisogno_acqua[x][\"Primavera\"])\n",
|
|
" dataset[\"Fabbisogno Acqua Estate (m³/ettaro)\"] = dataset[\"Varietà di Olive\"].apply(lambda x: fabbisogno_acqua[x][\"Estate\"])\n",
|
|
" dataset[\"Fabbisogno Acqua Autunno (m³/ettaro)\"] = dataset[\"Varietà di Olive\"].apply(lambda x: fabbisogno_acqua[x][\"Autunno\"])\n",
|
|
" dataset[\"Fabbisogno Acqua Inverno (m³/ettaro)\"] = dataset[\"Varietà di Olive\"].apply(lambda x: fabbisogno_acqua[x][\"Inverno\"])\n",
|
|
" dataset[\"Fabbisogno Idrico Annuale (m³/ettaro)\"] = dataset[\"Varietà di Olive\"].apply(lambda x: fabbisogno_acqua[x][\"Annuale\"])\n",
|
|
" dataset[\"Temperatura Ottimale\"] = dataset[\"Varietà di Olive\"].apply(lambda x: fabbisogno_acqua[x][\"Temperatura Ottimale\"])\n",
|
|
" dataset[\"Resistenza alla Siccità\"] = dataset[\"Varietà di Olive\"].apply(lambda x: fabbisogno_acqua[x][\"Resistenza\"])\n",
|
|
"\n",
|
|
" return dataset\n",
|
|
"\n",
|
|
"\n",
|
|
"olive_varieties = add_olive_water_consumption_correlation(olive_varieties)\n",
|
|
"\n",
|
|
"olive_varieties.to_csv(\"./data/olive_varieties.csv\")"
|
|
],
|
|
"outputs": [],
|
|
"execution_count": 9
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-22T16:01:55.347598Z",
|
|
"start_time": "2024-10-22T16:01:54.221545Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"def preprocess_weather_data(weather_df):\n",
|
|
" # Calcola statistiche mensili per ogni anno\n",
|
|
" monthly_weather = weather_df.groupby(['year', 'month']).agg({\n",
|
|
" 'temp': ['mean', 'min', 'max'],\n",
|
|
" 'humidity': 'mean',\n",
|
|
" 'precip': 'sum',\n",
|
|
" 'windspeed': 'mean',\n",
|
|
" 'cloudcover': 'mean',\n",
|
|
" 'solarradiation': 'sum',\n",
|
|
" 'solarenergy': 'sum',\n",
|
|
" 'uvindex': 'max'\n",
|
|
" }).reset_index()\n",
|
|
"\n",
|
|
" monthly_weather.columns = ['year', 'month'] + [f'{col[0]}_{col[1]}' for col in monthly_weather.columns[2:]]\n",
|
|
" return monthly_weather\n",
|
|
"\n",
|
|
"\n",
|
|
"def get_growth_phase(month):\n",
|
|
" if month in [12, 1, 2]:\n",
|
|
" return 'dormancy'\n",
|
|
" elif month in [3, 4, 5]:\n",
|
|
" return 'flowering'\n",
|
|
" elif month in [6, 7, 8]:\n",
|
|
" return 'fruit_set'\n",
|
|
" else:\n",
|
|
" return 'ripening'\n",
|
|
"\n",
|
|
"\n",
|
|
"def calculate_weather_effect(row, optimal_temp):\n",
|
|
" # Effetti base\n",
|
|
" temp_effect = -0.1 * (row['temp_mean'] - optimal_temp) ** 2\n",
|
|
" rain_effect = -0.05 * (row['precip_sum'] - 600) ** 2 / 10000\n",
|
|
" sun_effect = 0.1 * row['solarenergy_sum'] / 1000\n",
|
|
"\n",
|
|
" # Fattori di scala basati sulla fase di crescita\n",
|
|
" if row['growth_phase'] == 'dormancy':\n",
|
|
" temp_scale = 0.5\n",
|
|
" rain_scale = 0.2\n",
|
|
" sun_scale = 0.1\n",
|
|
" elif row['growth_phase'] == 'flowering':\n",
|
|
" temp_scale = 2.0\n",
|
|
" rain_scale = 1.5\n",
|
|
" sun_scale = 1.0\n",
|
|
" elif row['growth_phase'] == 'fruit_set':\n",
|
|
" temp_scale = 1.5\n",
|
|
" rain_scale = 1.0\n",
|
|
" sun_scale = 0.8\n",
|
|
" else: # ripening\n",
|
|
" temp_scale = 1.0\n",
|
|
" rain_scale = 0.5\n",
|
|
" sun_scale = 1.2\n",
|
|
"\n",
|
|
" # Calcolo dell'effetto combinato\n",
|
|
" combined_effect = (\n",
|
|
" temp_scale * temp_effect +\n",
|
|
" rain_scale * rain_effect +\n",
|
|
" sun_scale * sun_effect\n",
|
|
" )\n",
|
|
"\n",
|
|
" # Aggiustamenti specifici per fase\n",
|
|
" if row['growth_phase'] == 'flowering':\n",
|
|
" combined_effect -= 0.5 * max(0, row['precip_sum'] - 50) # Penalità per pioggia eccessiva durante la fioritura\n",
|
|
" elif row['growth_phase'] == 'fruit_set':\n",
|
|
" combined_effect += 0.3 * max(0, row['temp_mean'] - (optimal_temp + 5)) # Bonus per temperature più alte durante la formazione dei frutti\n",
|
|
"\n",
|
|
" return combined_effect\n",
|
|
"\n",
|
|
"\n",
|
|
"def calculate_water_need(weather_data, base_need, optimal_temp):\n",
|
|
" # Calcola il fabbisogno idrico basato su temperatura e precipitazioni\n",
|
|
" temp_factor = 1 + 0.05 * (weather_data['temp_mean'] - optimal_temp) # Aumenta del 5% per ogni grado sopra l'ottimale\n",
|
|
" rain_factor = 1 - 0.001 * weather_data['precip_sum'] # Diminuisce leggermente con l'aumentare delle precipitazioni\n",
|
|
" return base_need * temp_factor * rain_factor\n",
|
|
"\n",
|
|
"\n",
|
|
"def clean_column_name(name):\n",
|
|
" # Rimuove caratteri speciali e spazi, converte in snake_case e abbrevia\n",
|
|
" name = re.sub(r'[^a-zA-Z0-9\\s]', '', name) # Rimuove caratteri speciali\n",
|
|
" name = name.lower().replace(' ', '_') # Converte in snake_case\n",
|
|
"\n",
|
|
" # Abbreviazioni comuni\n",
|
|
" abbreviations = {\n",
|
|
" 'production': 'prod',\n",
|
|
" 'percentage': 'pct',\n",
|
|
" 'hectare': 'ha',\n",
|
|
" 'tonnes': 't',\n",
|
|
" 'litres': 'l',\n",
|
|
" 'minimum': 'min',\n",
|
|
" 'maximum': 'max',\n",
|
|
" 'average': 'avg'\n",
|
|
" }\n",
|
|
"\n",
|
|
" for full, abbr in abbreviations.items():\n",
|
|
" name = name.replace(full, abbr)\n",
|
|
"\n",
|
|
" return name\n",
|
|
"\n",
|
|
"\n",
|
|
"def create_technique_mapping(olive_varieties, mapping_path='models/technique_mapping.joblib'):\n",
|
|
" # Estrai tutte le tecniche uniche dal dataset e convertile in lowercase\n",
|
|
" all_techniques = olive_varieties['Tecnica di Coltivazione'].str.lower().unique()\n",
|
|
"\n",
|
|
" # Crea il mapping partendo da 1\n",
|
|
" technique_mapping = {tech: i + 1 for i, tech in enumerate(sorted(all_techniques))}\n",
|
|
"\n",
|
|
" # Salva il mapping\n",
|
|
" os.makedirs(os.path.dirname(mapping_path), exist_ok=True)\n",
|
|
" joblib.dump(technique_mapping, mapping_path)\n",
|
|
"\n",
|
|
" return technique_mapping\n",
|
|
"\n",
|
|
"\n",
|
|
"def encode_techniques(df, mapping_path='models/technique_mapping.joblib'):\n",
|
|
" if not os.path.exists(mapping_path):\n",
|
|
" raise FileNotFoundError(f\"Mapping not found at {mapping_path}. Run create_technique_mapping first.\")\n",
|
|
"\n",
|
|
" technique_mapping = joblib.load(mapping_path)\n",
|
|
"\n",
|
|
" # Trova tutte le colonne delle tecniche\n",
|
|
" tech_columns = [col for col in df.columns if col.endswith('_tech')]\n",
|
|
"\n",
|
|
" # Applica il mapping a tutte le colonne delle tecniche\n",
|
|
" for col in tech_columns:\n",
|
|
" df[col] = df[col].str.lower().map(technique_mapping).fillna(0).astype(int)\n",
|
|
"\n",
|
|
" return df\n",
|
|
"\n",
|
|
"\n",
|
|
"def decode_techniques(df, mapping_path='models/technique_mapping.joblib'):\n",
|
|
" if not os.path.exists(mapping_path):\n",
|
|
" raise FileNotFoundError(f\"Mapping not found at {mapping_path}\")\n",
|
|
"\n",
|
|
" technique_mapping = joblib.load(mapping_path)\n",
|
|
" reverse_mapping = {v: k for k, v in technique_mapping.items()}\n",
|
|
" reverse_mapping[0] = '' # Aggiungi un mapping per 0 a stringa vuota\n",
|
|
"\n",
|
|
" # Trova tutte le colonne delle tecniche\n",
|
|
" tech_columns = [col for col in df.columns if col.endswith('_tech')]\n",
|
|
"\n",
|
|
" # Applica il reverse mapping a tutte le colonne delle tecniche\n",
|
|
" for col in tech_columns:\n",
|
|
" df[col] = df[col].map(reverse_mapping)\n",
|
|
"\n",
|
|
" return df\n",
|
|
"\n",
|
|
"\n",
|
|
"def decode_single_technique(technique_value, mapping_path='models/technique_mapping.joblib'):\n",
|
|
" if not os.path.exists(mapping_path):\n",
|
|
" raise FileNotFoundError(f\"Mapping not found at {mapping_path}\")\n",
|
|
"\n",
|
|
" technique_mapping = joblib.load(mapping_path)\n",
|
|
" reverse_mapping = {v: k for k, v in technique_mapping.items()}\n",
|
|
" reverse_mapping[0] = ''\n",
|
|
"\n",
|
|
" return reverse_mapping.get(technique_value, '')\n",
|
|
"\n",
|
|
"\n",
|
|
"def simulate_olive_production(weather_data, olive_varieties, num_simulations=5, random_seed=None):\n",
|
|
" if random_seed is not None:\n",
|
|
" np.random.seed(random_seed)\n",
|
|
"\n",
|
|
" create_technique_mapping(olive_varieties)\n",
|
|
"\n",
|
|
" monthly_weather = preprocess_weather_data(weather_data)\n",
|
|
" all_results = []\n",
|
|
"\n",
|
|
" all_varieties = olive_varieties['Varietà di Olive'].unique()\n",
|
|
" variety_techniques = {variety: olive_varieties[olive_varieties['Varietà di Olive'] == variety]['Tecnica di Coltivazione'].unique()\n",
|
|
" for variety in all_varieties}\n",
|
|
"\n",
|
|
" for sim in range(num_simulations):\n",
|
|
" selected_year = np.random.choice(monthly_weather['year'].unique())\n",
|
|
" year_weather = monthly_weather[monthly_weather['year'] == selected_year].copy()\n",
|
|
" year_weather.loc[:, 'growth_phase'] = year_weather['month'].apply(get_growth_phase)\n",
|
|
"\n",
|
|
" # Aggiungi variabilità casuale ai dati meteo\n",
|
|
" year_weather['temp_mean'] *= np.random.uniform(0.95, 1.05, len(year_weather))\n",
|
|
" year_weather['precip_sum'] *= np.random.uniform(0.9, 1.1, len(year_weather))\n",
|
|
" year_weather['solarenergy_sum'] *= np.random.uniform(0.95, 1.05, len(year_weather))\n",
|
|
"\n",
|
|
" num_varieties = np.random.randint(1, 4)\n",
|
|
" selected_varieties = np.random.choice(all_varieties, size=num_varieties, replace=False)\n",
|
|
"\n",
|
|
" hectares = np.random.uniform(1, 10)\n",
|
|
" percentages = np.random.dirichlet(np.ones(num_varieties))\n",
|
|
"\n",
|
|
" annual_production = 0\n",
|
|
" annual_min_oil = 0\n",
|
|
" annual_max_oil = 0\n",
|
|
" annual_avg_oil = 0\n",
|
|
" annual_water_need = 0\n",
|
|
"\n",
|
|
" variety_data = {clean_column_name(variety): {\n",
|
|
" 'tech': '',\n",
|
|
" 'pct': 0,\n",
|
|
" 'prod_t_ha': 0,\n",
|
|
" 'oil_prod_t_ha': 0,\n",
|
|
" 'oil_prod_l_ha': 0,\n",
|
|
" 'min_yield_pct': 0,\n",
|
|
" 'max_yield_pct': 0,\n",
|
|
" 'min_oil_prod_l_ha': 0,\n",
|
|
" 'max_oil_prod_l_ha': 0,\n",
|
|
" 'avg_oil_prod_l_ha': 0,\n",
|
|
" 'l_per_t': 0,\n",
|
|
" 'min_l_per_t': 0,\n",
|
|
" 'max_l_per_t': 0,\n",
|
|
" 'avg_l_per_t': 0,\n",
|
|
" 'olive_prod': 0,\n",
|
|
" 'min_oil_prod': 0,\n",
|
|
" 'max_oil_prod': 0,\n",
|
|
" 'avg_oil_prod': 0,\n",
|
|
" 'water_need': 0\n",
|
|
" } for variety in all_varieties}\n",
|
|
"\n",
|
|
" for i, variety in enumerate(selected_varieties):\n",
|
|
" technique = np.random.choice(variety_techniques[variety])\n",
|
|
" percentage = percentages[i]\n",
|
|
"\n",
|
|
" variety_info = olive_varieties[(olive_varieties['Varietà di Olive'] == variety) &\n",
|
|
" (olive_varieties['Tecnica di Coltivazione'] == technique)].iloc[0]\n",
|
|
"\n",
|
|
" # Aggiungi variabilità alla produzione di base\n",
|
|
" base_production = variety_info['Produzione (tonnellate/ettaro)'] * 1000 * percentage * hectares / 12\n",
|
|
" base_production *= np.random.uniform(0.9, 1.1) # ±10% di variazione\n",
|
|
"\n",
|
|
" weather_effect = year_weather.apply(lambda row: calculate_weather_effect(row, variety_info['Temperatura Ottimale']), axis=1)\n",
|
|
" monthly_production = base_production * (1 + weather_effect / 10000)\n",
|
|
" monthly_production *= np.random.uniform(0.95, 1.05, len(year_weather))\n",
|
|
"\n",
|
|
" annual_variety_production = monthly_production.sum()\n",
|
|
"\n",
|
|
" # Aggiungi variabilità alle rese di olio\n",
|
|
" min_yield_factor = np.random.uniform(0.95, 1.05)\n",
|
|
" max_yield_factor = np.random.uniform(0.95, 1.05)\n",
|
|
" avg_yield_factor = (min_yield_factor + max_yield_factor) / 2\n",
|
|
"\n",
|
|
" min_oil_production = annual_variety_production * variety_info['Min Litri per Tonnellata'] / 1000 * min_yield_factor\n",
|
|
" max_oil_production = annual_variety_production * variety_info['Max Litri per Tonnellata'] / 1000 * max_yield_factor\n",
|
|
" avg_oil_production = annual_variety_production * variety_info['Media Litri per Tonnellata'] / 1000 * avg_yield_factor\n",
|
|
"\n",
|
|
" # Calcolo del fabbisogno idrico\n",
|
|
" base_water_need = (variety_info['Fabbisogno Acqua Primavera (m³/ettaro)'] +\n",
|
|
" variety_info['Fabbisogno Acqua Estate (m³/ettaro)'] +\n",
|
|
" variety_info['Fabbisogno Acqua Autunno (m³/ettaro)'] +\n",
|
|
" variety_info['Fabbisogno Acqua Inverno (m³/ettaro)']) / 4 # Media stagionale\n",
|
|
"\n",
|
|
" monthly_water_need = year_weather.apply(lambda row: calculate_water_need(row, base_water_need, variety_info['Temperatura Ottimale']), axis=1)\n",
|
|
" monthly_water_need *= np.random.uniform(0.95, 1.05, len(monthly_water_need))\n",
|
|
" annual_variety_water_need = monthly_water_need.sum() * percentage * hectares\n",
|
|
"\n",
|
|
" annual_production += annual_variety_production\n",
|
|
" annual_min_oil += min_oil_production\n",
|
|
" annual_max_oil += max_oil_production\n",
|
|
" annual_avg_oil += avg_oil_production\n",
|
|
" annual_water_need += annual_variety_water_need\n",
|
|
"\n",
|
|
" clean_variety = clean_column_name(variety)\n",
|
|
" variety_data[clean_variety].update({\n",
|
|
" 'tech': clean_column_name(technique),\n",
|
|
" 'pct': percentage,\n",
|
|
" 'prod_t_ha': variety_info['Produzione (tonnellate/ettaro)'] * np.random.uniform(0.95, 1.05),\n",
|
|
" 'oil_prod_t_ha': variety_info['Produzione Olio (tonnellate/ettaro)'] * np.random.uniform(0.95, 1.05),\n",
|
|
" 'oil_prod_l_ha': variety_info['Produzione Olio (litri/ettaro)'] * np.random.uniform(0.95, 1.05),\n",
|
|
" 'min_yield_pct': variety_info['Min % Resa'] * min_yield_factor,\n",
|
|
" 'max_yield_pct': variety_info['Max % Resa'] * max_yield_factor,\n",
|
|
" 'min_oil_prod_l_ha': variety_info['Min Produzione Olio (litri/ettaro)'] * min_yield_factor,\n",
|
|
" 'max_oil_prod_l_ha': variety_info['Max Produzione Olio (litri/ettaro)'] * max_yield_factor,\n",
|
|
" 'avg_oil_prod_l_ha': variety_info['Media Produzione Olio (litri/ettaro)'] * avg_yield_factor,\n",
|
|
" 'l_per_t': variety_info['Litri per Tonnellata'] * np.random.uniform(0.98, 1.02),\n",
|
|
" 'min_l_per_t': variety_info['Min Litri per Tonnellata'] * min_yield_factor,\n",
|
|
" 'max_l_per_t': variety_info['Max Litri per Tonnellata'] * max_yield_factor,\n",
|
|
" 'avg_l_per_t': variety_info['Media Litri per Tonnellata'] * avg_yield_factor,\n",
|
|
" 'olive_prod': annual_variety_production,\n",
|
|
" 'min_oil_prod': min_oil_production,\n",
|
|
" 'max_oil_prod': max_oil_production,\n",
|
|
" 'avg_oil_prod': avg_oil_production,\n",
|
|
" 'water_need': annual_variety_water_need\n",
|
|
" })\n",
|
|
"\n",
|
|
" flattened_variety_data = {f'{variety}_{key}': value\n",
|
|
" for variety, data in variety_data.items()\n",
|
|
" for key, value in data.items()}\n",
|
|
"\n",
|
|
" all_results.append({\n",
|
|
" 'sim': sim,\n",
|
|
" 'year': selected_year,\n",
|
|
" 'temp_mean': year_weather['temp_mean'].mean(),\n",
|
|
" 'precip_sum': year_weather['precip_sum'].sum(),\n",
|
|
" 'solar_energy_sum': year_weather['solarenergy_sum'].sum(),\n",
|
|
" 'ha': hectares,\n",
|
|
" 'zone': f\"zone_{sim + 1}\",\n",
|
|
" 'olive_prod': annual_production,\n",
|
|
" 'min_oil_prod': annual_min_oil,\n",
|
|
" 'max_oil_prod': annual_max_oil,\n",
|
|
" 'avg_oil_prod': annual_avg_oil,\n",
|
|
" 'total_water_need': annual_water_need,\n",
|
|
" **flattened_variety_data\n",
|
|
" })\n",
|
|
"\n",
|
|
" df_results = pd.DataFrame(all_results)\n",
|
|
"\n",
|
|
" return df_results\n",
|
|
"\n",
|
|
"\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",
|
|
"\n",
|
|
"\n",
|
|
"# Funzione per visualizzare il mapping delle tecniche\n",
|
|
"def print_technique_mapping(mapping_path='models/technique_mapping.joblib'):\n",
|
|
" if not os.path.exists(mapping_path):\n",
|
|
" print(\"Mapping file not found.\")\n",
|
|
" return\n",
|
|
"\n",
|
|
" mapping = joblib.load(mapping_path)\n",
|
|
" print(\"Technique Mapping:\")\n",
|
|
" for technique, code in mapping.items():\n",
|
|
" print(f\"{technique}: {code}\")\n",
|
|
"\n",
|
|
"\n",
|
|
"# Visualizza il mapping delle tecniche\n",
|
|
"print_technique_mapping()"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Technique Mapping:\n",
|
|
"intensiva: 1\n",
|
|
"superintensiva: 2\n",
|
|
"tradizionale: 3\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 10
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-22T16:03:29.783053Z",
|
|
"start_time": "2024-10-22T16:03:26.356907Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"simulated_data = pd.read_csv(\"./data/simulated_data.csv\")\n",
|
|
"\n",
|
|
"\n",
|
|
"def clean_column_names(df):\n",
|
|
" # Funzione per pulire i nomi delle colonne\n",
|
|
" new_columns = []\n",
|
|
" for col in df.columns:\n",
|
|
" # Usa regex per separare le varietà\n",
|
|
" varieties = re.findall(r'([a-z]+)_([a-z_]+)', col)\n",
|
|
" if varieties:\n",
|
|
" new_columns.append(f\"{varieties[0][0]}_{varieties[0][1]}\")\n",
|
|
" else:\n",
|
|
" new_columns.append(col)\n",
|
|
" return new_columns\n",
|
|
"\n",
|
|
"\n",
|
|
"def prepare_comparison_data(simulated_data, olive_varieties):\n",
|
|
" # Pulisci i nomi delle colonne\n",
|
|
" df = simulated_data.copy()\n",
|
|
"\n",
|
|
" df.columns = clean_column_names(df)\n",
|
|
" df = encode_techniques(df)\n",
|
|
"\n",
|
|
" all_varieties = olive_varieties['Varietà di Olive'].unique()\n",
|
|
" varieties = [clean_column_name(variety) for variety in all_varieties]\n",
|
|
" comparison_data = []\n",
|
|
"\n",
|
|
" for variety in varieties:\n",
|
|
" olive_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_olive_prod')), None)\n",
|
|
" oil_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_avg_oil_prod')), None)\n",
|
|
" tech_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_tech')), None)\n",
|
|
" water_need_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_water_need')), None)\n",
|
|
"\n",
|
|
" if olive_prod_col and oil_prod_col and tech_col and water_need_col:\n",
|
|
" variety_data = df[[olive_prod_col, oil_prod_col, tech_col, water_need_col]]\n",
|
|
" variety_data = variety_data[variety_data[tech_col] != 0] # Esclude le righe dove la tecnica è 0\n",
|
|
"\n",
|
|
" if not variety_data.empty:\n",
|
|
" avg_olive_prod = pd.to_numeric(variety_data[olive_prod_col], errors='coerce').mean()\n",
|
|
" avg_oil_prod = pd.to_numeric(variety_data[oil_prod_col], errors='coerce').mean()\n",
|
|
" avg_water_need = pd.to_numeric(variety_data[water_need_col], errors='coerce').mean()\n",
|
|
" efficiency = avg_oil_prod / avg_olive_prod if avg_olive_prod > 0 else 0\n",
|
|
" water_efficiency = avg_oil_prod / avg_water_need if avg_water_need > 0 else 0\n",
|
|
"\n",
|
|
" comparison_data.append({\n",
|
|
" 'Variety': variety,\n",
|
|
" 'Avg Olive Production (kg/ha)': avg_olive_prod,\n",
|
|
" 'Avg Oil Production (L/ha)': avg_oil_prod,\n",
|
|
" 'Avg Water Need (m³/ha)': avg_water_need,\n",
|
|
" 'Oil Efficiency (L/kg)': efficiency,\n",
|
|
" 'Water Efficiency (L oil/m³ water)': water_efficiency\n",
|
|
" })\n",
|
|
"\n",
|
|
" return pd.DataFrame(comparison_data)\n",
|
|
"\n",
|
|
"\n",
|
|
"def plot_variety_comparison(comparison_data, metric):\n",
|
|
" plt.figure(figsize=(12, 6))\n",
|
|
" bars = plt.bar(comparison_data['Variety'], comparison_data[metric])\n",
|
|
" plt.title(f'Comparison of {metric} across Olive Varieties')\n",
|
|
" plt.xlabel('Variety')\n",
|
|
" plt.ylabel(metric)\n",
|
|
" plt.xticks(rotation=45, ha='right')\n",
|
|
"\n",
|
|
" for bar in bars:\n",
|
|
" height = bar.get_height()\n",
|
|
" plt.text(bar.get_x() + bar.get_width() / 2., height,\n",
|
|
" f'{height:.2f}',\n",
|
|
" ha='center', va='bottom')\n",
|
|
"\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
"\n",
|
|
"def plot_efficiency_vs_production(comparison_data):\n",
|
|
" plt.figure(figsize=(10, 6))\n",
|
|
"\n",
|
|
" plt.scatter(comparison_data['Avg Olive Production (kg/ha)'],\n",
|
|
" comparison_data['Oil Efficiency (L/kg)'],\n",
|
|
" s=100)\n",
|
|
"\n",
|
|
" for i, row in comparison_data.iterrows():\n",
|
|
" plt.annotate(row['Variety'],\n",
|
|
" (row['Avg Olive Production (kg/ha)'], row['Oil Efficiency (L/kg)']),\n",
|
|
" xytext=(5, 5), textcoords='offset points')\n",
|
|
"\n",
|
|
" plt.title('Oil Efficiency vs Olive Production by Variety')\n",
|
|
" plt.xlabel('Average Olive Production (kg/ha)')\n",
|
|
" plt.ylabel('Oil Efficiency (L oil / kg olives)')\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
"\n",
|
|
"def plot_water_efficiency_vs_production(comparison_data):\n",
|
|
" plt.figure(figsize=(10, 6))\n",
|
|
"\n",
|
|
" plt.scatter(comparison_data['Avg Olive Production (kg/ha)'],\n",
|
|
" comparison_data['Water Efficiency (L oil/m³ water)'],\n",
|
|
" s=100)\n",
|
|
"\n",
|
|
" for i, row in comparison_data.iterrows():\n",
|
|
" plt.annotate(row['Variety'],\n",
|
|
" (row['Avg Olive Production (kg/ha)'], row['Water Efficiency (L oil/m³ water)']),\n",
|
|
" xytext=(5, 5), textcoords='offset points')\n",
|
|
"\n",
|
|
" plt.title('Water Efficiency vs Olive Production by Variety')\n",
|
|
" plt.xlabel('Average Olive Production (kg/ha)')\n",
|
|
" plt.ylabel('Water Efficiency (L oil / m³ water)')\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
"\n",
|
|
"def plot_water_need_vs_oil_production(comparison_data):\n",
|
|
" plt.figure(figsize=(10, 6))\n",
|
|
"\n",
|
|
" plt.scatter(comparison_data['Avg Water Need (m³/ha)'],\n",
|
|
" comparison_data['Avg Oil Production (L/ha)'],\n",
|
|
" s=100)\n",
|
|
"\n",
|
|
" for i, row in comparison_data.iterrows():\n",
|
|
" plt.annotate(row['Variety'],\n",
|
|
" (row['Avg Water Need (m³/ha)'], row['Avg Oil Production (L/ha)']),\n",
|
|
" xytext=(5, 5), textcoords='offset points')\n",
|
|
"\n",
|
|
" plt.title('Oil Production vs Water Need by Variety')\n",
|
|
" plt.xlabel('Average Water Need (m³/ha)')\n",
|
|
" plt.ylabel('Average Oil Production (L/ha)')\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
"\n",
|
|
"def analyze_by_technique(simulated_data, olive_varieties):\n",
|
|
" # Pulisci i nomi delle colonne\n",
|
|
" df = simulated_data.copy()\n",
|
|
"\n",
|
|
" df.columns = clean_column_names(df)\n",
|
|
" df = encode_techniques(df)\n",
|
|
" all_varieties = olive_varieties['Varietà di Olive'].unique()\n",
|
|
" varieties = [clean_column_name(variety) for variety in all_varieties]\n",
|
|
"\n",
|
|
" technique_data = []\n",
|
|
"\n",
|
|
" for variety in varieties:\n",
|
|
" olive_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_olive_prod')), None)\n",
|
|
" oil_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_avg_oil_prod')), None)\n",
|
|
" tech_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_tech')), None)\n",
|
|
" water_need_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_water_need')), None)\n",
|
|
"\n",
|
|
" if olive_prod_col and oil_prod_col and tech_col and water_need_col:\n",
|
|
" variety_data = df[[olive_prod_col, oil_prod_col, tech_col, water_need_col]]\n",
|
|
" variety_data = variety_data[variety_data[tech_col] != 0]\n",
|
|
"\n",
|
|
" if not variety_data.empty:\n",
|
|
" for tech in variety_data[tech_col].unique():\n",
|
|
" tech_data = variety_data[variety_data[tech_col] == tech]\n",
|
|
"\n",
|
|
" avg_olive_prod = pd.to_numeric(tech_data[olive_prod_col], errors='coerce').mean()\n",
|
|
" avg_oil_prod = pd.to_numeric(tech_data[oil_prod_col], errors='coerce').mean()\n",
|
|
" avg_water_need = pd.to_numeric(tech_data[water_need_col], errors='coerce').mean()\n",
|
|
"\n",
|
|
" efficiency = avg_oil_prod / avg_olive_prod if avg_olive_prod > 0 else 0\n",
|
|
" water_efficiency = avg_oil_prod / avg_water_need if avg_water_need > 0 else 0\n",
|
|
"\n",
|
|
" technique_data.append({\n",
|
|
" 'Variety': variety,\n",
|
|
" 'Technique': tech,\n",
|
|
" 'Technique String': decode_single_technique(tech),\n",
|
|
" 'Avg Olive Production (kg/ha)': avg_olive_prod,\n",
|
|
" 'Avg Oil Production (L/ha)': avg_oil_prod,\n",
|
|
" 'Avg Water Need (m³/ha)': avg_water_need,\n",
|
|
" 'Oil Efficiency (L/kg)': efficiency,\n",
|
|
" 'Water Efficiency (L oil/m³ water)': water_efficiency\n",
|
|
" })\n",
|
|
"\n",
|
|
" return pd.DataFrame(technique_data)\n",
|
|
"\n",
|
|
"\n",
|
|
"# Esecuzione dell'analisi\n",
|
|
"comparison_data = prepare_comparison_data(simulated_data, olive_varieties)\n",
|
|
"\n",
|
|
"# Genera i grafici\n",
|
|
"plot_variety_comparison(comparison_data, 'Avg Olive Production (kg/ha)')\n",
|
|
"plot_variety_comparison(comparison_data, 'Avg Oil Production (L/ha)')\n",
|
|
"plot_variety_comparison(comparison_data, 'Avg Water Need (m³/ha)')\n",
|
|
"plot_variety_comparison(comparison_data, 'Oil Efficiency (L/kg)')\n",
|
|
"plot_variety_comparison(comparison_data, 'Water Efficiency (L oil/m³ water)')\n",
|
|
"plot_efficiency_vs_production(comparison_data)\n",
|
|
"plot_water_efficiency_vs_production(comparison_data)\n",
|
|
"plot_water_need_vs_oil_production(comparison_data)\n",
|
|
"\n",
|
|
"# Analisi per tecnica\n",
|
|
"technique_data = analyze_by_technique(simulated_data, olive_varieties)\n",
|
|
"\n",
|
|
"print(technique_data)\n",
|
|
"\n",
|
|
"# Stampa un sommario statistico\n",
|
|
"print(\"Comparison by Variety:\")\n",
|
|
"print(comparison_data.set_index('Variety'))\n",
|
|
"print(\"\\nBest Varieties by Water Efficiency:\")\n",
|
|
"print(comparison_data.sort_values('Water Efficiency (L oil/m³ water)', ascending=False).head())"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 1200x600 with 1 Axes>"
|
|
],
|
|
"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"text/plain": [
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"<Figure size 1200x600 with 1 Axes>"
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],
|
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
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"<Figure size 1200x600 with 1 Axes>"
|
|
],
|
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"image/png": 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bG4bx9PUsWbKk8dVXXxmGYRgbN240unTpYn6+hvH0dShXrpwxatQo8zIPDw8jMDDQ/Pjjjz82qlSpYtEPQkNDjXLlyhn+/v6GYRjGiRMnDA8PD2P69OkvPH+GYRh16tQxP8fly5cbHh4extq1a595/J9//tkoVqyY+fkmmjFjhlGqVCnjwYMHRkJCglGzZk2jc+fOFut8++23hoeHh3HgwAHDMAyjb9++Ro0aNYzHjx+b19m+fXuS5/tnK1asMDw8PIzw8HCL5R4eHkarVq3MjxNfm7p16xqxsbHm5e+8847Rs2dPwzAM486dO0avXr2Md955x2jdurVx7Ngx83pt27Y1vvzyS4vzVKtWLePJkyfmZdOnTzc8PDzMr8WkSZOMzz77zKKuy5cvGx4eHsbWrVstlv/444+Gh4eHcfDgwWc+zx9++MF4//33jV9//dVieffu3Q1vb2/DMAwjPDzcKFmypOHn52exzqRJk4xOnToZhmEYQ4YMMTw8PIxffvnF3H7w4EHDw8PD3P8TzZkzx/Dw8DCuXbtm3Lt3L8k6ERERhp+fn3H16lXDMAyjfv36ST4TZ8+ebezfv/+Zz8kwDKNMmTJGv379nttuGP/3vt6zZ49hGEnfv3/sIydPnjQ8PDyMkydPmtuHDRtmeHl5mWt+6623jNGjR1scY+3atYaHh4fxv//977l1xMbGGlWrVjWGDh1qsdzHx8fcv5PzOhnG0/5Tu3Zti/d74vv0xIkThmEk772d+Lm7YcMG8zpDhgwx6tSpk6Ln+1ffAQCA5LOzdjAGAPhvsrF5etFtfHx8stb/7rvv9OTJEzVp0sRiefny5ZU3b16dPHnSYr6bsmXLmv/f3d1dkiyuWHF1dZUkiztz2draWtwFytHRUTVr1jQPnalevbqqV6+u2NhY3bhxQ7/88ouuXr2qBw8emPeXyMPD44XPp1KlSpo1a5auXLmiWrVqqWbNmhoyZIi5/dSpU6pTp44yZ85sXmZnZ6fGjRtrzpw5Fld5/PlKnFy5cunWrVvPPfapU6fk6empfPnyWSxv2rSphg0bpp9//llFihR5Yf2JNmzYoNdff10FCxY0n8uGDRtq1qxZ+umnn1S4cGFJT++SN2rUKN27d085cuTQ9u3b5eTkJC8vL0nSiRMnVKtWLYur5zw9PZU3b94XHt/Ozk4VKlTQuXPnJD0dxpcnTx7zcatXr64xY8YoMjJSly9fVmxsrKpXry7p6Rw2zZs31+PHj/Xbb7/p119/1ffff6/4+HjFxsY+95gnTpxQpUqV5OjoaB6akylTJpUvXz7JULg/Dv9Ljg8++EC7d++Wv7+/atSokWQenxMnTsgwDNWtW9diWFDdunU1b948nT17VgULFtSdO3fUvXt3i3UqVKigTJky6dixY6pdu7bOnj2r2rVrW8x75u3tLVtb2xfWGBQUpCxZsihLlixJ2jw9Pc3/b2dnJzc3N7355puys/u/fzK6urrq4cOHkqScOXNaXOmV6OHDh/ruu+/k7+9vsbx06dLmK6okmftwRESEMmXKZB72+fDhQ/3yyy/65ZdfzMNx//yaJvat590ps0SJElq5cqUSEhIUFBSkX375RdeuXdPPP/9sPq/fffedYmNjzf040Z+Hnzo6Oip//vzmx6dOnZKtrW2Su102bdpUM2fONH+eFSlSRKNGjdK3336rmjVrqnr16hbz7lWqVEmrV6/W3bt3VadOHdWqVUu9e/d+5vNJZBiGxevxLIl9wHjOUL4/qlChgvLmzavt27erYsWKevLkifbu3asOHTpIejpE99GjR8/ss5J07NgxFS1a9Jn7trOzU9OmTbV27VqNHTtWGTJk0M8//6yLFy8qMDBQUvJep0SFCxc2f/c8S0re28+T3Of7V98BAIDkI5gCALwUV1dXZcyYMcnwmj+Kjo7WkydP5Orqap5HKjFk+iN3d3fzD91Ez5rM1tHR8YU1Zc2a1eJHryRly5bNfOyEhARNnz5dK1asUHR0tHLnzq3SpUs/c56WZ9X5RzNmzNDnn3+unTt3ateuXbKxsVHVqlU1duxY5cuXT+Hh4c99roZhWMy79eehkDY2Ni/8QRkeHq7XXnvtuTX/Max7kQcPHujgwYOKjY1VhQoVkrSvWbNGw4cPl/R0WOb48eO1c+dOdejQQdu2bZO3t7e59gcPHihbtmxJ9pE9e/a/rKNq1arm+XCOHDliDp6kp8FUfHy8zpw5o8uXL+u1117T66+/LkmKiYnR+PHjtXnzZsXFxem1116Tp6en7OzsXnj+wsLCtGPHjiTzFklKcoe6Zz2nF0kc0te0aVONHDlSixYtSnJsSRYB6h/dvXtXbm5ukqRx48ZZzPGT6N69e5Ke9oM/15sYJr1IZGTkc4ffPut997x1X+To0aN67bXXkoSnzs7OFo8TQ4bEYWS//fabRo8erRMnTsjOzk6FChUyh4N/fk0T6/rje+nPvvjiC82fP1+hoaFyd3dXyZIl5eTkZP68SXw9/urOhNmyZZPJZDI/Dg8Pl5ubW5KAKLG/P3z4UCaTSUuWLNG8efO0d+9ebdy4Ufb29nr77bc1duxYubq6asSIEcqVK5e2bNlifq09PT01evTo507GnTdv3hcG15LMcyrlyZPnhetJT/tskyZNtHbtWo0aNUqHDx9WRESEeahc4jnq1q3bM7dP7I/P4+PjoyVLlmj//v1q2LChNm3apKxZs1rM6fRXr1Oiv/pcTsl7+0X7kP76+f7VdwAAIPkIpgAAL6169eo6efKkHj9+/Mxw5+uvv9bEiRO1cuVKubi4SHo6P1Li1TCJ7t+/nyr/kA8LC5NhGBY/IIODg83hwoIFC7R06VKNHTtW9evXN1/N9DK3K8+cObN8fX3l6+urn3/+Wfv27dPcuXM1btw4LVq0SC4uLgoODk6yXeJE4W5ubn/5g+55krPv5Ni8ebNiY2M1e/bsJFfPzJkzR5s2bdLHH38sR0dHZcqUSfXq1dPOnTtVvXp1XblyxeLqgFy5ciWZn0mSQkJCVLBgwRfWUaVKFU2aNEk//PCDLl68qE6dOpnbcufOrUKFCuncuXO6dOmSxd34Jk6cqN27dysgIEBVq1Y1hx5VqlR54fEyZ86sqlWrWhwn0V9diZIc+fPn18CBA+Xn56f169dbtCWe52XLliljxoxJts2TJ495kvnBgwerYsWKSdZJfC+5urom6QeGYVjcTOBZ3NzckvzgT22HDx9O8d34EhIS1K1bN9nb22vt2rV64403ZGdnp+vXrz/zzm6JAezz+vvWrVvl7++vTz75RD4+PuZgon///uY5zRJfjwcPHqhQoULmbX///Xf9+uuvKleu3DP37eLiotDQUMXFxVn0mcT3dGJNOXPm1NixYzVmzBhduXJFu3bt0sKFC+Xi4qJx48bJwcFBPXv2VM+ePXX79m0dOHBAc+fO1SeffKKdO3c+89h169bVkiVL9Pvvvz93nqndu3crd+7cyb7TXLNmzfT555/r5MmT2rZtm8qWLWv+TE48R1OnTlWBAgWSbPtXYVHhwoXl6emprVu3qkGDBtqyZYuaN29u/iNCcl6n5EqN93Zyn+9ffQcAAJKPyc8BAC/to48+UlhYmGbMmJGkLSQkRIsWLdLrr7+uMmXK6K233pKDg4O2bt1qsd6ZM2d0+/Zti6F7Lys2NlZHjhwxP46JidHhw4fNQcXZs2dVpEgR+fj4mEOpu3fv6n//+1+Su0a9yK1bt1SrVi3t2rVLklSoUCF17dpVVatW1Z07dyQ9HR5z4MABiwAgPj5e27dvV6lSpSyGX6VUhQoVdP78eYu7yEnSli1blD17dvMVRX/l66+/VpkyZeTl5aVKlSpZ/Pf+++8rPDzc4sdxs2bNdOHCBa1YscJ8V7Q/1nT48GGLu7X9+OOPzx1m9UfFihWTu7u7li9fLilpsFS9enX98MMPunDhgsXVVGfPnlWlSpX09ttvm0Opy5cv68GDBxav55+H/lSsWFHXr19XiRIlVKpUKZUqVUpvvvmmli5dqr179ybn1P2lDz/8UOXLl08ylC3xyrTQ0FDzsUuVKqWwsDAFBAQoLCxMhQoVUrZs2XTz5k2LdXLlyqVp06bphx9+MJ+nw4cP69GjR+b9Hzly5IXDGKWn4Vd0dPRfBlgvK/HKt1q1aqVou9DQUN24cUM+Pj4qXbq0OUhInPz+z+/RxLsfPu+qoLNnzypz5szq1q2bOeyIiorS2bNnzftKHFr4xztaSk+Dw/79+1uE3H9UsWJFxcfHJ7kyJzFAK1eunM6fP6+qVavq4sWLMplMKlGihAYOHCgPDw/duXNHMTExql+/vvkufHny5FG7du3UuHFj8+fIs7Rv316ZMmXSsGHDktwdUXp6t8yTJ0+qe/fuLxz29keFChVSqVKltH37dh08eNBiYvG33npL9vb2unv3rkV/tLe317Rp05L1Hm/ZsqWOHj2qY8eO6ffff7f4Y0ByXqfkSs57+6+Guibn+SbnOwAAkHxcMQUAeGllypRR//79FRAQoJ9++knvvvuu3NzcdO3aNS1ZskRRUVFasGCBTCaTXF1d1a1bN82ePVv29vaqV6+ebt68qZkzZ6pIkSJq0aJFqtQ0fPhwDRgwQNmyZdPixYsVHR2tnj17Snr6I3Tu3LlasGCBypQpo19//VXz58/XkydPLH7c/5W8efMqV65cmjBhgiIjI5U/f35dvnxZhw4dUvfu3SVJffr00eHDh/Xhhx+qW7ducnBw0FdffaWgoKC//df0Tp06acuWLerUqZP69OkjNzc3bdq0SSdOnJCfn1+yfoxevHhR//vf/zRixIhntterV08uLi5avXq13n33XUlPA6KsWbNq9erV6tixo8VxevTooR07dqhLly766KOPFBERoZkzZ8pkMj33x/0fVa5cWdu3b1fp0qWTXL1VvXp1rVq1SgkJCRZhWOnSpbVz506tWrVKhQsX1pUrVzRv3jyZTCaL1zNLliw6f/68Tp8+rfL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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
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"<Figure size 1200x600 with 1 Axes>"
|
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],
|
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
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"<Figure size 1200x600 with 1 Axes>"
|
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],
|
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"text/plain": [
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"<Figure size 1000x600 with 1 Axes>"
|
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],
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
|
"text/plain": [
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"<Figure size 1000x600 with 1 Axes>"
|
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],
|
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
|
"text/plain": [
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"<Figure size 1000x600 with 1 Axes>"
|
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],
|
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" Variety Technique Technique String \\\n",
|
|
"0 nocellara_delletna 1 intensiva \n",
|
|
"1 nocellara_delletna 2 superintensiva \n",
|
|
"2 nocellara_delletna 3 tradizionale \n",
|
|
"3 leccino 2 superintensiva \n",
|
|
"4 leccino 1 intensiva \n",
|
|
"5 leccino 3 tradizionale \n",
|
|
"6 frantoio 2 superintensiva \n",
|
|
"7 frantoio 1 intensiva \n",
|
|
"8 frantoio 3 tradizionale \n",
|
|
"9 coratina 3 tradizionale \n",
|
|
"10 coratina 2 superintensiva \n",
|
|
"11 coratina 1 intensiva \n",
|
|
"12 taggiasca 1 intensiva \n",
|
|
"13 taggiasca 2 superintensiva \n",
|
|
"14 taggiasca 3 tradizionale \n",
|
|
"15 pendolino 2 superintensiva \n",
|
|
"16 pendolino 1 intensiva \n",
|
|
"17 pendolino 3 tradizionale \n",
|
|
"18 moraiolo 1 intensiva \n",
|
|
"19 moraiolo 2 superintensiva \n",
|
|
"20 moraiolo 3 tradizionale \n",
|
|
"\n",
|
|
" Avg Olive Production (kg/ha) Avg Oil Production (L/ha) \\\n",
|
|
"0 13484.380922 2938.694834 \n",
|
|
"1 20366.964281 4485.935499 \n",
|
|
"2 7925.810184 1778.307276 \n",
|
|
"3 15997.067462 3121.798292 \n",
|
|
"4 9025.139571 1773.562931 \n",
|
|
"5 10108.932988 1997.845543 \n",
|
|
"6 24265.362958 5969.360729 \n",
|
|
"7 24967.318844 6123.110661 \n",
|
|
"8 10351.942823 2525.766714 \n",
|
|
"9 12170.657331 3084.579191 \n",
|
|
"10 19291.701394 4912.667547 \n",
|
|
"11 23042.159736 5897.569914 \n",
|
|
"12 8659.148508 1730.265375 \n",
|
|
"13 18886.125534 3872.390696 \n",
|
|
"14 5414.740132 1086.460307 \n",
|
|
"15 16312.500203 2931.345653 \n",
|
|
"16 14916.868189 2689.450670 \n",
|
|
"17 12279.427474 2203.132045 \n",
|
|
"18 10384.333348 2266.477318 \n",
|
|
"19 34375.199370 7479.433837 \n",
|
|
"20 10014.344313 2172.625658 \n",
|
|
"\n",
|
|
" Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n",
|
|
"0 33768.971526 0.217933 \n",
|
|
"1 36053.911751 0.220255 \n",
|
|
"2 26406.432054 0.224369 \n",
|
|
"3 20134.892898 0.195148 \n",
|
|
"4 13425.362086 0.196514 \n",
|
|
"5 22895.581275 0.197632 \n",
|
|
"6 28810.421723 0.246003 \n",
|
|
"7 34101.902697 0.245245 \n",
|
|
"8 22178.839690 0.243990 \n",
|
|
"9 38177.969803 0.253444 \n",
|
|
"10 37446.029717 0.254652 \n",
|
|
"11 53775.708012 0.255947 \n",
|
|
"12 20269.923048 0.199819 \n",
|
|
"13 28991.283481 0.205039 \n",
|
|
"14 20372.596517 0.200649 \n",
|
|
"15 21910.021485 0.179699 \n",
|
|
"16 29764.139976 0.180296 \n",
|
|
"17 27553.909208 0.179417 \n",
|
|
"18 23823.097838 0.218259 \n",
|
|
"19 59327.838908 0.217582 \n",
|
|
"20 37369.133353 0.216951 \n",
|
|
"\n",
|
|
" Water Efficiency (L oil/m³ water) \n",
|
|
"0 0.087024 \n",
|
|
"1 0.124423 \n",
|
|
"2 0.067344 \n",
|
|
"3 0.155044 \n",
|
|
"4 0.132105 \n",
|
|
"5 0.087259 \n",
|
|
"6 0.207194 \n",
|
|
"7 0.179553 \n",
|
|
"8 0.113882 \n",
|
|
"9 0.080795 \n",
|
|
"10 0.131193 \n",
|
|
"11 0.109670 \n",
|
|
"12 0.085361 \n",
|
|
"13 0.133571 \n",
|
|
"14 0.053329 \n",
|
|
"15 0.133790 \n",
|
|
"16 0.090359 \n",
|
|
"17 0.079957 \n",
|
|
"18 0.095138 \n",
|
|
"19 0.126070 \n",
|
|
"20 0.058140 \n",
|
|
"Comparison by Variety:\n",
|
|
" Avg Olive Production (kg/ha) Avg Oil Production (L/ha) \\\n",
|
|
"Variety \n",
|
|
"nocellara_delletna 16284.299055 3578.424294 \n",
|
|
"leccino 11690.857944 2292.608858 \n",
|
|
"frantoio 20596.615721 5056.515812 \n",
|
|
"coratina 18446.380732 4704.214155 \n",
|
|
"taggiasca 9980.432085 2022.516598 \n",
|
|
"pendolino 14761.441705 2654.171770 \n",
|
|
"moraiolo 16231.741808 3531.589211 \n",
|
|
"\n",
|
|
" Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n",
|
|
"Variety \n",
|
|
"nocellara_delletna 34061.917853 0.219747 \n",
|
|
"leccino 18024.908377 0.196103 \n",
|
|
"frantoio 28258.273811 0.245502 \n",
|
|
"coratina 44003.305214 0.255021 \n",
|
|
"taggiasca 22640.111000 0.202648 \n",
|
|
"pendolino 25766.594689 0.179804 \n",
|
|
"moraiolo 38202.360033 0.217573 \n",
|
|
"\n",
|
|
" Water Efficiency (L oil/m³ water) \n",
|
|
"Variety \n",
|
|
"nocellara_delletna 0.105056 \n",
|
|
"leccino 0.127191 \n",
|
|
"frantoio 0.178939 \n",
|
|
"coratina 0.106906 \n",
|
|
"taggiasca 0.089333 \n",
|
|
"pendolino 0.103008 \n",
|
|
"moraiolo 0.092444 \n",
|
|
"\n",
|
|
"Best Varieties by Water Efficiency:\n",
|
|
" Variety Avg Olive Production (kg/ha) \\\n",
|
|
"2 frantoio 20596.615721 \n",
|
|
"1 leccino 11690.857944 \n",
|
|
"3 coratina 18446.380732 \n",
|
|
"0 nocellara_delletna 16284.299055 \n",
|
|
"5 pendolino 14761.441705 \n",
|
|
"\n",
|
|
" Avg Oil Production (L/ha) Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n",
|
|
"2 5056.515812 28258.273811 0.245502 \n",
|
|
"1 2292.608858 18024.908377 0.196103 \n",
|
|
"3 4704.214155 44003.305214 0.255021 \n",
|
|
"0 3578.424294 34061.917853 0.219747 \n",
|
|
"5 2654.171770 25766.594689 0.179804 \n",
|
|
"\n",
|
|
" Water Efficiency (L oil/m³ water) \n",
|
|
"2 0.178939 \n",
|
|
"1 0.127191 \n",
|
|
"3 0.106906 \n",
|
|
"0 0.105056 \n",
|
|
"5 0.103008 \n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 11
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 4. Analisi della Relazione tra Meteo e Produzione"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-21T21:54:32.351863Z",
|
|
"start_time": "2024-10-21T21:54:30.887641Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"def get_full_data(simulated_data, olive_varieties):\n",
|
|
" # Assumiamo che simulated_data contenga già tutti i dati necessari\n",
|
|
" # Includiamo solo le colonne rilevanti\n",
|
|
" relevant_columns = ['year', 'temp_mean', 'precip_sum', 'solar_energy_sum', 'ha', 'zone', 'olive_prod']\n",
|
|
"\n",
|
|
" # Aggiungiamo le colonne specifiche per varietà\n",
|
|
" all_varieties = olive_varieties['Varietà di Olive'].unique()\n",
|
|
" varieties = [clean_column_name(variety) for variety in all_varieties]\n",
|
|
" for variety in varieties:\n",
|
|
" relevant_columns.extend([f'{variety}_olive_prod', f'{variety}_tech'])\n",
|
|
"\n",
|
|
" return simulated_data[relevant_columns].copy()\n",
|
|
"\n",
|
|
"\n",
|
|
"def analyze_correlations(full_data, variety):\n",
|
|
" # Filtra i dati per la varietà specifica\n",
|
|
" variety_data = full_data[[col for col in full_data.columns if not col.startswith('_') or col.startswith(f'{variety}_')]]\n",
|
|
"\n",
|
|
" # Rinomina le colonne per chiarezza\n",
|
|
" variety_data = variety_data.rename(columns={\n",
|
|
" f'{variety}_olive_prod': 'olive_production',\n",
|
|
" f'{variety}_tech': 'technique'\n",
|
|
" })\n",
|
|
"\n",
|
|
" # Matrice di correlazione\n",
|
|
" plt.figure(figsize=(12, 10))\n",
|
|
" corr_matrix = variety_data[['temp_mean', 'precip_sum', 'solar_energy_sum', 'olive_production']].corr()\n",
|
|
" sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')\n",
|
|
" plt.title(f'Matrice di Correlazione - {variety}')\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
" # Scatter plots\n",
|
|
" fig, axes = plt.subplots(2, 2, figsize=(20, 20))\n",
|
|
" fig.suptitle(f'Relazione tra Fattori Meteorologici e Produzione di Olive - {variety}', fontsize=16)\n",
|
|
"\n",
|
|
" for ax, var in zip(axes.flat, ['temp_mean', 'precip_sum', 'solar_energy_sum', 'ha']):\n",
|
|
" sns.scatterplot(data=variety_data, x=var, y='olive_production', hue='technique', ax=ax)\n",
|
|
" ax.set_title(f'{var.capitalize()} vs Produzione Olive')\n",
|
|
" ax.set_xlabel(var.capitalize())\n",
|
|
" ax.set_ylabel('Produzione Olive (kg/ettaro)')\n",
|
|
"\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
"\n",
|
|
"# Uso delle funzioni\n",
|
|
"full_data = get_full_data(simulated_data, olive_varieties)\n",
|
|
"\n",
|
|
"# Assumiamo che 'selected_variety' sia definito altrove nel codice\n",
|
|
"# Per esempio:\n",
|
|
"selected_variety = 'nocellara_delletna'\n",
|
|
"\n",
|
|
"analyze_correlations(full_data, selected_variety)"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 1200x1000 with 2 Axes>"
|
|
],
|
|
"image/png": 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|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"text/plain": [
|
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"<Figure size 2000x2000 with 4 Axes>"
|
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],
|
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"image/png": 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|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"execution_count": 225
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 5. Preparazione del Modello di Machine Learning"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-10-21T14:44:14.334863Z",
|
|
"start_time": "2024-10-19T08:47:38.767017Z"
|
|
}
|
|
},
|
|
"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",
|
|
"\n",
|
|
"model = RandomForestRegressor(n_estimators=100, random_state=random_state_value)\n",
|
|
"model.fit(X_train, y_train)\n",
|
|
"\n",
|
|
"# Valutazione del modello\n",
|
|
"y_pred = model.predict(X_test)\n",
|
|
"mse = mean_squared_error(y_test, y_pred)\n",
|
|
"r2 = r2_score(y_test, y_pred)\n",
|
|
"\n",
|
|
"print(f'Mean Squared Error: {mse}')\n",
|
|
"print(f'R2 Score: {r2}')\n",
|
|
"\n",
|
|
"# Importanza delle feature\n",
|
|
"feature_importance = pd.DataFrame({'feature': X.columns, 'importance': model.feature_importances_})\n",
|
|
"feature_importance = feature_importance.sort_values('importance', ascending=False)\n",
|
|
"sns.barplot(x='importance', y='feature', data=feature_importance)\n",
|
|
"plt.title('Importanza delle Feature')\n",
|
|
"plt.show()"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" year temp humidity cloudcover solarenergy uvindex \\\n",
|
|
"0 2012 16.106330 72.993202 37.448833 6329.0 1.997837 \n",
|
|
"1 2013 15.699121 76.081377 41.521258 6117.0 1.931271 \n",
|
|
"2 2014 16.372383 75.403644 41.882475 6109.8 1.930243 \n",
|
|
"3 2015 16.423907 70.602369 35.372725 6174.2 1.951707 \n",
|
|
"4 2016 16.420312 69.506617 40.081317 5645.5 1.781075 \n",
|
|
"\n",
|
|
" weather_effect olive_production min_oil_production max_oil_production \\\n",
|
|
"0 1164.287197 3907.500519 767.849447 938.482657 \n",
|
|
"1 -424.417623 3351.453832 658.582631 804.934326 \n",
|
|
"2 -93.418647 3467.303474 681.347844 832.758476 \n",
|
|
"3 1312.180766 3959.263268 778.021166 950.914759 \n",
|
|
"4 2366.542610 4328.289913 850.537319 1039.545612 \n",
|
|
"\n",
|
|
" avg_oil_production min_efficiency max_efficiency avg_efficiency \\\n",
|
|
"0 853.166052 0.196507 0.240175 0.218341 \n",
|
|
"1 731.758479 0.196507 0.240175 0.218341 \n",
|
|
"2 757.053160 0.196507 0.240175 0.218341 \n",
|
|
"3 864.467962 0.196507 0.240175 0.218341 \n",
|
|
"4 945.041466 0.196507 0.240175 0.218341 \n",
|
|
"\n",
|
|
" Technique \n",
|
|
"0 Tradizionale \n",
|
|
"1 Tradizionale \n",
|
|
"2 Tradizionale \n",
|
|
"3 Tradizionale \n",
|
|
"4 Tradizionale \n",
|
|
"Mean Squared Error: 2790084.5693922774\n",
|
|
"R2 Score: -1.8942184267499331\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
],
|
|
"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"execution_count": 79
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 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": {},
|
|
"source": [
|
|
"## 7. Previsione della Produzione di Olive per il Prossimo Anno"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"source": [
|
|
"next_year_production = model.predict(next_year_weather.mean().to_frame().T)[0]\n",
|
|
"print(f'Previsione produzione di olive per il prossimo anno: {next_year_production:.2f} kg/ettaro')"
|
|
],
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 8. Conclusioni e Prossimi Passi\n",
|
|
"\n",
|
|
"In questo notebook, abbiamo:\n",
|
|
"1. Caricato e analizzato i dati meteorologici\n",
|
|
"2. Simulato la produzione annuale di olive basata sui dati meteo\n",
|
|
"3. Esplorato le relazioni tra variabili meteorologiche e produzione di olive\n",
|
|
"4. Creato e valutato un modello di machine learning per prevedere la produzione\n",
|
|
"5. Utilizzato ARIMA per fare previsioni meteo\n",
|
|
"6. Previsto la produzione di olive per il prossimo anno\n",
|
|
"\n",
|
|
"Prossimi passi:\n",
|
|
"- Raccogliere dati reali sulla produzione di olive per sostituire i dati simulati\n",
|
|
"- Esplorare modelli più avanzati, come le reti neurali o i modelli di ensemble\n",
|
|
"- Incorporare altri fattori che potrebbero influenzare la produzione, come le pratiche agricole o l'età degli alberi\n",
|
|
"- Sviluppare una dashboard interattiva basata su questo modello"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.8.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|