Zadanie Biblioteka_DL
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0f5b3979-7704-4ea7-ac84-424f73e9a62d",
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"metadata": {},
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"source": [
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"# Predykcja na modelu zapisanym w pliku"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ef017730-4bbd-4fad-926a-2800fa636ccd",
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"metadata": {},
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"source": [
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"## Import bibliotek"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "a330d65d-8876-49b8-9507-27ee9f50d1e6",
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"metadata": {},
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"outputs": [],
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"source": [
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"import keras\n",
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"import pandas as pd\n",
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"from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
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"import numpy as np\n",
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"from sklearn.model_selection import train_test_split"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4bd52369-8c81-45a1-b091-5edae9bc2673",
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"metadata": {},
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"source": [
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"## Pobranie modelu z pliku"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "aa7d4e1f-2924-4c84-a06b-f19b451e923b",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = keras.models.load_model('./model.keras')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1c94dca7-7670-4ed9-b6ca-5c59c734cc44",
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"metadata": {},
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"source": [
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"## Przygotowanie danych do testowania modelu"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "3e72f7ea-91a6-45ac-9f38-872175ad2a8d",
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"metadata": {},
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"outputs": [],
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"source": [
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"dataset = pd.read_csv('./lettuce-growth-days/lettuce_dataset_updated.csv', encoding='ISO-8859-1')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "9f52a6e7-ae62-4ea6-bde5-e2b221fa2d7a",
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"metadata": {},
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"outputs": [],
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"source": [
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"ph_level = dataset['pH Level'].values.tolist()\n",
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"temp_F = dataset['Temperature (F)'].values.tolist()\n",
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"humid = dataset['Humidity'].values.tolist()\n",
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"days = dataset['Growth Days'].values.tolist()\n",
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"plant_id = dataset['Plant_ID'].values.tolist()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "a720aa83-bb5a-4c6f-89a5-f6e1fb7cf0e2",
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"metadata": {},
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"outputs": [],
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"source": [
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"X = []\n",
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"Y = []\n",
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"\n",
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"id = plant_id[0]\n",
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"temp_sum = 0\n",
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"humid_sum = 0\n",
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"ph_level_sum = 0\n",
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"day = 1\n",
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"\n",
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"for i in range(0, len(plant_id)):\n",
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" if plant_id[i] == id:\n",
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" temp_sum += temp_F[i]\n",
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" humid_sum += humid[i]\n",
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" ph_level_sum += ph_level[i]\n",
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" day = days[i]\n",
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" else:\n",
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" temp = []\n",
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" temp.append(temp_sum/day)\n",
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" temp.append(humid_sum/day)\n",
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" temp.append(ph_level_sum/day)\n",
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" X.append(temp)\n",
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" Y.append(day)\n",
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" temp_sum = 0\n",
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" humid_sum = 0\n",
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" ph_level_sum = 0\n",
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" day = 1\n",
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" id = plant_id[i]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "d8ef6898-38bb-4191-b7b9-bc47dd1ae5d8",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\obses\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:808: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"scaler = MinMaxScaler()\n",
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"X = scaler.fit_transform(X)\n",
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"X = np.array(X)\n",
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"Y = np.array(Y)\n",
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"\n",
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"encoder = OneHotEncoder(sparse=False)\n",
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"y_onehot = encoder.fit_transform(Y.reshape(-1,1))\n",
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"\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y_onehot, test_size=0.4, random_state=42)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "72650088-d370-4814-b381-6e5a5e01fe58",
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"metadata": {},
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"source": [
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"## Testowanie modelu"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "ed8d7cb0-eefd-4e64-80e1-4ea4ff6bb369",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 216ms/step - accuracy: 0.8571 - loss: 0.7300\n",
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"Dokładność testowa: 85.71%\n"
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]
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}
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],
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"source": [
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"test_loss, test_accuracy = model.evaluate(X_test, y_test)\n",
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"print(f\"Dokładność testowa: {test_accuracy:.2%}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5f0bceec-f95e-4192-aced-c9997eafee28",
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"metadata": {},
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"source": [
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"## Predykcja na zbiorze testowym"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "6ce776f4-859b-42d9-a756-bd29b11d1b56",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n"
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]
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}
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],
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"source": [
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"predictions = model.predict(X_test)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9908472c-08ae-4e46-afe3-27eefb43f671",
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"metadata": {},
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"source": [
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"## Zapisanie wyników predykcji do pliku tekstowego"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "738043a9-a312-4f96-af22-593362bc9624",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(\"predictions.txt\", \"w\") as txt_file:\n",
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" for line in predictions:\n",
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" txt_file.write(\" \".join(str(line)) + \"\\n\") "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "da604cf2-d13d-4dd3-a596-7b24ba5d92ef",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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