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@@ -0,0 +1,1663 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TQqrOdkY6nsy"
+ },
+ "source": [
+ "# **Klasyfikacja za pomocą naiwnej metody bayesowskiej z rozkładem normalnym**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SSaJsYOhz8h8"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AlcfRFCPSXIj"
+ },
+ "source": [
+ "# **Twierdzenie Bayesa**\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rcpTnWjOh5dq"
+ },
+ "source": [
+ "P(A) -- oznacza prawdopodobieństwo a-priori wystąpienia klasy A (tj. prawdopodobieństwo, że dowolny przykład należy do klasy A)\n",
+ "\n",
+ "P(B|A) -- oznacza prawdopodobieństwo a-posteriori, że B należy do \n",
+ "klasy A\n",
+ "\n",
+ "P(B) -- znacza prawdopodobieństwo a-priori wystąpienia przykładu B "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " | 26 | \n",
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+ " | 34 | \n",
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+ " | 39 | \n",
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+ "
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+ " \n",
+ " | 40 | \n",
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+ " 3.5 | \n",
+ " 1.3 | \n",
+ " 0.3 | \n",
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+ "
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+ " \n",
+ " | 41 | \n",
+ " 4.5 | \n",
+ " 2.3 | \n",
+ " 1.3 | \n",
+ " 0.3 | \n",
+ " Setosa | \n",
+ "
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+ " \n",
+ " | 42 | \n",
+ " 4.4 | \n",
+ " 3.2 | \n",
+ " 1.3 | \n",
+ " 0.2 | \n",
+ " Setosa | \n",
+ "
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+ " \n",
+ " | 43 | \n",
+ " 5.0 | \n",
+ " 3.5 | \n",
+ " 1.6 | \n",
+ " 0.6 | \n",
+ " Setosa | \n",
+ "
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+ " \n",
+ " | 44 | \n",
+ " 5.1 | \n",
+ " 3.8 | \n",
+ " 1.9 | \n",
+ " 0.4 | \n",
+ " Setosa | \n",
+ "
\n",
+ " \n",
+ " | 45 | \n",
+ " 4.8 | \n",
+ " 3.0 | \n",
+ " 1.4 | \n",
+ " 0.3 | \n",
+ " Setosa | \n",
+ "
\n",
+ " \n",
+ " | 46 | \n",
+ " 5.1 | \n",
+ " 3.8 | \n",
+ " 1.6 | \n",
+ " 0.2 | \n",
+ " Setosa | \n",
+ "
\n",
+ " \n",
+ " | 47 | \n",
+ " 4.6 | \n",
+ " 3.2 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " Setosa | \n",
+ "
\n",
+ " \n",
+ " | 48 | \n",
+ " 5.3 | \n",
+ " 3.7 | \n",
+ " 1.5 | \n",
+ " 0.2 | \n",
+ " Setosa | \n",
+ "
\n",
+ " \n",
+ " | 49 | \n",
+ " 5.0 | \n",
+ " 3.3 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " Setosa | \n",
+ "
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+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "fig = plt.figure()\n",
+ "ax = fig.add_subplot(111)\n",
+ "setosa = iris_data[:50]\n",
+ "versicolor = iris_data[50:100]\n",
+ "virginica = iris_data[100:150]\n",
+ "# ax.plot(np.arange(50),setosa['petal.length'], color='blue', lw=2)\n",
+ "# ax.plot(np.arange(50),versicolor['petal.length'], color='orange', lw=2)\n",
+ "# ax.plot(np.arange(50),virginica['petal.length'], color='green', lw=2)\n",
+ "ax.scatter(setosa['sepal.length'],np.arange(50), color='blue', lw=2)\n",
+ "ax.scatter(versicolor['sepal.length'],np.arange(50), color='orange', lw=2)\n",
+ "ax.scatter(virginica['sepal.length'],np.arange(50), color='green', lw=2) \n",
+ "setosa"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def gaussian_density(self, class_idx, x): \n",
+ " '''\n",
+ " Wyliczenie prawdopodobieństwa z rozkładu normalnego \n",
+ " (1/√2pi*σ) * exp((-1/2)*((x-μ)^2)/(2*σ²))\n",
+ " μ -średnia\n",
+ " σ² - wariancja\n",
+ " σ - odchylenie standardowe\n",
+ " '''\n",
+ " mean = self.mean[class_idx]\n",
+ " var = self.var[class_idx]\n",
+ "\n",
+ " numerator = np.exp((-1/2)*((x-mean)**2) / (2 * var)) # Licznik wzoru na gęstość rozkładu normalnego \n",
+ " denominator = np.sqrt(2 * np.pi * var) # Mianownik wzoru na gęstość rozkładu normalnego \n",
+ " prob = numerator / denominator\n",
+ "\n",
+ " return prob\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Yabcm4Rei2ue"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dsf6FnlgjiOL"
+ },
+ "source": [
+ "# Funkcja gęstości prawdopodobieństwa rozkładu normalnego \n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "v0oeHebytjNp"
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import scipy.stats as stats\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "sns.set(style=\"whitegrid\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "id": "fOYTA3VVtjNw"
+ },
+ "outputs": [],
+ "source": [
+ "class NaiveBayesClassifier():\n",
+ " def calc_prior(self, features, target):\n",
+ " '''\n",
+ " Wyliczenie prawdopodobieństwa a priori\n",
+ " '''\n",
+ " self.prior = (features.groupby(target).apply(lambda x: len(x)) / self.rows).to_numpy()\n",
+ "\n",
+ " return self.prior\n",
+ " \n",
+ " def calc_statistics(self, features, target):\n",
+ " '''\n",
+ " Wyliczenie średnich i wariancji dla danych\n",
+ " ''' \n",
+ " self.mean = features.groupby(target).apply(np.mean).to_numpy()\n",
+ " self.var = features.groupby(target).apply(np.var).to_numpy()\n",
+ " \n",
+ " return self.mean, self.var\n",
+ " \n",
+ " def gaussian_density(self, class_idx, x): \n",
+ " '''\n",
+ " Wyliczenie prawdopodobieństwa z rozkładu normalnego \n",
+ " (1/√2pi*σ) * exp((-1/2)*((x-μ)^2)/(2*σ²))\n",
+ " μ -średnia\n",
+ " σ² - wariancja\n",
+ " σ - odchylenie standardowe\n",
+ " '''\n",
+ " mean = self.mean[class_idx]\n",
+ " var = self.var[class_idx]\n",
+ " \n",
+ " numerator = np.exp((-1/2)*((x-mean)**2) / (2 * var)) # Licznik wzoru na gęstość rozkładu normalnego \n",
+ " denominator = np.sqrt(2 * np.pi * var) # Mianownik wzoru na gęstość rozkładu normalnego \n",
+ " prob = numerator / denominator\n",
+ " \n",
+ " return prob\n",
+ " \n",
+ " def classify(self, x):\n",
+ " '''\n",
+ " Wyliczenie prawdopodobieństwa a posteriori i zwrócenie klasy, dla której prawdopodobieństwo jest najwyższe\n",
+ " '''\n",
+ " posteriors = []\n",
+ " posteriors_no_log = []\n",
+ "\n",
+ " # calculate posterior probability for each class\n",
+ " for i in range(self.count):\n",
+ " prior = np.log(self.prior[i]) # Do predykcji używane jest prawodopodobieństwo logarytmiczne\n",
+ " prior_no_log = self.prior[i] # Zwykłe prawdopodobieństwo liczymy, żeby zwrócić je z predykcjami\n",
+ "\n",
+ " conditional = np.sum(np.log(self.gaussian_density(i, x))) \n",
+ " conditional_no_log = np.prod(self.gaussian_density(i, x))\n",
+ "\n",
+ " posterior = prior + conditional\n",
+ " posterior_no_log = prior_no_log * conditional_no_log\n",
+ "\n",
+ " posteriors.append(posterior)\n",
+ " posteriors_no_log.append(posterior_no_log)\n",
+ "\n",
+ " # Zwracamy klasę o największym prawdopodobieństwie\n",
+ " return self.classes[np.argmax(posteriors)], np.max(posteriors_no_log)\n",
+ "\n",
+ " def fit(self, features, target):\n",
+ " '''\n",
+ " Główna metoda trenująca model\n",
+ " '''\n",
+ " self.classes = np.unique(target)\n",
+ " self.count = len(self.classes)\n",
+ " self.feature_nums = features.shape[1]\n",
+ " self.rows = features.shape[0]\n",
+ " \n",
+ " self.calc_statistics(features, target)\n",
+ " self.calc_prior(features, target)\n",
+ " \n",
+ " def predict(self, features):\n",
+ " '''\n",
+ " Predykcja wartości dla każdego wiersza\n",
+ " '''\n",
+ " preds = [self.classify(f) for f in features.to_numpy()]\n",
+ " return preds\n",
+ "\n",
+ " def accuracy(self, y_test, y_pred):\n",
+ " '''\n",
+ " Wyliczenie accuracy modelu\n",
+ " '''\n",
+ " accuracy = np.sum(y_test == y_pred) / len(y_test)\n",
+ " return accuracy\n",
+ "\n",
+ " def visualize(self, y_true, y_pred, target):\n",
+ " '''\n",
+ " Narysowanie wykresu porównującego rozkład klas prawdziwych i przewidzianych\n",
+ " '''\n",
+ " tr = pd.DataFrame(data=y_true, columns=[target])\n",
+ " pr = pd.DataFrame(data=y_pred, columns=[target])\n",
+ " \n",
+ " \n",
+ " fig, ax = plt.subplots(1, 2, sharex='col', sharey='row', figsize=(15,6))\n",
+ " \n",
+ " sns.countplot(x=target, data=tr, ax=ax[0], alpha=0.7, hue=target, dodge=False)\n",
+ " sns.countplot(x=target, data=pr, ax=ax[1], alpha=0.7, hue=target, dodge=False)\n",
+ " \n",
+ " ax[0].tick_params(labelsize=12)\n",
+ " ax[1].tick_params(labelsize=12)\n",
+ " ax[0].set_title(\"Prawdziwe wartości\", fontsize=18)\n",
+ " ax[1].set_title(\"Predykcje\", fontsize=18)\n",
+ " plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Pitność wody"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 382
+ },
+ "id": "5-riUAGntjN2",
+ "outputId": "f87f047d-bc71-41ef-a43a-17b6f7cf84c3"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2948, 9) (2948,)\n",
+ "(328, 9) (328,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Preprocessing danych\n",
+ "\n",
+ "# Uzupełnienie pustych wartości w kolumnach\n",
+ "def fill_nan(df):\n",
+ " for index, column in enumerate(df.columns[:9]):\n",
+ " df[column] = df[column].fillna(df.groupby('Potability')[column].transform('mean'))\n",
+ " return df\n",
+ "\n",
+ "# Wczytywanie danych\n",
+ "df = pd.read_csv(\"water_potability.csv\")\n",
+ "\n",
+ "df = fill_nan(df)\n",
+ "\n",
+ "# Zrandomizowanie kolejności danych w datasecie\n",
+ "df = df.sample(frac=1, random_state=10).reset_index(drop=True)\n",
+ "\n",
+ "# Podział na atrybuty i przewidywane wartości\n",
+ "X, y = df.iloc[:, :-1], df.iloc[:, -1]\n",
+ "\n",
+ "# Normalizacja i skalowanie danych\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "sc = StandardScaler()\n",
+ "X = sc.fit_transform(X.to_numpy())\n",
+ "X = pd.DataFrame(X, columns=df.columns.values.tolist()[:-1])\n",
+ "\n",
+ "# Podział na dane trenujące i testowe, z uwzględnieniem równego rozłożenia danych\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, stratify=y, random_state=1)\n",
+ "\n",
+ "print(X_train.shape, y_train.shape)\n",
+ "print(X_test.shape, y_test.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "id": "O82SGzK6tjN5"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " ph | \n",
+ " Hardness | \n",
+ " Solids | \n",
+ " Chloramines | \n",
+ " Sulfate | \n",
+ " Conductivity | \n",
+ " Organic_carbon | \n",
+ " Trihalomethanes | \n",
+ " Turbidity | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 1022 | \n",
+ " 0.003078 | \n",
+ " 0.688791 | \n",
+ " 0.846257 | \n",
+ " 1.428934 | \n",
+ " -0.858263 | \n",
+ " 0.002792 | \n",
+ " 0.913790 | \n",
+ " 0.232417 | \n",
+ " 2.319505 | \n",
+ "
\n",
+ " \n",
+ " | 3191 | \n",
+ " -0.587365 | \n",
+ " 0.223203 | \n",
+ " -0.731867 | \n",
+ " 0.397503 | \n",
+ " 0.759893 | \n",
+ " 0.330607 | \n",
+ " 0.094379 | \n",
+ " 0.282563 | \n",
+ " 0.235024 | \n",
+ "
\n",
+ " \n",
+ " | 13 | \n",
+ " 0.003078 | \n",
+ " -0.241037 | \n",
+ " 0.773051 | \n",
+ " 0.580019 | \n",
+ " 1.334369 | \n",
+ " -0.049130 | \n",
+ " -1.121422 | \n",
+ " -0.200432 | \n",
+ " -0.946356 | \n",
+ "
\n",
+ " \n",
+ " | 2068 | \n",
+ " -2.176058 | \n",
+ " 1.443006 | \n",
+ " -1.626771 | \n",
+ " -4.164610 | \n",
+ " -0.033706 | \n",
+ " -1.050763 | \n",
+ " -0.391328 | \n",
+ " -0.398649 | \n",
+ " -0.298341 | \n",
+ "
\n",
+ " \n",
+ " | 1484 | \n",
+ " 0.213047 | \n",
+ " 0.403036 | \n",
+ " -0.464729 | \n",
+ " 0.070417 | \n",
+ " 0.021560 | \n",
+ " -0.952776 | \n",
+ " -0.213330 | \n",
+ " 0.111419 | \n",
+ " -0.235893 | \n",
+ "
\n",
+ " \n",
+ " | ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " | 691 | \n",
+ " 0.003078 | \n",
+ " 1.199106 | \n",
+ " -0.003483 | \n",
+ " -0.670308 | \n",
+ " -0.069513 | \n",
+ " 0.185754 | \n",
+ " -0.466010 | \n",
+ " 0.031975 | \n",
+ " 0.676276 | \n",
+ "
\n",
+ " \n",
+ " | 1283 | \n",
+ " -2.034004 | \n",
+ " -1.508135 | \n",
+ " 0.255310 | \n",
+ " 0.083839 | \n",
+ " -1.413707 | \n",
+ " 0.694074 | \n",
+ " -1.110579 | \n",
+ " 0.232996 | \n",
+ " 2.544703 | \n",
+ "
\n",
+ " \n",
+ " | 2818 | \n",
+ " -0.702987 | \n",
+ " -0.575677 | \n",
+ " 0.755056 | \n",
+ " 0.664695 | \n",
+ " 0.021560 | \n",
+ " -0.489334 | \n",
+ " 0.371852 | \n",
+ " -2.272990 | \n",
+ " -1.764684 | \n",
+ "
\n",
+ " \n",
+ " | 1330 | \n",
+ " 1.525943 | \n",
+ " 0.497074 | \n",
+ " -0.714355 | \n",
+ " -1.024237 | \n",
+ " -1.022037 | \n",
+ " -0.327074 | \n",
+ " -1.107341 | \n",
+ " 0.517432 | \n",
+ " -1.230528 | \n",
+ "
\n",
+ " \n",
+ " | 1926 | \n",
+ " -0.043558 | \n",
+ " -0.882359 | \n",
+ " -0.456141 | \n",
+ " -0.770271 | \n",
+ " 0.795189 | \n",
+ " 0.560306 | \n",
+ " -1.086081 | \n",
+ " -1.356820 | \n",
+ " 0.172521 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
2948 rows × 9 columns
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "x.visualize(y_test, predictions, 'Potability')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "id": "aw8Tefprhjnn"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ph_val = X_test[\"ph\"]\n",
+ "sulfate_val = X_test[\"Sulfate\"]\n",
+ "hard_val = X_test[\"Hardness\"]\n",
+ "carb_val = X_test[\"Organic_carbon\"]\n",
+ "turb_val = X_test[\"Turbidity\"]\n",
+ "ch_val = X_test[\"Chloramines\"]\n",
+ "\n",
+ "\n",
+ "figure, axes = plt.subplots(nrows=3, ncols=2)\n",
+ "\n",
+ "axes[0, 0].plot(ph_val, predictions, 'bo')\n",
+ "axes[0, 0].set_title(\"pH\")\n",
+ "\n",
+ "axes[0, 1].plot(sulfate_val, predictions, 'bo')\n",
+ "axes[0, 1].set_title(\"Sulfate\")\n",
+ "\n",
+ "axes[1, 0].plot(hard_val, predictions, 'bo')\n",
+ "axes[1, 0].set_title(\"Hardness\")\n",
+ "\n",
+ "axes[1, 1].plot(carb_val, predictions, 'bo')\n",
+ "axes[1, 1].set_title(\"Organic carbon\")\n",
+ "\n",
+ "axes[2, 0].plot(turb_val, predictions, 'bo')\n",
+ "axes[2, 0].set_title(\"Turbidity\")\n",
+ "\n",
+ "axes[2, 1].plot(ch_val, predictions, 'bo')\n",
+ "axes[2, 1].set_title(\"Chloramines\")\n",
+ "\n",
+ "figure.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from sklearn.decomposition import PCA\n",
+ "\n",
+ "pca = PCA(n_components=2)\n",
+ "pca.fit(X_test)\n",
+ "X_pca = pca.transform(X_test)\n",
+ "\n",
+ "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=predictions, s=50, cmap='viridis')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y_test, s=50, cmap='viridis')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Irysy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(105, 4) (105,)\n",
+ "(45, 4) (45,)\n",
+ "9.333333333333333e-01\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/andrzej/anaconda3/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3438: FutureWarning: In a future version, DataFrame.mean(axis=None) will return a scalar mean over the entire DataFrame. To retain the old behavior, use 'frame.mean(axis=0)' or just 'frame.mean()'\n",
+ " return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Preprocessing danych\n",
+ "\n",
+ "\n",
+ "# Wczytywanie danych\n",
+ "df = pd.read_csv(\"iris.csv\")\n",
+ "\n",
+ "# Zrandomizowanie kolejności danych w datasecie\n",
+ "df = df.sample(frac=1, random_state=10).reset_index(drop=True)\n",
+ "\n",
+ "# Podział na atrybuty i przewidywane wartości\n",
+ "X, y = df.iloc[:, :-1], df.iloc[:, -1]\n",
+ "\n",
+ "# Normalizacja i skalowanie danych\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "sc = StandardScaler()\n",
+ "X = sc.fit_transform(X.to_numpy())\n",
+ "X = pd.DataFrame(X, columns=df.columns.values.tolist()[:-1])\n",
+ "\n",
+ "# Podział na dane trenujące i testowe, z uwzględnieniem równego rozłożenia danych\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y, random_state=1)\n",
+ "\n",
+ "print(X_train.shape, y_train.shape)\n",
+ "print(X_test.shape, y_test.shape)\n",
+ "\n",
+ "\n",
+ "# Trenowanie modelu klasyfikatora\n",
+ "x = NaiveBayesClassifier()\n",
+ "x.fit(X_train, y_train)\n",
+ "\n",
+ "\n",
+ "# Predykcja wartości dla danych testowych\n",
+ "predictions = x.predict(X_test)\n",
+ "\n",
+ "# Prawdopodobieństwa kolejnych predykcji\n",
+ "probabilities = [p[1] for p in predictions]\n",
+ "\n",
+ "# Przewidziana wartość\n",
+ "predictions = [p[0] for p in predictions]\n",
+ "\n",
+ "\n",
+ "# Wyliczenie accuracy modelu\n",
+ "print(x.accuracy(y_test, predictions))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.9326599326599326"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "f1_score(y_test, predictions, average=\"macro\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " sepal.length sepal.width petal.length petal.width\n",
+ "106 -0.900681 0.558611 -1.169714 -0.920548\n",
+ "65 -1.143017 1.249201 -1.340227 -1.447076\n",
+ "49 1.038005 0.558611 1.103783 1.712096\n",
+ "74 2.128516 -0.131979 1.615320 1.185567\n",
+ "110 -0.173674 1.709595 -1.169714 -1.183812\n",
+ ".. ... ... ... ...\n",
+ "123 -1.021849 -0.131979 -1.226552 -1.315444\n",
+ "121 -0.052506 -1.052767 0.137547 0.000878\n",
+ "85 -0.537178 1.939791 -1.169714 -1.052180\n",
+ "13 -1.143017 -0.131979 -1.340227 -1.315444\n",
+ "125 -1.870024 -0.131979 -1.510739 -1.447076\n",
+ "\n",
+ "[105 rows x 4 columns]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pca = PCA(n_components=2)\n",
+ "pca.fit(X_test)\n",
+ "X_pca = pca.transform(X_test)\n",
+ "\n",
+ "df_pred = pd.DataFrame(predictions).replace({'Virginica': 0, 'Versicolor': 1, \"Setosa\": 2}, regex=True)\n",
+ "df_pred = np.array(df_pred).reshape(1, -1)\n",
+ "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=df_pred[0], s=50, cmap='viridis')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sep_len = X_test[\"sepal.length\"]\n",
+ "sep_with = X_test[\"sepal.width\"]\n",
+ "pet_len = X_test[\"petal.length\"]\n",
+ "pet_with = X_test[\"petal.width\"]\n",
+ "\n",
+ "figure, axes = plt.subplots(nrows=2, ncols=2)\n",
+ "\n",
+ "axes[0, 0].plot(sep_len, predictions, 'bo')\n",
+ "axes[0, 0].set_title(\"Sepal lenght\")\n",
+ "\n",
+ "axes[0, 1].plot(sep_with, predictions, 'bo')\n",
+ "axes[0, 1].set_title(\"Sepal width\")\n",
+ "\n",
+ "axes[1, 0].plot(pet_len, predictions, 'bo')\n",
+ "axes[1, 0].set_title(\"Petal length\")\n",
+ "\n",
+ "axes[1, 1].plot(pet_with, predictions, 'bo')\n",
+ "axes[1, 1].set_title(\"Petal width\")\n",
+ "\n",
+ "figure.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "collapsed_sections": [],
+ "name": "naive_bayes.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.8.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/naive_bayes.ipynb b/naive_bayes.ipynb
index b10cfed..77165b0 100644
--- a/naive_bayes.ipynb
+++ b/naive_bayes.ipynb
@@ -9,15 +9,6 @@
"# **Klasyfikacja za pomocą naiwnej metody bayesowskiej z rozkładem normalnym**"
]
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "SSaJsYOhz8h8"
- },
- "source": [
- ""
- ]
- },
{
"cell_type": "markdown",
"metadata": {
@@ -42,6 +33,222 @@
"P(B) -- znacza prawdopodobieństwo a-priori wystąpienia przykładu B "
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Naiwny klasyfikator bayesowski jest _naiwny, ponieważ zakłada, że poszczególne cechy są niezależne od siebie**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SSaJsYOhz8h8"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 104,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " sepal.length | \n",
+ " sepal.width | \n",
+ " petal.length | \n",
+ " petal.width | \n",
+ " variety | \n",
+ "
\n",
+ " \n",
+ " \n",
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+ " 5.9 | \n",
+ " 3.0 | \n",
+ " 5.1 | \n",
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+ " Virginica | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
150 rows × 5 columns
\n",
+ "