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naive-bayes-gaussian/naive_bayes.ipynb

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Added jupyter notebook and dataset file
2022-05-17 17:30:50 +02:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "TQqrOdkY6nsy"
},
"source": [
"# **Klasyfikacja za pomocą naiwnej metody bayesowskiej z rozkładem normalnym**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SSaJsYOhz8h8"
},
"source": [
"![rozklady.jpg](data:image/jpeg;base64,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
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AlcfRFCPSXIj"
},
"source": [
"# **Twierdzenie Bayesa**\n",
"![bayes.svg](data:image/svg+xml;base64,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
]
},
{
"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": "markdown",
"metadata": {
"id": "Yabcm4Rei2ue"
},
"source": [
"![GaussianNB.png](data:image/png;base64,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
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dsf6FnlgjiOL"
},
"source": [
"# Funkcja gęstości prawdopodobieństwa rozkładu normalnego \n",
"![gestosc.svg](data:image/svg+xml;base64,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
]
},
{
"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",
" numerator = np.exp((-1/2)*((x-mean)**2) / (2 * var))\n",
" denominator = np.sqrt(2 * np.pi * var)\n",
" prob = numerator / denominator\n",
" return prob\n",
" \n",
" def calc_posterior(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.calc_posterior(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], palette='viridis', alpha=0.7, hue=target, dodge=False)\n",
" sns.countplot(x=target, data=pr, ax=ax[1], palette='viridis', alpha=0.7, hue=target, dodge=False)\n",
" \n",
"\n",
" fig.suptitle('True vs Predicted Comparison', fontsize=20)\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(\"Predyckje\", fontsize=18)\n",
" plt.show()\n"
]
},
{
"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": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ph</th>\n",
" <th>Hardness</th>\n",
" <th>Solids</th>\n",
" <th>Chloramines</th>\n",
" <th>Sulfate</th>\n",
" <th>Conductivity</th>\n",
" <th>Organic_carbon</th>\n",
" <th>Trihalomethanes</th>\n",
" <th>Turbidity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1022</th>\n",
" <td>0.003078</td>\n",
" <td>0.688791</td>\n",
" <td>0.846257</td>\n",
" <td>1.428934</td>\n",
" <td>-0.858263</td>\n",
" <td>0.002792</td>\n",
" <td>0.913790</td>\n",
" <td>0.232417</td>\n",
" <td>2.319505</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3191</th>\n",
" <td>-0.587365</td>\n",
" <td>0.223203</td>\n",
" <td>-0.731867</td>\n",
" <td>0.397503</td>\n",
" <td>0.759893</td>\n",
" <td>0.330607</td>\n",
" <td>0.094379</td>\n",
" <td>0.282563</td>\n",
" <td>0.235024</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0.003078</td>\n",
" <td>-0.241037</td>\n",
" <td>0.773051</td>\n",
" <td>0.580019</td>\n",
" <td>1.334369</td>\n",
" <td>-0.049130</td>\n",
" <td>-1.121422</td>\n",
" <td>-0.200432</td>\n",
" <td>-0.946356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2068</th>\n",
" <td>-2.176058</td>\n",
" <td>1.443006</td>\n",
" <td>-1.626771</td>\n",
" <td>-4.164610</td>\n",
" <td>-0.033706</td>\n",
" <td>-1.050763</td>\n",
" <td>-0.391328</td>\n",
" <td>-0.398649</td>\n",
" <td>-0.298341</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1484</th>\n",
" <td>0.213047</td>\n",
" <td>0.403036</td>\n",
" <td>-0.464729</td>\n",
" <td>0.070417</td>\n",
" <td>0.021560</td>\n",
" <td>-0.952776</td>\n",
" <td>-0.213330</td>\n",
" <td>0.111419</td>\n",
" <td>-0.235893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>691</th>\n",
" <td>0.003078</td>\n",
" <td>1.199106</td>\n",
" <td>-0.003483</td>\n",
" <td>-0.670308</td>\n",
" <td>-0.069513</td>\n",
" <td>0.185754</td>\n",
" <td>-0.466010</td>\n",
" <td>0.031975</td>\n",
" <td>0.676276</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1283</th>\n",
" <td>-2.034004</td>\n",
" <td>-1.508135</td>\n",
" <td>0.255310</td>\n",
" <td>0.083839</td>\n",
" <td>-1.413707</td>\n",
" <td>0.694074</td>\n",
" <td>-1.110579</td>\n",
" <td>0.232996</td>\n",
" <td>2.544703</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2818</th>\n",
" <td>-0.702987</td>\n",
" <td>-0.575677</td>\n",
" <td>0.755056</td>\n",
" <td>0.664695</td>\n",
" <td>0.021560</td>\n",
" <td>-0.489334</td>\n",
" <td>0.371852</td>\n",
" <td>-2.272990</td>\n",
" <td>-1.764684</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1330</th>\n",
" <td>1.525943</td>\n",
" <td>0.497074</td>\n",
" <td>-0.714355</td>\n",
" <td>-1.024237</td>\n",
" <td>-1.022037</td>\n",
" <td>-0.327074</td>\n",
" <td>-1.107341</td>\n",
" <td>0.517432</td>\n",
" <td>-1.230528</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1926</th>\n",
" <td>-0.043558</td>\n",
" <td>-0.882359</td>\n",
" <td>-0.456141</td>\n",
" <td>-0.770271</td>\n",
" <td>0.795189</td>\n",
" <td>0.560306</td>\n",
" <td>-1.086081</td>\n",
" <td>-1.356820</td>\n",
" <td>0.172521</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2948 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" ph Hardness Solids Chloramines Sulfate Conductivity \\\n",
"1022 0.003078 0.688791 0.846257 1.428934 -0.858263 0.002792 \n",
"3191 -0.587365 0.223203 -0.731867 0.397503 0.759893 0.330607 \n",
"13 0.003078 -0.241037 0.773051 0.580019 1.334369 -0.049130 \n",
"2068 -2.176058 1.443006 -1.626771 -4.164610 -0.033706 -1.050763 \n",
"1484 0.213047 0.403036 -0.464729 0.070417 0.021560 -0.952776 \n",
"... ... ... ... ... ... ... \n",
"691 0.003078 1.199106 -0.003483 -0.670308 -0.069513 0.185754 \n",
"1283 -2.034004 -1.508135 0.255310 0.083839 -1.413707 0.694074 \n",
"2818 -0.702987 -0.575677 0.755056 0.664695 0.021560 -0.489334 \n",
"1330 1.525943 0.497074 -0.714355 -1.024237 -1.022037 -0.327074 \n",
"1926 -0.043558 -0.882359 -0.456141 -0.770271 0.795189 0.560306 \n",
"\n",
" Organic_carbon Trihalomethanes Turbidity \n",
"1022 0.913790 0.232417 2.319505 \n",
"3191 0.094379 0.282563 0.235024 \n",
"13 -1.121422 -0.200432 -0.946356 \n",
"2068 -0.391328 -0.398649 -0.298341 \n",
"1484 -0.213330 0.111419 -0.235893 \n",
"... ... ... ... \n",
"691 -0.466010 0.031975 0.676276 \n",
"1283 -1.110579 0.232996 2.544703 \n",
"2818 0.371852 -2.272990 -1.764684 \n",
"1330 -1.107341 0.517432 -1.230528 \n",
"1926 -1.086081 -1.356820 0.172521 \n",
"\n",
"[2948 rows x 9 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "a3jkTMFLtjN6"
},
"outputs": [],
"source": [
"# Trenowanie modelu klasyfikatora\n",
"x = NaiveBayesClassifier()\n",
"x.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "CoC22aNgtjN9"
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 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",
"predictions[0]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "JR06zodmtjN9"
},
"outputs": [
{
"data": {
"text/plain": [
"0.6280487804878049"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Wyliczenie accuracy modelu\n",
"x.accuracy(y_test, predictions)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "1jW0QPootjN_"
},
"outputs": [
{
"data": {
"text/plain": [
"0.14084507042253522"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import f1_score\n",
"\n",
"f1_score(y_test, predictions)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "vEVogTmAtjOA"
},
"outputs": [
{
"data": {
"text/plain": [
"0 0.609756\n",
"1 0.390244\n",
"Name: Potability, dtype: float64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_test.value_counts(normalize=True)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "jCVOdBZytjOB"
},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1080x432 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x.visualize(y_test, predictions, 'Potability')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "aw8Tefprhjnn"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/matplotlib/cbook/__init__.py:1377: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
" x[:, None]\n",
"/usr/lib/python3/dist-packages/matplotlib/axes/_base.py:237: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
" x = x[:, np.newaxis]\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAELCAYAAAAry2Y+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzt3XtcFPX+P/DX7rLLVYUQFNPykhiKpqJHMz3eUxTFjMRU8FJ6vKX57WYPTa3ItHrksbxlP29odkozvJKWmpdKT5KZpB4M8QYIKKjIIiy78/uDdmNhL7MXdpfp9Xw8fMjufOYz75nPe947O7s7IxMEQQAREdVpcncHQEREjmMxJyKSABZzIiIJYDEnIpIAFnMiIglgMScikgAWcyJyqoSEBGzbts3weNmyZejWrRueeOIJN0YlfSzmHuTkyZP45z//WeP56jsHkSucOnUKo0ePRlRUFP7xj39g9OjR+O2332zqIzc3Fxs2bMC+ffvwww8/WG0/d+5cLFu2zN6Q/9a83B0AEXmee/fuYerUqVi0aBGio6Oh0Whw6tQpqFQqm/rJzs5GYGAggoODaylS0uORuRv069cPn3zyCYYMGYKuXbvi9ddfR1lZmbvDIjLIysoCAMTExEChUMDHxwc9e/bEo48+io8//hgvv/yyoe3169fRpk0bVFRUGPXx448/YtKkScjPz0enTp0wd+5cAMCsWbPwxBNPICoqCmPHjsXFixcBAF988QV2796NdevWoVOnTpg6dSoAIC8vDy+88AK6d++Ofv36ITk52RWboM5hMXcTfdJ+++23yMrKwqpVq9wdEpFBixYtoFAo8Nprr+HIkSO4c+eOzX306NEDn376KUJDQ3H69GksWbIEAPDPf/4T+/fvx08//YS2bdsaXhji4+MxbNgwPPfcczh9+jTWrFkDnU6HadOmoU2bNjh69Cg2bdqETZs24dixY05dXylgMXeTsWPHIiwsDIGBgZg2bRr27t0LAMjPz0eXLl2M/qWlpbk5Wvq7CQgIwNatWyGTyfDGG2/g8ccfx9SpU3Hz5k2H+46Li0NAQABUKhVeeOEFXLhwAcXFxSbbnj17FoWFhZg5cyZUKhWaNWuGUaNGYd++fQ7HITU8Z+4mYWFhhr+bNGmC/Px8AEBoaCiOHj1q1DYhIcGlsREBQKtWrQxH05mZmXjllVewePFitGjRwu4+tVotli1bhm+++QaFhYWQyyuPJ4uKilCvXr0a7bOzsw0HOFX7qPqYKrGYu0lubq7h75ycHISGhroxGiLLWrVqhZEjR+KLL75A27Ztcf/+fcM0W47Wd+/ejYMHD2LDhg1o2rQpiouL0bVrV+gv3iqTyYzah4WFoWnTpjhw4IBzVkTCeJrFTbZu3YobN27g9u3bhg9DiTxFZmYm1q9fjxs3bgCoPPjYs2cPHnvsMURERODnn39GTk4OiouL8cknn4jut6SkBCqVCkFBQSgtLcWHH35oND04OBjXr183PO7QoQMCAgKwdu1a3L9/H1qtFhkZGTZ/RfLvgMXcTWJiYjBp0iQMGDAAzZo1w7Rp09wdEpFBQEAAzpw5g2eeeQYdO3bEqFGjEB4ejrlz5+KJJ57AkCFDMHz4cIwcORJ9+/YV3e+IESPQpEkT9OrVC0OHDkXHjh2NpsfFxeGPP/5Aly5dMH36dCgUCqxevRoXLlxA//790b17d8yfPx/37t1z9irXeTLenML1+vXrh6SkJPTo0cPdoRCRRPDInIhIAljMiYgkgKdZiIgkgEfmREQSYPV75kuXLsX+/fuRnZ2N3bt3Izw8XHTnOp0OJSUlUCqVNb4/SuQoQRCg0Wjg7+9v+PGJLezNbeY11SZ789pqMe/fvz8SExMxduxYm4MqKSlBRkaGzfMR2SI8PNzkrwetsTe3mdfkCrbmtdVi7sjPZpVKpSEoWy+dWVV6ejoiIyPtnt+UE+m5SN57HuUV2hrTVF4K9OjQGD/+dsNouv75Y7/mQKur9lGDDJDLZNBVf/5PEQ8H4aVx4rflifRcrN/9u8n+5HIZJg1rh+6RYSbnq75eKi8FEodGmGxvjrlt7qz+naG8vBwZGRmGPLOVvbltKq/NbRdzeeTI9rKUu2K5a8zMqY19vDpn5a6lsTZZG6pQyGXo1bGJxZywN69r9ef8+regKpUK3t7eDvXl6PzVbUrNQMGdcjNTtdj1wzUThdTc89b9dO6mTeuwKTUDhcUVFqf3jmpu8vma66U1294SU/E6s39ncfWpDlN5bW67mMsjR7aX5dwVy71jZoqz9/HqnJW7to11TWJzwta8dsm1WdLT0x3uw9lXDiwoKrU43dyg2FPI9WxZB2vxFRSVmuzP3Hzm2ltS2/3XdVXz2tx2MZcvjmwva7lhSz+eNGa1HYuzctfWsRbbztHxcEkxj4yMdOhVNy0tDVFRUU6MCAhJvWVxp5DLTZ8yMfe8GLasg7X4QoJ8TfZnbj5z7c0xt82d1b8zlJWVOeVAwV5V89rcdjGXL45sL2u5IbofN4yZObWxj1fnrNy1dazFttPHYW9e/22/mpgYHQFvpcLkNG+lAoO7PVRjuv55L0XNtz8yWeX5MHMee8S222YlRkeY7U8hlyExOsLsfKbiNtfeVrXdf11lbruYyyNHtpel3BXr7zhmzspdS2NtqjZU5aWQ1UpOACKOzJOSknDgwAHcvHkTEydORGBgoOFGCnVZn6hmAIDk1PMoKCo1vFqGBPkiMToCfaKaIaJFMJJTz+NmUSkaVnt+bcpZFKs1AIB6fkpMGdHeqL+qHnskGEnTetoVX9XlVF2Wfrql9aoetzPUdv+u5MzctrRdzOWRvaovK8Cv8oOye2oNGgb54uFgGa7cEoymFas1JnP878RZuatv//9SzuCuWmuxNlRVdd91dk4AtfwLUP3bBU88zeIqdTX2uhC3s/KrrixXrLowdtUx5r/Ym19/29MsRERSwmJORCQBLOZERBLAYk5EJAEs5kREEsBiTkQkASzmREQSwGJORCQBLOZERBLAYk5EJAEs5kREEsBiTkQkASzmREQSwGJORCQBLOZERBLAYk5EJAEs5kREEsBiTkQkASzmREQSwGJORCQBLOZERBLAYk5EJAEs5kREEsBiTkQkASzmREQSwGJORCQBLOZERBLAYk5EJAEs5kREEsBiTkQkASzmREQSwGJORCQBLOZERBLAYk5EJAEs5kREEsBiTkQkASzmREQSwGJORCQBLOZERBLAYk5EJAEs5kREEsBiTkQkASzmREQSwGJORCQBLOZERBLAYk5EJAEs5kREEsBiTkQkASzmREQSIKqYZ2VlIT4+HoMGDUJ8fDwuX75cy2ERuQZzm6TCS0yjhQsXYsyYMYiNjcXOnTuxYMECJCcn273Q79OuITn1PG4WlSLATwkAuKfWoGGQLxKjI9AnqpnJedamnEWxWmP0vLdSDpVSUeN5j7L1ursjMAgJ8oW2QovC4nKj5+v5KY3GoJ6VfqqOoblxE9PG0WU4ytm57anM7T8AIJMBguCGoKrzoP1Er56fElNGtAcAJKeeR0FRKeRyGXQ6AQ38FHge15yek/ayemR+69YtnDt3DjExMQCAmJgYnDt3DoWFhXYt8Pu0a1ix7QwKikohAChWa1Cs1kAAUFBUihXbzuD7tGtG8/yWVYLlX5w2mYhlGp1nF3IPU1BUWqOQA6gxBr9llZjto/oYmho3MW0scXR+MZyd257q+7Rr+Pd/TO8/gIcUcg9VrNbgw62/YPkXp1FQVAoA0OkqN9gdtdbpOekIq8U8NzcXjRo1gkKhAAAoFAqEhoYiNzfXrgUmp55HmUZrdnqZRovk1PNGzx08cxcVWmacq5RptDh45q7Z6abGsPq4iWljiaPzi+Hs3PZUyannodVx/7GXAJitP87OSUeIOs3iqPT0dMPf+lc3SwqKSpGWlmZ4fEdtvvjXdRp1IbIOLUHrIe9CJle4OxyDO2qt0RhUZW4Mq46bmDaWODq/K1TNa09TdRuJ2eccdf3kOtRr8hgaNOtS68syJ2PPq2je91Wo/Bu6dLmekpNWi3l
"text/plain": [
"<Figure size 432x288 with 6 Axes>"
]
},
"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",
"plt.show()"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "naive_bayes.ipynb",
"provenance": []
},
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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