{"nbformat":4,"nbformat_minor":2,"metadata":{"colab":{"name":"04M_Bayes_irysy.ipynb","provenance":[{"file_id":"18GZJRU1jLT766DuWKP8HaxfM0leRXJlH","timestamp":1573037468536}],"collapsed_sections":[]},"kernelspec":{"display_name":"Python [default]","language":"python","name":"python3"}},"cells":[{"cell_type":"code","execution_count":1,"source":["import matplotlib\r\n","%matplotlib inline\r\n","matplotlib.rcParams['figure.figsize']=(15,8)\r\n"],"outputs":[],"metadata":{"id":"Gq4B3HvSZ7fU","executionInfo":{"status":"ok","timestamp":1605702015562,"user_tz":-60,"elapsed":1972,"user":{"displayName":"Anna Dawid","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgUQAwZ7wyayL4BbiM0n_EANCgBjSdZ9H14lgcCFEE=s64","userId":"02862484648310443813"}}}},{"cell_type":"markdown","source":["# Naiwny klasyfikator Bayesa\r\n","Autor: Jarosław Żygierewicz\r\n","\r\n","Z klasyfikatorem tym zapoznamy się próbując klasyfikować gatunki irysów. Jest to klasyczny już problem, często wykorzystywany przy porównywaniu różnych technik klasyfikacji. Więcej o pochodzeniu tych danych i problemie można przeczytać tu [https://en.wikipedia.org/wiki/Iris_flower_data_set]\r\n","\r\n","Kod napiszemy w oparciu o implementacje klasyfikatora Bayesa z biblioteki scikit-learn [http://scikit-learn.org/stable/about.html#citing-scikit-learn]\r\n","\r\n","Zaczerpniemy stamtąd:\r\n","* obiekt klasyfikatora GaussianNB\r\n","* zbiór danych\r\n","* funkcje do oceny jakości "],"metadata":{"id":"g6aMBGt6Z7fY"}},{"cell_type":"markdown","source":["Zatem importujemy:"],"metadata":{"id":"_yOp4madZ7fZ"}},{"cell_type":"code","execution_count":2,"source":["from sklearn import datasets\r\n","from sklearn.naive_bayes import GaussianNB\r\n","import matplotlib.pyplot as plt\r\n","import numpy as np\r\n","from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\r\n"],"outputs":[],"metadata":{"id":"vgv3tI12Z7fa","executionInfo":{"status":"ok","timestamp":1605702016108,"user_tz":-60,"elapsed":2513,"user":{"displayName":"Anna Dawid","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgUQAwZ7wyayL4BbiM0n_EANCgBjSdZ9H14lgcCFEE=s64","userId":"02862484648310443813"}}}},{"cell_type":"markdown","source":["Przyda nam się potem funkcja rysująca dwuwymiarowe rozkłady Gaussa"],"metadata":{"id":"480Gh25dZ7fc"}},{"cell_type":"code","execution_count":3,"source":["def plot_gauss(mu,sigma,xx,yy):\r\n"," ''' Funkcja rysująca kontury funkcji gęstości prawdopodobieństwa \r\n"," dwuwymiarowego rozkładu Gaussa'''\r\n","\r\n"," XX = np.c_[xx.ravel(), yy.ravel()] \r\n"," R = XX - mu \r\n"," invS = np.linalg.inv(np.diag(sigma))\r\n"," z = np.zeros(len(R))\r\n"," for i in range(len(R)):\r\n"," z[i] = np.exp(-0.5*np.dot( R[i,:].T,np.dot(invS,R[i,:])))\r\n"," z.shape = xx.shape\r\n"," #plt.figure()\r\n"," plt.contourf(xx,yy,z,alpha = 0.5)\r\n"," plt.plot(mu[0],mu[1],'o')\r\n"," #plt.show()"],"outputs":[],"metadata":{"id":"JTJZsG8bZ7fd","executionInfo":{"status":"ok","timestamp":1605702016109,"user_tz":-60,"elapsed":2511,"user":{"displayName":"Anna Dawid","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgUQAwZ7wyayL4BbiM0n_EANCgBjSdZ9H14lgcCFEE=s64","userId":"02862484648310443813"}}}},{"cell_type":"markdown","source":["## Zbiór danych irys\n","Zapoznajemy się z danymi i wybieramy ich podzbiór do dalszej zabawy."],"metadata":{"id":"6R48NZA7Z7fh"}},{"cell_type":"markdown","source":["Ładujemy dane"],"metadata":{"id":"UFK-4Ph7Z7fh"}},{"cell_type":"code","execution_count":4,"source":["iris = datasets.load_iris() #https://en.wikipedia.org/wiki/Iris_flower_data_set"],"outputs":[],"metadata":{"id":"42SLWGQBZ7fi","executionInfo":{"status":"ok","timestamp":1605702016109,"user_tz":-60,"elapsed":2508,"user":{"displayName":"Anna Dawid","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgUQAwZ7wyayL4BbiM0n_EANCgBjSdZ9H14lgcCFEE=s64","userId":"02862484648310443813"}}}},{"cell_type":"markdown","source":["Zobaczmy co ten zbiór ma w środku:"],"metadata":{"id":"GAEWybxwZ7fl"}},{"cell_type":"code","execution_count":5,"source":["print(dir(iris))"],"outputs":[{"output_type":"stream","name":"stdout","text":["['DESCR', 'data', 'feature_names', 'filename', 'target', 'target_names']\n"]}],"metadata":{"id":"8IihOS07Z7fm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1605702016110,"user_tz":-60,"elapsed":2500,"user":{"displayName":"Anna Dawid","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgUQAwZ7wyayL4BbiM0n_EANCgBjSdZ9H14lgcCFEE=s64","userId":"02862484648310443813"}},"outputId":"4ba0f8bd-732e-4cf4-f149-03115552f864"}},{"cell_type":"markdown","source":["Wypiszemy sobie opis danych:"],"metadata":{"id":"JPZBOgBxZ7fs"}},{"cell_type":"code","execution_count":6,"source":["print(iris['DESCR'])"],"outputs":[{"output_type":"stream","name":"stdout","text":[".. _iris_dataset:\n","\n","Iris plants dataset\n","--------------------\n","\n","**Data Set Characteristics:**\n","\n"," :Number of Instances: 150 (50 in each of three classes)\n"," :Number of Attributes: 4 numeric, predictive attributes and the class\n"," :Attribute Information:\n"," - sepal length in cm\n"," - sepal width in cm\n"," - petal length in cm\n"," - petal width in cm\n"," - class:\n"," - Iris-Setosa\n"," - Iris-Versicolour\n"," - Iris-Virginica\n"," \n"," :Summary Statistics:\n","\n"," ============== ==== ==== ======= ===== ====================\n"," Min Max Mean SD Class Correlation\n"," ============== ==== ==== ======= ===== ====================\n"," sepal length: 4.3 7.9 5.84 0.83 0.7826\n"," sepal width: 2.0 4.4 3.05 0.43 -0.4194\n"," petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\n"," petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\n"," ============== ==== ==== ======= ===== ====================\n","\n"," :Missing Attribute Values: None\n"," :Class Distribution: 33.3% for each of 3 classes.\n"," :Creator: R.A. Fisher\n"," :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n"," :Date: July, 1988\n","\n","The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n","from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n","Machine Learning Repository, which has two wrong data points.\n","\n","This is perhaps the best known database to be found in the\n","pattern recognition literature. Fisher's paper is a classic in the field and\n","is referenced frequently to this day. (See Duda & Hart, for example.) The\n","data set contains 3 classes of 50 instances each, where each class refers to a\n","type of iris plant. One class is linearly separable from the other 2; the\n","latter are NOT linearly separable from each other.\n","\n",".. topic:: References\n","\n"," - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n"," Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n"," Mathematical Statistics\" (John Wiley, NY, 1950).\n"," - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n"," (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\n"," - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n"," Structure and Classification Rule for Recognition in Partially Exposed\n"," Environments\". IEEE Transactions on Pattern Analysis and Machine\n"," Intelligence, Vol. PAMI-2, No. 1, 67-71.\n"," - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\". IEEE Transactions\n"," on Information Theory, May 1972, 431-433.\n"," - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al\"s AUTOCLASS II\n"," conceptual clustering system finds 3 classes in the data.\n"," - Many, many more ...\n"]}],"metadata":{"id":"LR22U62NZ7fs","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1605702016111,"user_tz":-60,"elapsed":2495,"user":{"displayName":"Anna Dawid","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgUQAwZ7wyayL4BbiM0n_EANCgBjSdZ9H14lgcCFEE=s64","userId":"02862484648310443813"}},"outputId":"65962023-2d3c-4f09-ef5e-7dd575657952"}},{"cell_type":"markdown","source":["Wypiszmy nazwy gatunków:"],"metadata":{"id":"-qkkkTzgZ7fw"}},{"cell_type":"code","execution_count":7,"source":["print(iris['target_names'])"],"outputs":[{"output_type":"stream","name":"stdout","text":["['setosa' 'versicolor' 'virginica']\n"]}],"metadata":{"id":"dM4sQ9s_Z7fx","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1605702016111,"user_tz":-60,"elapsed":2489,"user":{"displayName":"Anna Dawid","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgUQAwZ7wyayL4BbiM0n_EANCgBjSdZ9H14lgcCFEE=s64","userId":"02862484648310443813"}},"outputId":"e717f93a-4b9d-465f-e829-f795aa09ab5e"}},{"cell_type":"markdown","source":["Wypiszmy nazwy cech:"],"metadata":{"id":"EYM_0H6AZ7f1"}},{"cell_type":"code","execution_count":8,"source":["print(iris['feature_names'])"],"outputs":[{"output_type":"stream","name":"stdout","text":["['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"]}],"metadata":{"id":"N-ZoaV6sZ7f2","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1605702016112,"user_tz":-60,"elapsed":2484,"user":{"displayName":"Anna Dawid","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgUQAwZ7wyayL4BbiM0n_EANCgBjSdZ9H14lgcCFEE=s64","userId":"02862484648310443813"}},"outputId":"af009737-a9a1-4d00-b160-3137802db031"}},{"cell_type":"markdown","source":["Wypiszmy kodowanie gatunków. To są wyjścia, które chcielibyśmy uzyskać od wytrenowanego klasyfikatora:"],"metadata":{"id":"V-_83R_CZ7f5"}},{"cell_type":"code","execution_count":9,"source":["print(iris['target'])"],"outputs":[{"output_type":"stream","name":"stdout","text":["[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n"," 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n"," 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n"," 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n"," 2 2]\n"]}],"metadata":{"id":"rs6J2LBVZ7f6","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1605702016112,"user_tz":-60,"elapsed":2478,"user":{"displayName":"Anna Dawid","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgUQAwZ7wyayL4BbiM0n_EANCgBjSdZ9H14lgcCFEE=s64","userId":"02862484648310443813"}},"outputId":"3df7772a-9cb5-4837-82bd-60e0c0b88115"}},{"cell_type":"markdown","source":["Wypiszmy wartości cech. Są to dane wejściowe do klasyfkiacji. "],"metadata":{"id":"iwNEvTXQZ7gA"}},{"cell_type":"code","execution_count":10,"source":["print(iris['data'])"],"outputs":[{"output_type":"stream","name":"stdout","text":["[[5.1 3.5 1.4 0.2]\n"," [4.9 3. 1.4 0.2]\n"," [4.7 3.2 1.3 0.2]\n"," [4.6 3.1 1.5 0.2]\n"," [5. 3.6 1.4 0.2]\n"," [5.4 3.9 1.7 0.4]\n"," [4.6 3.4 1.4 0.3]\n"," [5. 3.4 1.5 0.2]\n"," [4.4 2.9 1.4 0.2]\n"," [4.9 3.1 1.5 0.1]\n"," [5.4 3.7 1.5 0.2]\n"," [4.8 3.4 1.6 0.2]\n"," [4.8 3. 1.4 0.1]\n"," [4.3 3. 1.1 0.1]\n"," [5.8 4. 1.2 0.2]\n"," [5.7 4.4 1.5 0.4]\n"," [5.4 3.9 1.3 0.4]\n"," [5.1 3.5 1.4 0.3]\n"," [5.7 3.8 1.7 0.3]\n"," [5.1 3.8 1.5 0.3]\n"," [5.4 3.4 1.7 0.2]\n"," [5.1 3.7 1.5 0.4]\n"," [4.6 3.6 1. 0.2]\n"," [5.1 3.3 1.7 0.5]\n"," [4.8 3.4 1.9 0.2]\n"," [5. 3. 1.6 0.2]\n"," [5. 3.4 1.6 0.4]\n"," [5.2 3.5 1.5 0.2]\n"," [5.2 3.4 1.4 0.2]\n"," [4.7 3.2 1.6 0.2]\n"," [4.8 3.1 1.6 0.2]\n"," [5.4 3.4 1.5 0.4]\n"," [5.2 4.1 1.5 0.1]\n"," [5.5 4.2 1.4 0.2]\n"," [4.9 3.1 1.5 0.2]\n"," [5. 3.2 1.2 0.2]\n"," [5.5 3.5 1.3 0.2]\n"," [4.9 3.6 1.4 0.1]\n"," [4.4 3. 1.3 0.2]\n"," [5.1 3.4 1.5 0.2]\n"," [5. 3.5 1.3 0.3]\n"," [4.5 2.3 1.3 0.3]\n"," [4.4 3.2 1.3 0.2]\n"," [5. 3.5 1.6 0.6]\n"," [5.1 3.8 1.9 0.4]\n"," [4.8 3. 1.4 0.3]\n"," [5.1 3.8 1.6 0.2]\n"," [4.6 3.2 1.4 0.2]\n"," [5.3 3.7 1.5 0.2]\n"," [5. 3.3 1.4 0.2]\n"," [7. 3.2 4.7 1.4]\n"," [6.4 3.2 4.5 1.5]\n"," [6.9 3.1 4.9 1.5]\n"," [5.5 2.3 4. 1.3]\n"," [6.5 2.8 4.6 1.5]\n"," [5.7 2.8 4.5 1.3]\n"," [6.3 3.3 4.7 1.6]\n"," [4.9 2.4 3.3 1. ]\n"," [6.6 2.9 4.6 1.3]\n"," [5.2 2.7 3.9 1.4]\n"," [5. 2. 3.5 1. ]\n"," [5.9 3. 4.2 1.5]\n"," [6. 2.2 4. 1. ]\n"," [6.1 2.9 4.7 1.4]\n"," [5.6 2.9 3.6 1.3]\n"," [6.7 3.1 4.4 1.4]\n"," [5.6 3. 4.5 1.5]\n"," [5.8 2.7 4.1 1. ]\n"," [6.2 2.2 4.5 1.5]\n"," [5.6 2.5 3.9 1.1]\n"," [5.9 3.2 4.8 1.8]\n"," [6.1 2.8 4. 1.3]\n"," [6.3 2.5 4.9 1.5]\n"," [6.1 2.8 4.7 1.2]\n"," [6.4 2.9 4.3 1.3]\n"," [6.6 3. 4.4 1.4]\n"," [6.8 2.8 4.8 1.4]\n"," [6.7 3. 5. 1.7]\n"," [6. 2.9 4.5 1.5]\n"," [5.7 2.6 3.5 1. ]\n"," [5.5 2.4 3.8 1.1]\n"," [5.5 2.4 3.7 1. ]\n"," [5.8 2.7 3.9 1.2]\n"," [6. 2.7 5.1 1.6]\n"," [5.4 3. 4.5 1.5]\n"," [6. 3.4 4.5 1.6]\n"," [6.7 3.1 4.7 1.5]\n"," [6.3 2.3 4.4 1.3]\n"," [5.6 3. 4.1 1.3]\n"," [5.5 2.5 4. 1.3]\n"," [5.5 2.6 4.4 1.2]\n"," [6.1 3. 4.6 1.4]\n"," [5.8 2.6 4. 1.2]\n"," [5. 2.3 3.3 1. ]\n"," [5.6 2.7 4.2 1.3]\n"," [5.7 3. 4.2 1.2]\n"," [5.7 2.9 4.2 1.3]\n"," [6.2 2.9 4.3 1.3]\n"," [5.1 2.5 3. 1.1]\n"," [5.7 2.8 4.1 1.3]\n"," [6.3 3.3 6. 2.5]\n"," [5.8 2.7 5.1 1.9]\n"," [7.1 3. 5.9 2.1]\n"," [6.3 2.9 5.6 1.8]\n"," [6.5 3. 5.8 2.2]\n"," [7.6 3. 6.6 2.1]\n"," [4.9 2.5 4.5 1.7]\n"," [7.3 2.9 6.3 1.8]\n"," [6.7 2.5 5.8 1.8]\n"," [7.2 3.6 6.1 2.5]\n"," [6.5 3.2 5.1 2. ]\n"," [6.4 2.7 5.3 1.9]\n"," [6.8 3. 5.5 2.1]\n"," [5.7 2.5 5. 2. ]\n"," [5.8 2.8 5.1 2.4]\n"," [6.4 3.2 5.3 2.3]\n"," [6.5 3. 5.5 1.8]\n"," [7.7 3.8 6.7 2.2]\n"," [7.7 2.6 6.9 2.3]\n"," [6. 2.2 5. 1.5]\n"," [6.9 3.2 5.7 2.3]\n"," [5.6 2.8 4.9 2. ]\n"," [7.7 2.8 6.7 2. ]\n"," [6.3 2.7 4.9 1.8]\n"," [6.7 3.3 5.7 2.1]\n"," [7.2 3.2 6. 1.8]\n"," [6.2 2.8 4.8 1.8]\n"," [6.1 3. 4.9 1.8]\n"," [6.4 2.8 5.6 2.1]\n"," [7.2 3. 5.8 1.6]\n"," [7.4 2.8 6.1 1.9]\n"," [7.9 3.8 6.4 2. ]\n"," [6.4 2.8 5.6 2.2]\n"," [6.3 2.8 5.1 1.5]\n"," [6.1 2.6 5.6 1.4]\n"," [7.7 3. 6.1 2.3]\n"," [6.3 3.4 5.6 2.4]\n"," [6.4 3.1 5.5 1.8]\n"," [6. 3. 4.8 1.8]\n"," [6.9 3.1 5.4 2.1]\n"," [6.7 3.1 5.6 2.4]\n"," [6.9 3.1 5.1 2.3]\n"," [5.8 2.7 5.1 1.9]\n"," [6.8 3.2 5.9 2.3]\n"," [6.7 3.3 5.7 2.5]\n"," [6.7 3. 5.2 2.3]\n"," [6.3 2.5 5. 1.9]\n"," [6.5 3. 5.2 2. ]\n"," [6.2 3.4 5.4 2.3]\n"," [5.9 3. 5.1 1.8]]\n"]}],"metadata":{"id":"7secdC2uZ7gA","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1605702016113,"user_tz":-60,"elapsed":2474,"user":{"displayName":"Anna Dawid","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgUQAwZ7wyayL4BbiM0n_EANCgBjSdZ9H14lgcCFEE=s64","userId":"02862484648310443813"}},"outputId":"bb73542a-3c07-4512-fe06-28c41aead297"}},{"cell_type":"markdown","source":["Zatem, np. obserwacja nr 5 ma cechy:"],"metadata":{"id":"a3TCsNIyZ7gE"}},{"cell_type":"code","execution_count":11,"source":["print(iris.data[5,:])"],"outputs":[{"output_type":"stream","name":"stdout","text":["[5.4 3.9 1.7 0.4]\n"]}],"metadata":{"id":"QUWwl7w0Z7gF","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1605702016114,"user_tz":-60,"elapsed":2469,"user":{"displayName":"Anna Dawid","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgUQAwZ7wyayL4BbiM0n_EANCgBjSdZ9H14lgcCFEE=s64","userId":"02862484648310443813"}},"outputId":"ca99a7af-9ece-48b1-ee4f-52cada383693"}},{"cell_type":"markdown","source":["i ma przypisaną klasę:"],"metadata":{"id":"mPJTZoGqZ7gJ"}},{"cell_type":"code","execution_count":12,"source":["print(iris.target[5])"],"outputs":[{"output_type":"stream","name":"stdout","text":["0\n"]}],"metadata":{"id":"Cs7o4JtcZ7gK","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1605702016114,"user_tz":-60,"elapsed":2463,"user":{"displayName":"Anna Dawid","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgUQAwZ7wyayL4BbiM0n_EANCgBjSdZ9H14lgcCFEE=s64","userId":"02862484648310443813"}},"outputId":"7e93daf9-9e36-4016-d010-2b8df433ba7f"}},{"cell_type":"markdown","source":["Czyli jest to gatunek:"],"metadata":{"id":"FdTaCybgZ7gQ"}},{"cell_type":"code","execution_count":13,"source":["print(iris.target_names[iris.target[5]])"],"outputs":[{"output_type":"stream","name":"stdout","text":["setosa\n"]}],"metadata":{"id":"oPjGr9meZ7gR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1605702016115,"user_tz":-60,"elapsed":2458,"user":{"displayName":"Anna Dawid","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgUQAwZ7wyayL4BbiM0n_EANCgBjSdZ9H14lgcCFEE=s64","userId":"02862484648310443813"}},"outputId":"7a5062ab-2130-43d4-f825-68ee3c825194"}},{"cell_type":"markdown","source":["## Ilustrowanie własności zbioru danych"],"metadata":{"id":"qVa4NHQ4Z7gX"}},{"cell_type":"markdown","source":["Do rysowania zależniści między cechami i klasami przyda nam się własną mapę kolorów:"],"metadata":{"id":"iOF6SXcoZ7gX"}},{"cell_type":"code","execution_count":14,"source":["color_map = {-1: (1, 1, 1), 0: (0, 0, .9), 1: (1, 0, 0), 2: (.5, .5, 0)}"],"outputs":[],"metadata":{"id":"y2dlvcxhZ7gZ","executionInfo":{"status":"ok","timestamp":1605702016115,"user_tz":-60,"elapsed":2456,"user":{"displayName":"Anna Dawid","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgUQAwZ7wyayL4BbiM0n_EANCgBjSdZ9H14lgcCFEE=s64","userId":"02862484648310443813"}}}},{"cell_type":"markdown","source":["Wytwarzamy wektor, który każdemu wierszowi w tabeli danych przypisze kolor odpowiadający gatunkowi irysa"],"metadata":{"id":"dd99qOSZZ7gb"}},{"cell_type":"code","execution_count":15,"source":["colors = [color_map[y] for y in iris.target]"],"outputs":[],"metadata":{"id":"kcrKjYkZZ7gb","executionInfo":{"status":"ok","timestamp":1605702016116,"user_tz":-60,"elapsed":2455,"user":{"displayName":"Anna Dawid","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgUQAwZ7wyayL4BbiM0n_EANCgBjSdZ9H14lgcCFEE=s64","userId":"02862484648310443813"}}}},{"cell_type":"markdown","source":["### Aby przyjrzeć się zbiorowi danych warto zbadać: \n","#### 1) Rozkłady cech w klasach: \n","* np. histogramy. Proszę wykreślić histogramy rozkładu cech w poszczególnych klasach:"],"metadata":{"id":"-77CE_gBZ7gd"}},{"cell_type":"code","execution_count":16,"source":["plt.figure()\r\n","for f, f_name in enumerate(iris['feature_names']):\r\n"," plt.subplot(1,4,f+1)\r\n"," for k in range(3): # k - klasa\r\n"," plt.hist(iris.data[iris.target==k,f],color=color_map[k],alpha=0.3,bins=np.arange(0,8,0.2))\r\n"," plt.xlabel(str(f)+' '+ f_name)\r\n","plt.show() "],"outputs":[{"output_type":"display_data","data":{"image/png":"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","text/plain":["