Chapter 04 Dimentionality Reduction.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Chapter 04 Dimentionality Reduction.ipynb","provenance":[],"authorship_tag":"ABX9TyOkaVNPQ60BqZDZw4nSfZYA"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","metadata":{"id":"oN9_pSwHeGxx","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1627886148892,"user_tz":-180,"elapsed":1134,"user":{"displayName":"Savas Yıldırım","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdhYZMfq-hvK2xI7HqkzvJuCbfgFrIs4wypQEm5w=s64","userId":"10717726124681851716"}},"outputId":"7b152c6f-1433-4d03-9cf9-c0526f801446"},"source":["import numpy as np\n","from sklearn.decomposition import PCA\n","# Principal Component Analysis\n","\n","X = np.array([[-1, -1,3], [-2, -1, 3],  [-3, -2, -4], [-2, -1, 13],  [-3, -2, 1], [2, 1, 13],  [-3, 2, 1]])\n","X"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-1, -1,  3],\n","       [-2, -1,  3],\n","       [-3, -2, -4],\n","       [-2, -1, 13],\n","       [-3, -2,  1],\n","       [ 2,  1, 13],\n","       [-3,  2,  1]])"]},"metadata":{"tags":[]},"execution_count":1}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tOoeIVEdeSau","executionInfo":{"status":"ok","timestamp":1627886157742,"user_tz":-180,"elapsed":340,"user":{"displayName":"Savas Yıldırım","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdhYZMfq-hvK2xI7HqkzvJuCbfgFrIs4wypQEm5w=s64","userId":"10717726124681851716"}},"outputId":"d84a7a83-18e5-43b8-a9c9-7d4f04cfe03b"},"source":["pca = PCA(n_components=2)\n","pca.fit(X)\n","pca"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,\n","    svd_solver='auto', tol=0.0, whiten=False)"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TW8-Mzr7ezvA","executionInfo":{"status":"ok","timestamp":1627886164443,"user_tz":-180,"elapsed":251,"user":{"displayName":"Savas Yıldırım","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdhYZMfq-hvK2xI7HqkzvJuCbfgFrIs4wypQEm5w=s64","userId":"10717726124681851716"}},"outputId":"76727b70-7db9-4858-9106-5923b545723c"},"source":["print(pca.explained_variance_ratio_)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[0.92260355 0.04773706]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Le0V0n5Uez_D"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UCzOL37PfmB7","executionInfo":{"status":"ok","timestamp":1627886230411,"user_tz":-180,"elapsed":259,"user":{"displayName":"Savas Yıldırım","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdhYZMfq-hvK2xI7HqkzvJuCbfgFrIs4wypQEm5w=s64","userId":"10717726124681851716"}},"outputId":"db581765-87ab-4678-c39e-b41b91040ea1"},"source":["pca.transform(X)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-1.14649608,  0.1811109 ],\n","       [-1.34758941, -0.28779877],\n","       [-8.46357142, -0.43837663],\n","       [ 8.41384957, -1.98093838],\n","       [-3.58285193, -1.28494644],\n","       [ 9.38198567,  1.62843214],\n","       [-3.25532639,  2.18251718]])"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rLRA3AM3f9K9","executionInfo":{"status":"ok","timestamp":1627886262286,"user_tz":-180,"elapsed":259,"user":{"displayName":"Savas Yıldırım","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdhYZMfq-hvK2xI7HqkzvJuCbfgFrIs4wypQEm5w=s64","userId":"10717726124681851716"}},"outputId":"7d2e2acb-5095-4407-cefb-d18231ca6453"},"source":["pca.transform([[1,1,1]])"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-2.53283445,  3.19128999]])"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dhFBfWmkfy8C","executionInfo":{"status":"ok","timestamp":1627886274613,"user_tz":-180,"elapsed":254,"user":{"displayName":"Savas Yıldırım","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdhYZMfq-hvK2xI7HqkzvJuCbfgFrIs4wypQEm5w=s64","userId":"10717726124681851716"}},"outputId":"e81c03b6-6659-4cec-da25-3a37832a8c9c"},"source":["from sklearn.datasets import load_iris\n","iris = load_iris()\n","print(iris.data.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["(150, 4)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"DNI8tN8rhIlO","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1627886276523,"user_tz":-180,"elapsed":249,"user":{"displayName":"Savas Yıldırım","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdhYZMfq-hvK2xI7HqkzvJuCbfgFrIs4wypQEm5w=s64","userId":"10717726124681851716"}},"outputId":"1643b8a3-62bd-41bc-e087-2d0522a765ba"},"source":["iris.data"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[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]])"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GwLaPxW9hC1U","executionInfo":{"status":"ok","timestamp":1627281051966,"user_tz":-180,"elapsed":428,"user":{"displayName":"Savas Yıldırım","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdhYZMfq-hvK2xI7HqkzvJuCbfgFrIs4wypQEm5w=s64","userId":"10717726124681851716"}},"outputId":"ccf47e52-7dab-409f-ef88-05a79f8b9787"},"source":["iris.target"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([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, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 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,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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,\n","       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"]},"metadata":{"tags":[]},"execution_count":42}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-lxSjmxZhFFX","executionInfo":{"status":"ok","timestamp":1627281078124,"user_tz":-180,"elapsed":443,"user":{"displayName":"Savas Yıldırım","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdhYZMfq-hvK2xI7HqkzvJuCbfgFrIs4wypQEm5w=s64","userId":"10717726124681851716"}},"outputId":"2f324293-ef01-46af-b595-bdeba9c6114a"},"source":["iris.target_names"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"]},"metadata":{"tags":[]},"execution_count":44}]},{"cell_type":"code","metadata":{"id":"FKndXd7Sf9xq","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1627886288636,"user_tz":-180,"elapsed":257,"user":{"displayName":"Savas Yıldırım","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdhYZMfq-hvK2xI7HqkzvJuCbfgFrIs4wypQEm5w=s64","userId":"10717726124681851716"}},"outputId":"ef1f88cb-3e5b-43e2-fde2-767fe4136f9b"},"source":["pca = PCA(n_components=2)\n","pca.fit(iris.data)\n","pca"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,\n","    svd_solver='auto', tol=0.0, whiten=False)"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"id":"OXH9N5rSg7Pr","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1627886291186,"user_tz":-180,"elapsed":338,"user":{"displayName":"Savas Yıldırım","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdhYZMfq-hvK2xI7HqkzvJuCbfgFrIs4wypQEm5w=s64","userId":"10717726124681851716"}},"outputId":"438b8bee-a3de-4619-e1fd-ca3a568a3b97"},"source":["iris2D=pca.transform(iris.data)\n","iris2D"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-2.68412563,  0.31939725],\n","       [-2.71414169, -0.17700123],\n","       [-2.88899057, -0.14494943],\n","       [-2.74534286, -0.31829898],\n","       [-2.72871654,  0.32675451],\n","       [-2.28085963,  0.74133045],\n","       [-2.82053775, -0.08946138],\n","       [-2.62614497,  0.16338496],\n","       [-2.88638273, -0.57831175],\n","       [-2.6727558 , -0.11377425],\n","       [-2.50694709,  0.6450689 ],\n","       [-2.61275523,  0.01472994],\n","       [-2.78610927, -0.235112  ],\n","       [-3.22380374, -0.51139459],\n","       [-2.64475039,  1.17876464],\n","       [-2.38603903,  1.33806233],\n","       [-2.62352788,  0.81067951],\n","       [-2.64829671,  0.31184914],\n","       [-2.19982032,  0.87283904],\n","       [-2.5879864 ,  0.51356031],\n","       [-2.31025622,  0.39134594],\n","       [-2.54370523,  0.43299606],\n","       [-3.21593942,  0.13346807],\n","       [-2.30273318,  0.09870885],\n","       [-2.35575405, -0.03728186],\n","       [-2.50666891, -0.14601688],\n","       [-2.46882007,  0.13095149],\n","       [-2.56231991,  0.36771886],\n","       [-2.63953472,  0.31203998],\n","       [-2.63198939, -0.19696122],\n","       [-2.58739848, -0.20431849],\n","       [-2.4099325 ,  0.41092426],\n","       [-2.64886233,  0.81336382],\n","       [-2.59873675,  1.09314576],\n","       [-2.63692688, -0.12132235],\n","       [-2.86624165,  0.06936447],\n","       [-2.62523805,  0.59937002],\n","       [-2.80068412,  0.26864374],\n","       [-2.98050204, -0.48795834],\n","       [-2.59000631,  0.22904384],\n","       [-2.77010243,  0.26352753],\n","       [-2.84936871, -0.94096057],\n","       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2.41874618,  0.3047982 ],\n","       [ 1.94410979,  0.1875323 ],\n","       [ 1.52716661, -0.37531698],\n","       [ 1.76434572,  0.07885885],\n","       [ 1.90094161,  0.11662796],\n","       [ 1.39018886, -0.28266094]])"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"id":"4KB_-n7OhFAm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1627886297487,"user_tz":-180,"elapsed":482,"user":{"displayName":"Savas Yıldırım","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdhYZMfq-hvK2xI7HqkzvJuCbfgFrIs4wypQEm5w=s64","userId":"10717726124681851716"}},"outputId":"c4160c11-cf99-44cf-b217-064d6771a8df"},"source":["iris2D=pca.fit_transform(iris.data)\n","iris2D"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-2.68412563,  0.31939725],\n","       [-2.71414169, -0.17700123],\n","       [-2.88899057, -0.14494943],\n","       [-2.74534286, -0.31829898],\n","       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-0.17700123],\n","       [-2.88899057, -0.14494943]])"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZKmjgSA1hbq6","executionInfo":{"status":"ok","timestamp":1627886338198,"user_tz":-180,"elapsed":235,"user":{"displayName":"Savas Yıldırım","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdhYZMfq-hvK2xI7HqkzvJuCbfgFrIs4wypQEm5w=s64","userId":"10717726124681851716"}},"outputId":"7791602f-df66-49f6-add9-d073d8f3f4a1"},"source":["iris.data[:3,]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[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]])"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"id":"54smyoDihlkL"},"source":["x=iris2D[:,0]\n","y=iris2D[:,1]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":269},"id":"NvguNzEihU9y","executionInfo":{"status":"ok","timestamp":1627886352345,"user_tz":-180,"elapsed":450,"user":{"displayName":"Savas Yıldırım","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdhYZMfq-hvK2xI7HqkzvJuCbfgFrIs4wypQEm5w=s64","userId":"10717726124681851716"}},"outputId":"b6ebc4e6-84f9-446c-d8c9-27424286a00c"},"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","\n","plt.scatter(x, y)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"xIzm4cVtmfs8"},"source":["import seaborn as sns"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":669},"id":"Q34wil2kmnd0","executionInfo":{"status":"ok","timestamp":1627886358346,"user_tz":-180,"elapsed":745,"user":{"displayName":"Savas Yıldırım","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdhYZMfq-hvK2xI7HqkzvJuCbfgFrIs4wypQEm5w=s64","userId":"10717726124681851716"}},"outputId":"ccdea75e-5e9c-488c-8ba8-5a918e0142da"},"source":["sns.set(rc={'figure.figsize':(10,10)})\n","sns.scatterplot(x,y, hue= iris.target)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n","  FutureWarning\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2vEZSQFLUZZb","executionInfo":{"status":"ok","timestamp":1627886386845,"user_tz":-180,"elapsed":5,"user":{"displayName":"Savas Yıldırım","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdhYZMfq-hvK2xI7HqkzvJuCbfgFrIs4wypQEm5w=s64","userId":"10717726124681851716"}},"outputId":"04775aac-ceac-4026-c294-688ca57ab15e"},"source":["# transform a point\n","pca.transform([[0,0,0,0]])"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-5.50236513, -5.32695258]])"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oW593RVpi0Vp","executionInfo":{"status":"ok","timestamp":1627886403001,"user_tz":-180,"elapsed":6934,"user":{"displayName":"Savas Yıldırım","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdhYZMfq-hvK2xI7HqkzvJuCbfgFrIs4wypQEm5w=s64","userId":"10717726124681851716"}},"outputId":"108cd122-7651-47e9-adfe-6c9540d17533"},"source":["!pip install umap-learn"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting umap-learn\n","  Downloading umap-learn-0.5.1.tar.gz (80 kB)\n","\u001b[K     |████████████████████████████████| 80 kB 4.1 MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (1.19.5)\n","Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (0.22.2.post1)\n","Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (1.4.1)\n","Requirement already satisfied: numba>=0.49 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (0.51.2)\n","Collecting pynndescent>=0.5\n","  Downloading pynndescent-0.5.4.tar.gz (1.1 MB)\n","\u001b[K     |████████████████████████████████| 1.1 MB 11.8 MB/s \n","\u001b[?25hRequirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba>=0.49->umap-learn) (57.2.0)\n","Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.49->umap-learn) (0.34.0)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from pynndescent>=0.5->umap-learn) (1.0.1)\n","Building wheels for collected packages: umap-learn, pynndescent\n","  Building wheel for umap-learn (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for umap-learn: filename=umap_learn-0.5.1-py3-none-any.whl size=76566 sha256=d948fe742602bc5b7d33f8088b7ff1de548fa944d6d2d5802a17d5f616dca9a3\n","  Stored in directory: /root/.cache/pip/wheels/01/e7/bb/347dc0e510803d7116a13d592b10cc68262da56a8eec4dd72f\n","  Building wheel for pynndescent (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for pynndescent: filename=pynndescent-0.5.4-py3-none-any.whl size=52372 sha256=cc400f911418bd7af43473c7a9545a0d19d69dcb8341a410e6367335d6ff5d36\n","  Stored in directory: /root/.cache/pip/wheels/d0/5b/62/3401692ddad12324249c774c4b15ccb046946021e2b581c043\n","Successfully built umap-learn pynndescent\n","Installing collected packages: pynndescent, umap-learn\n","Successfully installed pynndescent-0.5.4 umap-learn-0.5.1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"oG4ffPHml0y-"},"source":["import umap\n","iris2D_umap= umap.UMAP(n_components=2, random_state=42).fit_transform(iris.data)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":665},"id":"bNtnEwg-onIT","executionInfo":{"status":"ok","timestamp":1627886459973,"user_tz":-180,"elapsed":573,"user":{"displayName":"Savas Yıldırım","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdhYZMfq-hvK2xI7HqkzvJuCbfgFrIs4wypQEm5w=s64","userId":"10717726124681851716"}},"outputId":"0a730b05-452a-4d05-ff9d-62ae970054fd"},"source":["x= iris2D_umap[:,0]\n","y= iris2D_umap[:,1]\n","sns.set(rc={'figure.figsize':(10,10)})\n","sns.scatterplot(x,y, hue= iris.target)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n","  FutureWarning\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7efefc991f50>"]},"metadata":{"tags":[]},"execution_count":24},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"GHRH0oj9oz5N"},"source":["from sklearn import datasets"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Jx9f67FDrIRj"},"source":["* Quiz\n","\n","Take Boston Dataset as in the following. \n","\n","```\n","boston=datasets.load_boston()\n","```\n","\n","Q1: \n","how many rows in boston dataset\n","\n","\n","Q2: Apply PCA to Boston Dataset to reduce 2 dimensions. What is the first component(x) of the first row ?\n","\n","Q3: Apply PCA to Boston Dataset to reduce 3 dimensions. What is the first explained_variance_ratio_ ?\n","\n","Q4:\n","Apply PCA to iris dataset to reduce 3 dimensions. What is the first explained_variance_ratio_ ?\n","\n","Q5: Appyl PCA to iris dataset to reduce 3 dimensions. What is the first component(x) of the first row ?\n","\n"]},{"cell_type":"code","metadata":{"id":"zGEfOojNjIvI"},"source":["iris= datasets.load_iris()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"J0uHiu0vr8ku"},"source":[""]},{"cell_type":"code","metadata":{"id":"OgA04V-crGHy"},"source":["boston=datasets.load_boston()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5NxhfGhNvJcl","executionInfo":{"status":"ok","timestamp":1627886506672,"user_tz":-180,"elapsed":371,"user":{"displayName":"Savas Yıldırım","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdhYZMfq-hvK2xI7HqkzvJuCbfgFrIs4wypQEm5w=s64","userId":"10717726124681851716"}},"outputId":"e9b43d09-7a65-4fb5-fd07-4b23bb94c075"},"source":["#1\n","boston.data.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(506, 13)"]},"metadata":{"tags":[]},"execution_count":28}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3TK69ftYv6c_","executionInfo":{"status":"ok","timestamp":1627886533134,"user_tz":-180,"elapsed":255,"user":{"displayName":"Savas Yıldırım","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdhYZMfq-hvK2xI7HqkzvJuCbfgFrIs4wypQEm5w=s64","userId":"10717726124681851716"}},"outputId":"9df5f220-aabd-46d0-92f0-707521f716df"},"source":["# 2\n","from sklearn.decomposition import PCA\n","pca=PCA(n_components=2)\n","pca.fit_transform(boston.data)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-119.81884272,   -5.56005586],\n","       [-168.89015548,   10.11620863],\n","       [-169.31170747,   14.0805323 ],\n","       ...,\n","       [-138.38716306,    0.9380922 ],\n","       [-137.50517338,    4.2518251 ],\n","       [-139.19033295,    1.00906423]])"]},"metadata":{"tags":[]},"execution_count":29}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1KkEdOZuwWmr","executionInfo":{"status":"ok","timestamp":1627886568227,"user_tz":-180,"elapsed":274,"user":{"displayName":"Savas Yıldırım","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdhYZMfq-hvK2xI7HqkzvJuCbfgFrIs4wypQEm5w=s64","userId":"10717726124681851716"}},"outputId":"a2cae3be-8407-411a-9942-acd7a85dd435"},"source":["#3  Apply PCA to Boston Dataset to reduce 3 dimensions. What is the first explained_variance_ratio_ ?\n","from sklearn.decomposition import PCA\n","pca=PCA(n_components=3)\n","pca.fit(boston.data)\n","pca.explained_variance_ratio_"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.80582318, 0.16305197, 0.02134861])"]},"metadata":{"tags":[]},"execution_count":30}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Y27pPe_xwpX3","executionInfo":{"status":"ok","timestamp":1627886608907,"user_tz":-180,"elapsed":261,"user":{"displayName":"Savas Yıldırım","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdhYZMfq-hvK2xI7HqkzvJuCbfgFrIs4wypQEm5w=s64","userId":"10717726124681851716"}},"outputId":"81fc0a63-7c33-49f0-91e0-742ada1957c1"},"source":["# Q4: Apply PCA to iris dataset to reduce 3 dimensions. What is the first explained_variance_ratio_ ?\n","from sklearn.decomposition import PCA\n","pca=PCA(n_components=3)\n","pca.fit(iris.data)\n","pca.explained_variance_ratio_"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.92461872, 0.05306648, 0.01710261])"]},"metadata":{"tags":[]},"execution_count":32}]},{"cell_type":"code","metadata":{"id":"vjgj-5KbxH5e"},"source":["# Apply PCA to iris dataset to reduce 3 dimensions. What is the first component(x) of the first row ?\n","from sklearn.decomposition import PCA\n","pca=PCA(n_components=3)\n","pca.fit_transform(iris.data)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"3xnAyIUgxgX2"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Uh1mwUGUrp_F"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Hvl-LyK7rqBc"},"source":["from sklearn.decomposition import PCA\n","pca=PCA(n_components=2)\n","pca.fit_transform(iris.data[:100])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"0HtjqxFxrrtw"},"source":[""],"execution_count":null,"outputs":[]}]}