diff --git a/Lecture-Slides/Machine-Learning-Lecture/MachineLearningIISlide.pdf b/Lecture-Slides/Machine-Learning-Lecture/MachineLearningIISlide.pdf index 39dcdef..7a0e8e4 100644 Binary files a/Lecture-Slides/Machine-Learning-Lecture/MachineLearningIISlide.pdf and b/Lecture-Slides/Machine-Learning-Lecture/MachineLearningIISlide.pdf differ diff --git a/Lecture-Slides/Machine-Learning-Lecture/ipynb/Classification.ipynb b/Lecture-Slides/Machine-Learning-Lecture/ipynb/Classification.ipynb new file mode 100644 index 0000000..9810dda --- /dev/null +++ b/Lecture-Slides/Machine-Learning-Lecture/ipynb/Classification.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","id":"8244638f-81cd-4e4c-94dd-5e4a1f245993","metadata":{},"source":"Code for Machine Learning and Data Science II\n=============================================\n\n"},{"cell_type":"markdown","id":"4c0065e3-9790-4748-95e0-7f153dabea7b","metadata":{},"source":["These are the code snippets used in Classification\npart of Machine Learning and Data Science II.\n\n"]},{"cell_type":"markdown","id":"88803470-b332-4009-b876-f2d9d2741fcd","metadata":{},"source":["### Introduction\n\n"]},{"cell_type":"markdown","id":"91407057-4815-43eb-ab06-469ea6f33de6","metadata":{},"source":["#### Preamble\n\n"]},{"cell_type":"code","execution_count":1,"id":"d2289792-84e7-4f0e-8128-720de76f53e7","metadata":{},"outputs":[],"source":["# import for custom color cycling\nfrom cycler import cycler \n\n# define custom default colors for generating image with\n# consisten color cycling\nnew_colors = ['#326199',\n '#4fb1a1',\n '#fcc055',\n '#eb8d50',\n '#df6e5b',\n '#9a031e',\n '#984ea3']\n\n\n# define a function to store matplotlib style information\ndef plot_settings(style, size=None):\n \n # define the styles for generating web compatible images.\n if style == \"web\":\n style = {\n 'axes.edgecolor': '1e1e2e',\n 'axes.facecolor': '363a4f',\n 'axes.axisbelow' : True,\n 'axes.labelcolor' : 'cad3f5',\n 'axes.grid': True,\n 'axes.grid.which': 'both',\n 'axes.spines.left': False,\n 'axes.spines.right': False,\n 'axes.spines.top': False,\n 'axes.spines.bottom': False,\n 'axes.prop_cycle': cycler(color=new_colors),\n\n 'grid.color': '5b6078',\n 'grid.linewidth': '1.2',\n \n 'xtick.color': 'cad3f5',\n 'xtick.major.bottom': True,\n 'xtick.labelsize': 10,\n 'xtick.minor.bottom': True,\n 'xtick.minor.bottom': True,\n 'xtick.minor.visible': True,\n 'xtick.minor.width': 0.5,\n \n 'ytick.color': 'cad3f5',\n 'ytick.major.left': True,\n 'ytick.minor.left': False,\n 'ytick.minor.visible': True,\n 'ytick.labelsize': 10,\n\n 'savefig.facecolor': '363a4f',\n\n 'text.color': 'cad3f5',\n \n 'lines.linewidth': 4,\n\n 'font.size': 16,\n \n 'legend.fancybox' : False,\n 'legend.facecolor' : '6c7086',\n \n 'figure.facecolor': '838ba7',\n }\n\n # Define the font dictionary to store label formatting\n font = {'color': '#cad3f5',\n 'weight': 'normal',\n 'size': 16,\n } \n\n elif style == \"slide\":\n style = {\n 'axes.edgecolor': 'f0f0f0',\n 'axes.facecolor': 'fafafa',\n 'axes.axisbelow' : True,\n 'axes.labelcolor' : '1e1e1e',\n 'axes.grid': True,\n 'axes.grid.which': 'both',\n 'axes.spines.left': False,\n 'axes.spines.right': False,\n 'axes.spines.top': False,\n 'axes.spines.bottom': False,\n 'axes.prop_cycle': cycler(color=new_colors),\n\n 'grid.color': 'f0f0f0',\n 'grid.linewidth': '1.2',\n \n 'xtick.color': '1e1e1e',\n 'xtick.major.bottom': True,\n 'xtick.labelsize': 10,\n 'xtick.minor.bottom': True,\n 'xtick.minor.bottom': True,\n 'xtick.minor.visible': True,\n 'xtick.minor.width': 0.5,\n \n 'ytick.color': '1e1e1e',\n 'ytick.major.left': True,\n 'ytick.minor.left': False,\n 'ytick.labelsize': 10,\n\n 'savefig.facecolor': 'fafafa',\n\n 'text.color': '1e1e1e',\n \n 'lines.linewidth': 4,\n\n 'font.size': 16,\n \n 'legend.fancybox' : False,\n 'legend.facecolor' : '6c7086',\n \n 'figure.facecolor': 'fafafa',\n }\n\n # Define the font dictionary to store label formatting\n font = {'color': '#1e1e1e',\n 'weight': 'normal',\n 'size': 16,\n } \n\n # Apply style sheet for use in matplotlib\n plt.rcParams.update(style)\n \n # Define figure size based on the number of figures\n if size == 1:\n plt.figure(figsize = (10, 6))\n elif size == 2:\n plt.figure(figsize = (12, 5))\n elif size == 3:\n plt.figure(figsize = (12, 8))\n elif size == None:\n return 0\n\ndef grid_settings(style):\n\n if style == \"web\":\n plt.grid(which='minor', color='#5b6078', linestyle=':', linewidth=0.5)\n plt.grid(which='major', color='#5b6078', linestyle=':', linewidth=0.8)\n \n elif style == \"slide\": \n plt.grid(which='minor', color='#c8c8c8', linestyle=':', linewidth=0.5)\n plt.grid(which='major', color='#c8c8c8', linestyle=':', linewidth=0.8)\n\n plt.minorticks_on()"]},{"cell_type":"code","execution_count":1,"id":"aaf3a5c5-6b43-415e-b65c-d43edcafbaf0","metadata":{},"outputs":[],"source":["from pathlib import Path\n\n# Define paths to store images\nIMAGES_PATH = Path() / \"images\" / \"Classification\"\n# Check if path exists\nIMAGES_PATH.mkdir(parents=True, exist_ok=True)\n\ndef store_fig(fig_id,\n tight_layout=True,\n fig_extension=\"png\",\n resolution=300,\n style=None,\n close=None):\n \n if tight_layout:\n plt.tight_layout()\n \n if style == \"web\":\n plt.grid(which='minor', color='#5b6078', linestyle=':', linewidth=0.5)\n plt.grid(which='major', color='#5b6078', linestyle=':', linewidth=0.8)\n fig_extension = \"png\"\n \n elif style == 'slide':\n grid_settings(style = \"slide\")\n fig_extension = \"pdf\"\n\n path = IMAGES_PATH / f\"{fig_id}.{fig_extension}\"\n plt.savefig(path, format=fig_extension, dpi=resolution)\n\n if close:\n plt.close()"]},{"cell_type":"markdown","id":"1ffdf469-52c4-4031-bb91-21d3d0638bb5","metadata":{},"source":["### MNIST\n\n"]},{"cell_type":"markdown","id":"6069d10f-3754-492e-9135-743a480221f4","metadata":{},"source":["#### Download Initial Data\n\n"]},{"cell_type":"code","execution_count":1,"id":"b340554f-0e45-499f-96f7-ed14f961c362","metadata":{},"outputs":[],"source":["from sklearn.datasets import fetch_openml\n\nmnist = fetch_openml('mnist_784', as_frame=False)"]},{"cell_type":"markdown","id":"257629f6-aad4-4bdc-bf1b-bfdbe9737c23","metadata":{},"source":["For more info on the sklearn.datasets.fetchopenml click [here](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_openml.html).\nHowever for our application two parameters are used.\n\n\n| PARAMETER|DESCRIPTION|\n|---|---|\n| name|String identifier of the dataset. Note that OpenML can have multiple datasets with the same name.|\n| asframe|If True, the data is a pandas DataFrame including columns with appropriate dtypes, if false e data and target will be NumPy arrays and the data will only contain numerical values|\n\n"]},{"cell_type":"code","execution_count":1,"id":"62d476cd-256b-4c4a-bb8d-f4162f8a9338","metadata":{},"outputs":[],"source":["print(mnist.keys())"]},{"cell_type":"code","execution_count":1,"id":"8e0ed918-6a91-434e-934a-ddde4cd1c90d","metadata":{},"outputs":[],"source":["X, y = mnist.data, mnist.target\nprint(X)"]},{"cell_type":"code","execution_count":1,"id":"c8fe76e1-d799-4f80-be94-fc3c1f56e059","metadata":{},"outputs":[],"source":["print(X.shape)"]},{"cell_type":"code","execution_count":1,"id":"cf184e41-784b-4b3f-aaa1-b3676d96f8f6","metadata":{},"outputs":[],"source":["X[0]"]},{"cell_type":"code","execution_count":1,"id":"c953eaf0-a4fb-4e4d-9a08-7a3ed347f4ba","metadata":{},"outputs":[],"source":["import matplotlib.pyplot as plt\n\ndef plot_digit(image_data):\n image = image_data.reshape(28, 28)\n plt.imshow(image, cmap=\"binary\")\n plt.axis(\"off\")\n\nsome_digit = X[0]\nplot_digit(some_digit)\nstore_fig(\"some-digits-plot\",\n close = True)"]},{"cell_type":"code","execution_count":1,"id":"ed02c08c-f11f-4467-b338-668f6573d679","metadata":{},"outputs":[],"source":["plt.figure(figsize=(9, 9))\nfor idx, image_data in enumerate(X[:100]):\n plt.subplot(10, 10, idx + 1)\n plot_digit(image_data)\nplt.subplots_adjust(wspace=0, hspace=0)\n\nstore_fig(\"more-digits-plot\",\n close = True)"]},{"cell_type":"code","execution_count":1,"id":"fbd33566-2c34-4313-8b32-65c613e4d91e","metadata":{},"outputs":[],"source":["X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]"]},{"cell_type":"markdown","id":"79512997-3dc2-4e80-891d-c7881acd1b9a","metadata":{},"source":["#### Training a Binary Classifier\n\n"]},{"cell_type":"code","execution_count":1,"id":"3b43a050-c799-4dba-9ea0-f1835ad49160","metadata":{},"outputs":[],"source":["y_train_5 = (y_train == '5') # True for all 5s, False for all other digits\ny_test_5 = (y_test == '5')"]},{"cell_type":"code","execution_count":1,"id":"110ea320-f39a-48e4-8cd8-c0edc0212c15","metadata":{},"outputs":[],"source":["from sklearn.linear_model import SGDClassifier\n\nsgd_clf = SGDClassifier(random_state=42)\nsgd_clf.fit(X_train, y_train_5)"]},{"cell_type":"code","execution_count":1,"id":"d6096bab-7a53-413c-a736-18ba6655d252","metadata":{},"outputs":[],"source":["sgd_clf.predict([some_digit])"]},{"cell_type":"markdown","id":"4c5a47a2-7ea9-452c-99e7-92c1b98463ee","metadata":{},"source":["### Performance Measures\n\n"]},{"cell_type":"markdown","id":"60516fb8-23f6-40bc-a243-9f53e949d977","metadata":{},"source":["#### Measuring Accuracy Using Cross-Validation\n\n"]},{"cell_type":"code","execution_count":1,"id":"54aeb5ee-cfab-43f1-9757-3b5c59deeb78","metadata":{},"outputs":[],"source":["from sklearn.model_selection import cross_val_score\n\ncross_val_score(sgd_clf, X = X_train,y = y_train_5, cv=3, scoring=\"accuracy\")"]},{"cell_type":"markdown","id":"ba1c54c3-e99c-4ee7-a049-4b94f0316291","metadata":{},"source":["\n| PARAMETER|DESCRIPTION|\n|---|---|\n| estimator|The object to use to fit the data.|\n| X|The data to fit. Can be for example a list, or an array.|\n| y|The target variable to try to predict in the case of supervised learning.|\n| cv|Determines the cross-validation splitting strategy.|\n| scoring|A str (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y) which should return only a single value.|\n\n"]},{"cell_type":"code","execution_count":1,"id":"c146e975-e6ff-442f-b253-4fc656ab45a1","metadata":{},"outputs":[],"source":["from sklearn.model_selection import StratifiedKFold\nfrom sklearn.base import clone\n\nskfolds = StratifiedKFold(n_splits=3) # add shuffle=True if the dataset is not\n # already shuffled\nfor train_index, test_index in skfolds.split(X_train, y_train_5):\n clone_clf = clone(sgd_clf)\n X_train_folds = X_train[train_index]\n y_train_folds = y_train_5[train_index]\n X_test_fold = X_train[test_index]\n y_test_fold = y_train_5[test_index]\n\n clone_clf.fit(X_train_folds, y_train_folds)\n y_pred = clone_clf.predict(X_test_fold)\n n_correct = sum(y_pred == y_test_fold)\n print(n_correct / len(y_pred))"]},{"cell_type":"code","execution_count":1,"id":"4c31fe34-a821-4168-94d3-f20e2e707fbc","metadata":{},"outputs":[],"source":["from sklearn.dummy import DummyClassifier\n\ndummy_clf = DummyClassifier()\ndummy_clf.fit(X_train, y_train_5)\nprint(any(dummy_clf.predict(X_train)))"]},{"cell_type":"code","execution_count":1,"id":"c6eb0611-19a8-428a-b986-d0c073460c68","metadata":{},"outputs":[],"source":["cross_val_score(dummy_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")"]},{"cell_type":"markdown","id":"3247c12a-4c99-4f2d-a12d-c3db2ecb9653","metadata":{},"source":["#### Confusion Matrix\n\n"]},{"cell_type":"code","execution_count":1,"id":"23487611-1853-49c2-b3a3-04cb9230b670","metadata":{},"outputs":[],"source":["from sklearn.model_selection import cross_val_predict\n\ny_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)"]},{"cell_type":"code","execution_count":1,"id":"43ac5ac3-ff8c-4e42-9283-0dcce724e43b","metadata":{},"outputs":[],"source":["from sklearn.metrics import confusion_matrix\n\ncm = confusion_matrix(y_train_5, y_train_pred)\ncm"]},{"cell_type":"code","execution_count":1,"id":"1e991d3b-ea43-4a01-9a32-f6a2ebfb53e7","metadata":{},"outputs":[],"source":["y_train_perfect_predictions = y_train_5 # pretend we reached perfection\nconfusion_matrix(y_train_5, y_train_perfect_predictions)"]},{"cell_type":"markdown","id":"03f0967a-6d7a-47f4-961d-280640e2fcea","metadata":{},"source":["#### Presicion and Recall\n\n"]},{"cell_type":"code","execution_count":1,"id":"94cd7195-92d0-4df9-a83a-8bd1637e10c2","metadata":{},"outputs":[],"source":["from sklearn.metrics import precision_score, recall_score\n\nprecision_score(y_train_5, y_train_pred) # == 3530 / (687 + 3530)"]}],"metadata":{"org":null,"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":"3.5.2"}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file diff --git a/Lecture-Slides/Machine-Learning-Lecture/ipynb/End-to-End-ML-Project.ipynb b/Lecture-Slides/Machine-Learning-Lecture/ipynb/End-to-End-ML-Project.ipynb index 2b88d1c..f21bab2 100644 --- a/Lecture-Slides/Machine-Learning-Lecture/ipynb/End-to-End-ML-Project.ipynb +++ b/Lecture-Slides/Machine-Learning-Lecture/ipynb/End-to-End-ML-Project.ipynb @@ -1 +1,3015 @@ -{"cells":[{"cell_type":"markdown","id":"a09ddb01-96ec-46b9-881e-7ace9e59cb86","metadata":{},"source":"Code for Machine Learning and Data Science II\n=============================================\n\n"},{"cell_type":"markdown","id":"9807bde7-696c-4372-b6b2-cb806daeb121","metadata":{},"source":["These are the code snippets used in End to End ML Project\npart of Machine Learning and Data Science II.\n\n"]},{"cell_type":"markdown","id":"9a201050-bc1e-4bad-82d6-a071acd88b07","metadata":{},"source":["### Introduction\n\n"]},{"cell_type":"markdown","id":"02c51564-628f-4c76-b765-8ef7cb1419b5","metadata":{},"source":["#### Preamble\n\n"]},{"cell_type":"code","execution_count":1,"id":"a572842c-0c47-4157-8594-e454cea09a95","metadata":{},"outputs":[],"source":["import matplotlib.pyplot as plt\nimport ChalcedonPy as cp\n\n# Initialise ChalcedonPy\ncp.init(save_path=\"End-to-End-ML-Project\",\n display_mode=\"slide\")"]},{"cell_type":"markdown","id":"ab42e7d1-cf5c-4093-a6d3-b12d0a2c634d","metadata":{},"source":["#### Download Initial Data\n\n"]},{"cell_type":"markdown","id":"9f915f99-d82e-4ef5-9f9c-3565678ebfea","metadata":{},"source":["First lets load the necessary modules for downloading the data so we\ncan work on it.\n\n"]},{"cell_type":"code","execution_count":1,"id":"51dfa77d-eb8c-4972-a27c-e27b4f456a5e","metadata":{},"outputs":[],"source":["from pathlib import Path\nimport pandas as pd # for dataframes\nimport tarfile # read/write tar files\n# to access and download data from web\nimport urllib.request\nfrom tabulate import tabulate # for table printing"]},{"cell_type":"markdown","id":"6bab24d4-3c98-463f-99bf-a60d2535943d","metadata":{},"source":["Define a function called loadhousingdata() to access and\ndownload the data, finally returning a read of it using pandas.\n\n"]},{"cell_type":"code","execution_count":1,"id":"79bc64a3-4f51-4020-9a5b-8b63868a8a6e","metadata":{},"outputs":[],"source":["def load_housing_data():\n # path to save the file\n tarball_path = Path(\"datasets/housing.tgz\")\n # check if the path exists, if not create one\n\n if not tarball_path.is_file():\n Path(\"datasets\").mkdir(parents=True, exist_ok=True)\n url = \"https://github.com/dTmC0945/L-MCI-BSc-Data-Science-II/raw/main/data/housing.tgz\"\n urllib.request.urlretrieve(url, tarball_path)\n with tarfile.open(tarball_path) as housing_tarball:\n housing_tarball.extractall(path=\"datasets\")\n return pd.read_csv(Path(\"datasets/housing/housing.csv\"))"]},{"cell_type":"markdown","id":"5d7935d1-f02a-4937-b6c7-72042925ea55","metadata":{},"source":["Now read the data and assign it to the value housing.\n\n"]},{"cell_type":"code","execution_count":1,"id":"60df4167-7d6b-4b7a-83ac-43f63b30adbb","metadata":{},"outputs":[],"source":["housing = load_housing_data()"]},{"cell_type":"markdown","id":"4eeb321a-b4da-44dd-8f59-5e67aa73b0dc","metadata":{},"source":["Let's have a look at the data\n\n"]},{"cell_type":"code","execution_count":1,"id":"854d9470-cf84-48a0-8844-914b1f3f7390","metadata":{},"outputs":[],"source":["print(housing.head())"]},{"cell_type":"markdown","id":"40aef187-9215-4b09-932e-da8ca7e7fea5","metadata":{},"source":["Each row represents one district.\n\nThere are 10 attributes\n\n- longitude,\n- latitude,\n- housingmedianage,\n- totalrooms,\n- totalbed rooms,\n- population,\n- households,\n- medianincome,\n- medianhousevalue,\n- and oceanproximity.\n\nTo get more information about the data use the info() method.\n\n"]},{"cell_type":"code","execution_count":1,"id":"9a445f72-cb19-4ef9-b761-934ecafe65c1","metadata":{},"outputs":[],"source":["print(housing.info())"]},{"cell_type":"markdown","id":"69afa7ac-4542-4e50-aaa9-7ceef2267a46","metadata":{},"source":["There seems to be some repetition on the oceanproximity parameters.\nLet's have a bit more look into the data.\n\nTo get more information on it use the valuecounts() method.\n\n"]},{"cell_type":"code","execution_count":1,"id":"344d439c-e893-4fd3-9114-2910b32fd6d1","metadata":{},"outputs":[],"source":["print(housing[\"ocean_proximity\"].value_counts())"]},{"cell_type":"markdown","id":"012323dd-000e-43ab-9d0d-c30160461c60","metadata":{},"source":["Time to look at the the other fields using the describe() method which\nshows their numerical attributes.\n\n"]},{"cell_type":"code","execution_count":1,"id":"1527e44a-7bf7-4987-a0c4-611c9fc2c9fe","metadata":{},"outputs":[],"source":["print(housing.describe)"]},{"cell_type":"markdown","id":"e712a87e-44d4-47a7-bf45-64e68496b59e","metadata":{},"source":["Allows the pretty print of the results.\n\n"]},{"cell_type":"code","execution_count":1,"id":"3387fb2e-62d6-4adc-b32b-dc04194905fb","metadata":{},"outputs":[],"source":["import matplotlib.pyplot as plt\n\nhousing.hist(bins=50, figsize=(12, 8))\n\nplt.show()"]},{"cell_type":"markdown","id":"b84aba26-6038-4e9e-9ea7-9f1a1aaa7b70","metadata":{},"source":["#### Create a Set for Testing\n\n"]},{"cell_type":"code","execution_count":1,"id":"7bbcb7fe-560a-4024-bab0-f04361f808cf","metadata":{},"outputs":[],"source":["import numpy as np\n\n# Define a function to shuffle and split data\ndef shuffle_and_split_data(data, test_ratio):\n shuffled_indices = np.random.permutation(len(data))\n test_set_size = int(len(data) * test_ratio)\n test_indices = shuffled_indices[:test_set_size]\n train_indices = shuffled_indices[test_set_size:]\n return data.iloc[train_indices], data.iloc[test_indices]"]},{"cell_type":"markdown","id":"66f7b878-6ee3-4c60-838d-c7150aba1687","metadata":{},"source":["To use the data we can do the following\n\n"]},{"cell_type":"code","execution_count":1,"id":"0cfab345-57ab-4271-8a8c-53cca85a11ec","metadata":{},"outputs":[],"source":["train_set, test_set = shuffle_and_split_data(housing, 0.2)"]},{"cell_type":"markdown","id":"3ec7dfa9-d57e-48cb-9928-076d734a766d","metadata":{},"source":["Lets see the sizes of the datasets\n\n"]},{"cell_type":"code","execution_count":1,"id":"7d080038-8fcc-4c93-ae23-2404cb402cc3","metadata":{},"outputs":[],"source":["print(\"The size of the training set is:\", len(train_set))\nprint(\"The size of the test data is:\", len(test_set))"]},{"cell_type":"markdown","id":"f09b2630-1f49-435c-993c-535814f5ece3","metadata":{},"source":["This will shuffle but because of shuffling the program will see\nall the data eventually which is not something good.\n\nTo avoid it we set a RNG seed to keep the shuffled indices constant.\n\n"]},{"cell_type":"code","execution_count":1,"id":"a285ac59-8b35-453f-b256-0d0937dbb046","metadata":{},"outputs":[],"source":["np.random.seed(42)"]},{"cell_type":"markdown","id":"c5a1ca98-7239-4f55-86d0-c851a280c36d","metadata":{},"source":["Here is another method in which we can keep the split constant even if\nthe dataset is refreshed.\n\n"]},{"cell_type":"code","execution_count":1,"id":"d7941262-bb4a-4edb-9b19-a72cfc128321","metadata":{},"outputs":[],"source":["from zlib import crc32\n\ndef is_id_in_test_set(identifier, test_ratio):\n return crc32(np.int64(identifier)) < test_ratio * 2**32\n\ndef split_data_with_id_hash(data, test_ratio, id_column):\n ids = data[id_column]\n in_test_set = ids.apply(\n lambda id_: is_id_in_test_set(id_, test_ratio))\n return data.loc[~in_test_set], data.loc[in_test_set]"]},{"cell_type":"markdown","id":"d321d355-41b4-406d-b0b3-61b8bcf20dd8","metadata":{},"source":["Unfortunately, the housing dataset does not have an identifier column.\nThe simplest solution is to use the row index as the ID:\n\n"]},{"cell_type":"code","execution_count":1,"id":"30843266-7afe-4204-b530-76a31a108d3a","metadata":{},"outputs":[],"source":["housing_with_id = housing.reset_index() # adds an `index` column\ntrain_set, test_set = split_data_with_id_hash(housing_with_id, 0.2, \"index\")"]},{"cell_type":"markdown","id":"04f92264-8f80-4f7c-8921-2dbdca85f9d8","metadata":{},"source":["If you use the row index as a unique identifier, you need to make\nsure that new data gets appended to the end of the dataset and that no row ever gets deleted.\n\n"]},{"cell_type":"code","execution_count":1,"id":"ed49b3ac-27d1-4b95-b9b5-6aa109bdefc6","metadata":{},"outputs":[],"source":["housing_with_id[\"id\"] = housing[\"longitude\"] * 1000 + housing[\"latitude\"]\ntrain_set, test_set = split_data_with_id_hash(housing_with_id, 0.2, \"id\")"]},{"cell_type":"markdown","id":"544a3dba-2156-48f0-8f01-cefa32ffa726","metadata":{},"source":["Scikit-Learn provides a few functions to split datasets into multiple subsets in various ways.\nAn easy function is traintestsplit()\n\n"]},{"cell_type":"code","execution_count":1,"id":"b33932c3-38e0-4089-bb2c-3bc9baff0982","metadata":{},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n\ntrain_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)"]},{"cell_type":"markdown","id":"a914ea35-1722-4d3d-a54f-eafb9482678e","metadata":{},"source":["To find the probability that a random sample of 1,000 people contains less than 48.5% female or\nmore than 53.5% female when the population's female ratio is 51.1%, we use the binomial distribution.\n\nThe cdf() method of the binomial distribution gives us the probability that the\nnumber of females will be equal or less than the given value.\n\n"]},{"cell_type":"code","execution_count":1,"id":"a94577e1-3194-43e7-a1bf-003badfd4044","metadata":{},"outputs":[],"source":["from scipy.stats import binom\n\nsample_size = 1000\nratio_female = 0.511\nproba_too_small = binom(sample_size, ratio_female).cdf(485 - 1)\nproba_too_large = 1 - binom(sample_size, ratio_female).cdf(535)\nprint(proba_too_small + proba_too_large)"]},{"cell_type":"markdown","id":"f989f5cb-00c3-41cc-9c06-62c21aa16f3d","metadata":{},"source":["However, for the ones who prefer numerical results over explicit solutions,\nthere is also the below method to achieve a similar result.\n\n"]},{"cell_type":"code","execution_count":1,"id":"39d7b95e-8bc7-4deb-9b4c-2bcaa5dd1b9c","metadata":{},"outputs":[],"source":["np.random.seed(42)\n\nsamples = (np.random.rand(100_000, sample_size) < ratio_female).sum(axis=1)\n((samples < 485) | (samples > 535)).mean()"]},{"cell_type":"code","execution_count":1,"id":"7f5bedbf-7bca-44ee-b0cb-3bf4d08ea757","metadata":{},"outputs":[],"source":["housing[\"income_cat\"] = pd.cut(housing[\"median_income\"],\n bins=[0., 1.5, 3.0, 4.5, 6., np.inf],\n labels=[1, 2, 3, 4, 5])"]},{"cell_type":"code","execution_count":1,"id":"ab2462b4-0da8-4270-a382-798f9997a650","metadata":{},"outputs":[],"source":["plt.xlabel(\"Income category\")\nplt.ylabel(\"Number of districts\")\nhousing[\"income_cat\"].value_counts().sort_index().plot.bar(rot=0, grid=True)\n\nplt.show()"]},{"cell_type":"markdown","id":"32aecc3f-d478-4225-8299-65973d36e2a1","metadata":{},"source":["Now you are ready to do stratified sampling based on the income category.\nFor this you can use Scikit-Learn’s StratifiedShuffleSplit class:\n\n"]},{"cell_type":"code","execution_count":1,"id":"4b5ca3da-2a23-4b22-b7ec-b356f7f1f4a6","metadata":{},"outputs":[],"source":["from sklearn.model_selection import StratifiedShuffleSplit\n\nsplitter = StratifiedShuffleSplit(n_splits=10, test_size=0.2, random_state=42)\nstrat_splits = []\nfor train_index, test_index in splitter.split(housing, housing[\"income_cat\"]):\n strat_train_set_n = housing.iloc[train_index]\n strat_test_set_n = housing.iloc[test_index]\n strat_splits.append([strat_train_set_n, strat_test_set_n])"]},{"cell_type":"code","execution_count":1,"id":"5355e4ad-9968-4970-9108-27e3c7f9b717","metadata":{},"outputs":[],"source":["strat_train_set, strat_test_set = strat_splits[0]"]},{"cell_type":"code","execution_count":1,"id":"33c7878e-0c03-456e-9345-d0582f26020a","metadata":{},"outputs":[],"source":["strat_train_set, strat_test_set = train_test_split(\n housing,\n test_size=0.2,\n stratify=housing[\"income_cat\"],\n random_state=42)"]},{"cell_type":"code","execution_count":1,"id":"209ac8bd-a37a-43e2-80fe-cc7c24feca11","metadata":{},"outputs":[],"source":["strat_test_set[\"income_cat\"].value_counts() / len(strat_test_set)"]},{"cell_type":"code","execution_count":1,"id":"543978b8-b7d3-4dae-9308-7f1e68326ac7","metadata":{},"outputs":[],"source":["def income_cat_proportions(data):\n return data[\"income_cat\"].value_counts() / len(data)\n\ntrain_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n\ncompare_props = pd.DataFrame({\n \"Overall %\": income_cat_proportions(housing),\n \"Stratified %\": income_cat_proportions(strat_test_set),\n \"Random %\": income_cat_proportions(test_set),\n}).sort_index()\ncompare_props.index.name = \"Income Category\"\ncompare_props[\"Strat. Error %\"] = (compare_props[\"Stratified %\"] /\n compare_props[\"Overall %\"] - 1)\ncompare_props[\"Rand. Error %\"] = (compare_props[\"Random %\"] /\n compare_props[\"Overall %\"] - 1)\n(compare_props * 100).round(2)"]},{"cell_type":"markdown","id":"063cb0e1-9c0b-47f5-950d-af5b747429e6","metadata":{},"source":["Time to drop incomecat() attribute to go back to the\noriginal state.\n\n"]},{"cell_type":"code","execution_count":1,"id":"233c911d-3ca4-40ed-809e-51b3edda3680","metadata":{},"outputs":[],"source":["for set_ in (strat_train_set, strat_test_set):\n set_.drop(\"income_cat\", axis=1, inplace=True)"]},{"cell_type":"markdown","id":"33b1504a-4b7e-4c6e-bca1-936c6e3fde38","metadata":{},"source":["### Discover and Visualize the Data to Gain Insights\n\n"]},{"cell_type":"markdown","id":"9d20829c-72a6-4506-b85d-59c50f81a811","metadata":{},"source":["Before we start playing with the data it is a good habit to\ncreate a copy so as to not tamper with the training set.\n\n"]},{"cell_type":"code","execution_count":1,"id":"9864e03b-bec7-476b-801a-c9ea4c3d95b8","metadata":{},"outputs":[],"source":["housing = strat_train_set.copy()"]},{"cell_type":"markdown","id":"9f0eaaf3-bfd9-4a1a-974c-1694f8cddbad","metadata":{},"source":["#### Visualising Geographical Data\n\n"]},{"cell_type":"markdown","id":"9dc1ffb3-8ece-4a71-8cc8-647335f14663","metadata":{},"source":["As data is a bunch of points in 2D space, it is benefical to\nplot it in a scatter plot.\n\n"]},{"cell_type":"code","execution_count":1,"id":"bf08ffe4-4638-495f-80eb-f474f8e5d56e","metadata":{},"outputs":[],"source":["housing.plot(kind = \"scatter\",\n x = \"longitude\",\n y = \"latitude\",\n title = \"Housing Market in California\")\n\nplt.show()"]},{"cell_type":"markdown","id":"ca3aaa28-9309-484d-ad5b-6fa2435df4aa","metadata":{},"source":["This might remind you a state in a country but it is currently not\npossible to see a pattern.\n\nLet's set the alpha to 0.2 to see if it helps better.\n\n"]},{"cell_type":"code","execution_count":1,"id":"d967e640-fc64-4e08-ad4a-ce7db40914c7","metadata":{},"outputs":[],"source":["housing.plot(kind = \"scatter\",\n x = \"longitude\",\n y = \"latitude\",\n title = r\"Housing Market in California ($\\alpha = 0.2$)\",\n alpha = 0.2)\n\nplt.show()"]},{"cell_type":"markdown","id":"619e83f5-88da-4246-b383-9b0537c2853f","metadata":{},"source":["Let's make it a bit more interesting and create a plot where the higher cluster areas\nwould be coloured red and sparser places will be coloured colder as the value of\nthe land is less.\n\n"]},{"cell_type":"code","execution_count":1,"id":"f522c2f2-5712-43f9-a9b0-0e53d3515e9d","metadata":{},"outputs":[],"source":["housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\",\n s=housing[\"population\"] / 100, label=\"population\",\n c=\"median_house_value\", cmap=\"jet\", colorbar=True,\n legend=True, sharex=False, figsize=(10, 7))\n\nplt.show()"]},{"cell_type":"markdown","id":"2e6829b5-7fcb-4a85-b283-7376b88dc7cc","metadata":{},"source":["Now we can see the housing prices are very much related to the location\nin this case closer to the ocean. To add a final addition let's superimpose\nthe state map over it.\n\n"]},{"cell_type":"code","execution_count":1,"id":"a01a4828-828d-40b2-979c-c98b365f08f7","metadata":{},"outputs":[],"source":["filename = \"california.png\"\n\nif not (IMAGES_PATH / filename).is_file():\n homl3_root = \"https://github.com/ageron/handson-ml3/raw/main/\"\n url = homl3_root + \"images/end_to_end_project/\" + filename\n print(\"Downloading\", filename)\n urllib.request.urlretrieve(url, IMAGES_PATH / filename)\n\nhousing_renamed = housing.rename(columns={\n \"latitude\": \"Latitude\", \"longitude\": \"Longitude\",\n \"population\": \"Population\",\n \"median_house_value\": \"Median house value (ᴜsᴅ)\"})\n\nplot_settings(style = \"slide\")\nhousing_renamed.plot(\n kind=\"scatter\", x=\"Longitude\", y=\"Latitude\",\n s=housing_renamed[\"Population\"] / 100, label=\"Population\",\n c=\"Median house value (ᴜsᴅ)\", cmap=\"jet\", colorbar=True,\n legend=True, sharex=False, figsize=(10, 7))\n\ncalifornia_img = plt.imread(IMAGES_PATH / filename)\naxis = -124.55, -113.95, 32.45, 42.05\nplt.axis(axis)\nplt.imshow(california_img, extent=axis)\n\nstore_fig(\"california-housing-prices-plot\",\n style = \"slide\",\n close = True)"]},{"cell_type":"markdown","id":"b5180c09-48e7-4a35-9669-e9c8c46d602a","metadata":{},"source":["#### Looking for Correlations\n\n"]},{"cell_type":"markdown","id":"e956b962-a15e-4275-8e87-0e8d8cd6380c","metadata":{},"source":["Time to see if there is any correlation between the value of the houses\nand other parameters using pearsons correlation\n\n"]},{"cell_type":"code","execution_count":1,"id":"41f3260e-e134-46dd-b9f8-89d1489cb085","metadata":{},"outputs":[],"source":["corr_matrix = housing.corr(numeric_only=True)\ncorr_matrix[\"median_house_value\"].sort_values(ascending=False)"]},{"cell_type":"markdown","id":"e8643f6b-f88d-4c95-9b5f-a49f460c07e6","metadata":{},"source":["Another way to check for correlation between attributes is to\nuse the pandas scattermatrix() function,\n\n"]},{"cell_type":"code","execution_count":1,"id":"3b1a4a20-842c-4ad9-ad80-c72dfd5f32fe","metadata":{},"outputs":[],"source":["from pandas.plotting import scatter_matrix\n\nattributes = [\"median_house_value\", \"median_income\", \"total_rooms\",\n \"housing_median_age\"]\n\nscatter_matrix(housing[attributes], figsize=(12, 8))\nplt.show()"]},{"cell_type":"markdown","id":"e71614aa-ed9f-4c1b-acb6-f5d81c2214ba","metadata":{},"source":["The most promising attribute to predict the median house value\nis the median income,\n\n"]},{"cell_type":"code","execution_count":1,"id":"7b6c2696-29f6-40b0-b704-8f0b53ecf3ed","metadata":{},"outputs":[],"source":["housing.plot(kind=\"scatter\", x=\"median_income\", y=\"median_house_value\",\n alpha=0.1, grid=True)\nplt.show()"]},{"cell_type":"markdown","id":"8c90240c-d179-4c18-b61d-65403bc13bf2","metadata":{},"source":["#### Experimenting with Attribute Combinations\n\n"]},{"cell_type":"code","execution_count":1,"id":"a741331f-4897-4c83-80d6-a56349bdbae2","metadata":{},"outputs":[],"source":["housing[\"rooms_per_house\"] = housing[\"total_rooms\"] / housing[\"households\"]\nhousing[\"bedrooms_ratio\"] = housing[\"total_bedrooms\"] / housing[\"total_rooms\"]\nhousing[\"people_per_house\"] = housing[\"population\"] / housing[\"households\"]"]},{"cell_type":"code","execution_count":1,"id":"1cf72e63-3bfb-405d-8599-fc0ea463a942","metadata":{},"outputs":[],"source":["corr_matrix = housing.corr(numeric_only=True)\ncorr_matrix[\"median_house_value\"].sort_values(ascending=False)"]},{"cell_type":"markdown","id":"d709cab0-feee-4200-91d3-34ff500de3ed","metadata":{},"source":["### Prepare the Data for Machine Learning Algorithms\n\n"]},{"cell_type":"markdown","id":"2ce7e023-142d-41a8-b853-89c4d55fd428","metadata":{},"source":["It’s time to prepare the data for your Machine Learning algorithms.\nInstead of doing this manually, you should write functions for this\npurpose, \n\nFirst revert to a clean training set and separate predictors and labels.\n\n"]},{"cell_type":"code","execution_count":1,"id":"79e5c576-d5ec-4f3a-bd91-fc51d31923d4","metadata":{},"outputs":[],"source":["housing = strat_train_set.drop(\"median_house_value\", axis=1)\nhousing_labels = strat_train_set[\"median_house_value\"].copy()"]},{"cell_type":"markdown","id":"66be89a5-f458-4e2a-bd32-1f5e6f884b18","metadata":{},"source":["#### Data Cleaning\n\n"]},{"cell_type":"markdown","id":"9ea967d8-ac7d-4f58-8c49-d03736902082","metadata":{},"source":["Not all data comes perfect for ML as some of them may contain NaN or 0 or\nsome other value which you don't want ML algorithm to process.\n\n"]},{"cell_type":"code","execution_count":1,"id":"f70d559d-1f63-4645-bb59-b98f015a2702","metadata":{},"outputs":[],"source":["null_rows_idx = housing.isnull().any(axis=1)\nhousing.loc[null_rows_idx].head()"]},{"cell_type":"markdown","id":"1477ebc7-8a75-4ab7-b0c2-8483c9938add","metadata":{},"source":["Let's look at three (3) options:\n\n1 - Get rid of the corresponding districts\n\n"]},{"cell_type":"code","execution_count":1,"id":"e5031687-97e8-4675-a859-64e9d21fc95d","metadata":{},"outputs":[],"source":["housing_option1 = housing.copy()\nhousing_option1.dropna(subset=[\"total_bedrooms\"], inplace=True) # option 1\nhousing_option1.loc[null_rows_idx].head()"]},{"cell_type":"markdown","id":"8546481f-e20f-4b4c-a4f0-a534025e8172","metadata":{},"source":["2 - Get rid of the whole attribute\n\n"]},{"cell_type":"code","execution_count":1,"id":"fad8635e-531c-4504-8645-dee201547f00","metadata":{},"outputs":[],"source":["housing_option2 = housing.copy()\nhousing_option2.drop(\"total_bedrooms\", axis=1, inplace=True) # option 2\nhousing_option2.loc[null_rows_idx].head()"]},{"cell_type":"markdown","id":"94f072c4-4ed0-417c-a1a3-08fecef06e0a","metadata":{},"source":["3 - Set the values to some value (zero, the mean, the median, etc.).\n\n"]},{"cell_type":"code","execution_count":1,"id":"8ee62839-7353-432e-b801-8050d4b3fcbd","metadata":{},"outputs":[],"source":["housing_option3 = housing.copy()\nmedian = housing[\"total_bedrooms\"].median()\nhousing_option3[\"total_bedrooms\"].fillna(median, inplace=True) # option 3\n\nhousing_option3.loc[null_rows_idx].head()"]},{"cell_type":"markdown","id":"971e85ff-6f75-4f8a-a2c1-02a8931a93b1","metadata":{},"source":["Scikit-Learn provides a handy class to take care of missing values: SimpleImputer.\n\nSeparating out the numerical attributes to use the \"median\" strategy\n\n"]},{"cell_type":"code","execution_count":1,"id":"d59219be-5b14-4ade-95e8-0d0b5f47844d","metadata":{},"outputs":[],"source":["from sklearn.impute import SimpleImputer\n\nimputer = SimpleImputer(strategy=\"median\")"]},{"cell_type":"markdown","id":"2ad4d7fc-d009-445f-8c34-d0ee67f0dbc0","metadata":{},"source":["create a copy of the data without the text attribute oceanproximity:\n\n"]},{"cell_type":"code","execution_count":1,"id":"8e026b51-750a-4edd-999a-014fd466b750","metadata":{},"outputs":[],"source":["housing_num = housing.select_dtypes(include=[np.number])"]},{"cell_type":"markdown","id":"e4ca43fe-f340-40c7-b364-4489252cd429","metadata":{},"source":["can fit the imputer instance to the training data using the fit() method:\n\n"]},{"cell_type":"code","execution_count":1,"id":"e8327d4e-d2f5-4c4b-b381-55a4bfe358f1","metadata":{},"outputs":[],"source":["imputer.fit(housing_num)"]},{"cell_type":"markdown","id":"2d2dfbf3-ed67-481d-8609-b783e9c2e5a7","metadata":{},"source":["we cannot be sure that there won’t be any missing values in new\ndata after the system goes live, so it is safer to apply the\nimputer to all the numerical attributes:\n\n"]},{"cell_type":"code","execution_count":1,"id":"8b638d41-d4b9-44ce-8790-9544c83c4d9e","metadata":{},"outputs":[],"source":["print(imputer.statistics_)"]},{"cell_type":"code","execution_count":1,"id":"f104b66a-a005-4aa5-acc4-d9638443d4c9","metadata":{},"outputs":[],"source":["print(housing_num.median().values)"]},{"cell_type":"markdown","id":"afd6e8e0-21a2-4e2f-b991-3f97038be8b4","metadata":{},"source":["use this “trained” imputer to transform the training set by\nreplacing missing values with the learned medians:\n\n"]},{"cell_type":"code","execution_count":1,"id":"eede1193-54d3-4894-9173-52331abbaccd","metadata":{},"outputs":[],"source":["X = imputer.transform(housing_num)\n# to see the name of the columns\nprint(imputer.feature_names_in_)"]},{"cell_type":"markdown","id":"8377c4b9-82b6-4709-835e-20e71f954bf0","metadata":{},"source":["To convert this to pd dataframe:\n\n"]},{"cell_type":"code","execution_count":1,"id":"35926455-4039-4e3a-a05a-0c3a0a394dab","metadata":{},"outputs":[],"source":["housing_tr = pd.DataFrame(X, columns=housing_num.columns,\n index=housing_num.index)"]},{"cell_type":"code","execution_count":1,"id":"7f9c9605-b99a-48bf-9081-29a21fdb41d2","metadata":{},"outputs":[],"source":["housing_tr.loc[null_rows_idx].head()"]},{"cell_type":"code","execution_count":1,"id":"a201fe39-4468-47db-989f-6af1b5cb1447","metadata":{},"outputs":[],"source":["imputer.strategy"]},{"cell_type":"code","execution_count":1,"id":"dae0bd75-7d8d-49a7-a63f-83d0d5167fbb","metadata":{},"outputs":[],"source":["housing_tr = pd.DataFrame(X, columns=housing_num.columns,\n index=housing_num.index)\n\nhousing_tr.loc[null_rows_idx].head()"]},{"cell_type":"markdown","id":"34161f59-0cf7-4895-85b5-69b31747e2ba","metadata":{},"source":["Time to drop some outliers.\n\n"]},{"cell_type":"code","execution_count":1,"id":"d1e1fbc6-61d9-4305-a7b5-a079fb4d222a","metadata":{},"outputs":[],"source":["from sklearn.ensemble import IsolationForest\n\nisolation_forest = IsolationForest(random_state=42)\noutlier_pred = isolation_forest.fit_predict(X)\n\nprint(outlier_pred)"]},{"cell_type":"markdown","id":"a043eac3-4da5-430b-963b-4f317970d82b","metadata":{},"source":["#### Handling Text and Categorical Attributes\n\n"]},{"cell_type":"markdown","id":"868b4670-71da-493a-ad1d-0c45f2f6ae1e","metadata":{},"source":["Time to process text instead of numbers.\n\nIn this dataset, there is just one: the oceanproximity attribute.\nLet’s look at its value for the first 10 instances:\n\n"]},{"cell_type":"code","execution_count":1,"id":"c382e678-9186-4d60-8b0a-5d1a0d3878bc","metadata":{},"outputs":[],"source":["housing_cat = housing[[\"ocean_proximity\"]]\nhousing_cat.head(8)"]},{"cell_type":"markdown","id":"776e1ac4-3b25-4df3-96b2-0eb760c2ad15","metadata":{},"source":["As ML likes numbers instead of text, convert these values to numbers\nusing OrdinalEncoder class from Scikit-learn\n\n"]},{"cell_type":"code","execution_count":1,"id":"b8e605ff-0f14-4c61-b945-643294f2c178","metadata":{},"outputs":[],"source":["from sklearn.preprocessing import OrdinalEncoder\n\nordinal_encoder = OrdinalEncoder()\nhousing_cat_encoded = ordinal_encoder.fit_transform(housing_cat)"]},{"cell_type":"markdown","id":"b5554519-18fe-4747-a7d0-2bf905ba7d7b","metadata":{},"source":["Let's see the results\n\n"]},{"cell_type":"code","execution_count":1,"id":"69a577da-2ef3-4125-8e90-2409b08472c1","metadata":{},"outputs":[],"source":["print(housing_cat_encoded[:8])"]},{"cell_type":"markdown","id":"21b9dc90-95cc-4b31-b5b1-debe25bf09fd","metadata":{},"source":["And if we were to see the categories:\n\n"]},{"cell_type":"code","execution_count":1,"id":"fbdaabb5-5f4b-435b-b56b-bcb017621255","metadata":{},"outputs":[],"source":["print(ordinal_encoder.categories_)"]},{"cell_type":"markdown","id":"e78a4336-e0ed-4077-aa7c-8db3e21592a7","metadata":{},"source":["To stop from ML algorithm from treating the numbers too literally\n\n"]},{"cell_type":"code","execution_count":1,"id":"adc3cf15-d02e-4d2c-a81e-8d2a09590606","metadata":{},"outputs":[],"source":["from sklearn.preprocessing import OneHotEncoder\n\ncat_encoder = OneHotEncoder()\nhousing_cat_1hot = cat_encoder.fit_transform(housing_cat)"]},{"cell_type":"code","execution_count":1,"id":"28bf833b-868d-4226-954d-a098aac5bf56","metadata":{},"outputs":[],"source":["type(housing_cat_1hot)"]},{"cell_type":"markdown","id":"5409726c-b8c4-4f90-8f83-a276c3fe9010","metadata":{},"source":["the output is a SciPy sparse matrix, instead of a NumPy array.\n\nconvert it to a dense array if needed by calling the toarray() method:\n\n"]},{"cell_type":"code","execution_count":1,"id":"15d076f8-19fe-42a4-8d14-337a638997bc","metadata":{},"outputs":[],"source":["print(housing_cat_1hot.toarray())"]},{"cell_type":"markdown","id":"f5974873-4982-4e5a-a887-7c9a711e850e","metadata":{},"source":["It is also possible to list categories using categoreis\\_ instance\n\n"]},{"cell_type":"code","execution_count":1,"id":"d8cbbcae-249d-48c0-8ef8-c4384bba1e6b","metadata":{},"outputs":[],"source":["cat_encoder = OneHotEncoder(sparse_output=False)\nhousing_cat_1hot = cat_encoder.fit_transform(housing_cat)\nprint(housing_cat_1hot)"]},{"cell_type":"code","execution_count":1,"id":"fcb65ef9-8117-4bb9-af92-c5a2549f3b20","metadata":{},"outputs":[],"source":["print(cat_encoder.categories_)"]},{"cell_type":"code","execution_count":1,"id":"a4fb09de-a5ef-4ca2-8e2c-f7cdc668f82e","metadata":{},"outputs":[],"source":["df_test = pd.DataFrame({\"ocean_proximity\": [\"INLAND\", \"NEAR BAY\"]})\npd.get_dummies(df_test)"]},{"cell_type":"code","execution_count":1,"id":"16e33000-ce4c-4540-acbf-d4766a8cd586","metadata":{},"outputs":[],"source":["print(cat_encoder.transform(df_test))"]},{"cell_type":"code","execution_count":1,"id":"f18bd498-a402-461c-b428-ec61f3c2b5b4","metadata":{},"outputs":[],"source":["df_test_unknown = pd.DataFrame({\"ocean_proximity\": [\"<2H OCEAN\", \"ISLAND\"]})\npd.get_dummies(df_test_unknown)"]},{"cell_type":"code","execution_count":1,"id":"c5775f95-255e-45dd-8b2b-c40d12a9d366","metadata":{},"outputs":[],"source":["cat_encoder.handle_unknown = \"ignore\"\nprint(cat_encoder.transform(df_test_unknown))"]},{"cell_type":"code","execution_count":1,"id":"a6fa57a8-97c8-4ded-931a-da5e0e30ea84","metadata":{},"outputs":[],"source":["print(cat_encoder.feature_names_in_)"]},{"cell_type":"code","execution_count":1,"id":"c293314a-542f-46a3-adb9-4868a85fff5b","metadata":{},"outputs":[],"source":["print(cat_encoder.get_feature_names_out())"]},{"cell_type":"code","execution_count":1,"id":"305f01a5-f6d7-4a7a-a8c8-9ea5780580e3","metadata":{},"outputs":[],"source":["df_output = pd.DataFrame(cat_encoder.transform(df_test_unknown),\n columns=cat_encoder.get_feature_names_out(),\n index=df_test_unknown.index)\n\ndf_output"]},{"cell_type":"markdown","id":"786f97cc-0d9d-4c4f-af44-561a2d7f3a18","metadata":{},"source":["#### Feature Scaling\n\n"]},{"cell_type":"code","execution_count":1,"id":"2800089c-62bd-4524-801e-2c2270d31b9e","metadata":{},"outputs":[],"source":["from sklearn.preprocessing import MinMaxScaler\n\nmin_max_scaler = MinMaxScaler(feature_range=(-1, 1))\nhousing_num_min_max_scaled = min_max_scaler.fit_transform(housing_num)"]},{"cell_type":"code","execution_count":1,"id":"aadf51c3-6d72-4e09-8290-62689cb5bdc5","metadata":{},"outputs":[],"source":["from sklearn.preprocessing import StandardScaler\n\nstd_scaler = StandardScaler()\nhousing_num_std_scaled = std_scaler.fit_transform(housing_num)"]},{"cell_type":"code","execution_count":1,"id":"0d62804b-d0f8-47fd-a3ed-d7e9d562e57c","metadata":{},"outputs":[],"source":["fig, axs = plt.subplots(1, 2, figsize=(8, 3), sharey=True)\nhousing[\"population\"].hist(ax=axs[0], bins=50)\nhousing[\"population\"].apply(np.log).hist(ax=axs[1], bins=50)\naxs[0].set_xlabel(\"Population\")\naxs[1].set_xlabel(\"Log of population\")\naxs[0].set_ylabel(\"Number of districts\")\nplt.show()"]},{"cell_type":"code","execution_count":1,"id":"8c58a3e9-103d-46c4-9e5a-1e8cdd859ba2","metadata":{},"outputs":[],"source":["percentiles = [np.percentile(housing[\"median_income\"], p)\n for p in range(1, 100)]\nflattened_median_income = pd.cut(housing[\"median_income\"],\n bins=[-np.inf] + percentiles + [np.inf],\n labels=range(1, 100 + 1))\nflattened_median_income.hist(bins=50)\nplt.xlabel(\"Median income percentile\")\nplt.ylabel(\"Number of districts\")\nplt.show()"]},{"cell_type":"code","execution_count":1,"id":"a79c6c63-4e48-48b0-9a1f-7a2182a3f787","metadata":{},"outputs":[],"source":["from sklearn.metrics.pairwise import rbf_kernel\n\nage_simil_35 = rbf_kernel(housing[[\"housing_median_age\"]], [[35]], gamma=0.1)"]},{"cell_type":"code","execution_count":1,"id":"0df04895-d875-4f33-91de-d03e0e2cef41","metadata":{},"outputs":[],"source":["ages = np.linspace(housing[\"housing_median_age\"].min(),\n housing[\"housing_median_age\"].max(),\n 500).reshape(-1, 1)\ngamma1 = 0.1\ngamma2 = 0.03\nrbf1 = rbf_kernel(ages, [[35]], gamma=gamma1)\nrbf2 = rbf_kernel(ages, [[35]], gamma=gamma2)\n\nfig, ax1 = plt.subplots()\n\nax1.set_xlabel(\"Housing median age\")\nax1.set_ylabel(\"Number of districts\")\nax1.hist(housing[\"housing_median_age\"], bins=50)\n\nax2 = ax1.twinx() # create a twin axis that shares the same x-axis\ncolor = \"#984ea3\"\nax2.plot(ages, rbf1, color=color, label=\"gamma = 0.10\")\nax2.plot(ages, rbf2, color=color, label=\"gamma = 0.03\", linestyle=\"--\")\nax2.tick_params(axis='y', labelcolor=color)\nax2.set_ylabel(\"Age similarity\", color=color)\n\nplt.legend(loc=\"upper left\")\nplt.show()"]},{"cell_type":"code","execution_count":1,"id":"de7af0e5-b6fd-4d99-acef-4dc37e137d03","metadata":{},"outputs":[],"source":["from sklearn.linear_model import LinearRegression\n\ntarget_scaler = StandardScaler()\nscaled_labels = target_scaler.fit_transform(housing_labels.to_frame())\n\nmodel = LinearRegression()\nmodel.fit(housing[[\"median_income\"]], scaled_labels)\nsome_new_data = housing[[\"median_income\"]].iloc[:5] # pretend this is new data\n\nscaled_predictions = model.predict(some_new_data)\npredictions = target_scaler.inverse_transform(scaled_predictions)"]},{"cell_type":"code","execution_count":1,"id":"fa1fc2b5-5fa6-4f92-ad08-0b920ec50083","metadata":{},"outputs":[],"source":["print(predictions)"]},{"cell_type":"code","execution_count":1,"id":"c5f966b7-7d97-411f-a77e-3bae14958143","metadata":{},"outputs":[],"source":["from sklearn.compose import TransformedTargetRegressor\n\nmodel = TransformedTargetRegressor(LinearRegression(),\n transformer=StandardScaler())\nmodel.fit(housing[[\"median_income\"]], housing_labels)\npredictions = model.predict(some_new_data)"]},{"cell_type":"code","execution_count":1,"id":"d864ccca-0ee9-4445-94a5-09b8d614fc85","metadata":{},"outputs":[],"source":["print(predictions)"]},{"cell_type":"markdown","id":"1f232151-d01a-4774-9251-04e8c03f180d","metadata":{},"source":["#### Custom Transformers\n\n"]},{"cell_type":"code","execution_count":1,"id":"b07e3a15-b90d-457b-81d6-4023269d955c","metadata":{},"outputs":[],"source":["from sklearn.preprocessing import FunctionTransformer\n\nlog_transformer = FunctionTransformer(np.log, inverse_func=np.exp)\nlog_pop = log_transformer.transform(housing[[\"population\"]])"]},{"cell_type":"code","execution_count":1,"id":"816433a9-1c22-48a5-8b9a-f76687f2e39c","metadata":{},"outputs":[],"source":["rbf_transformer = FunctionTransformer(rbf_kernel,\n kw_args=dict(Y=[[35.]], gamma=0.1))\nage_simil_35 = rbf_transformer.transform(housing[[\"housing_median_age\"]])"]},{"cell_type":"code","execution_count":1,"id":"2adc7904-9cf7-4fa4-8f40-e41f409354e8","metadata":{},"outputs":[],"source":["print(age_simil_35)"]},{"cell_type":"code","execution_count":1,"id":"5a269320-ccd2-4b39-9cc5-1bae514b83ed","metadata":{},"outputs":[],"source":["sf_coords = 37.7749, -122.41\nsf_transformer = FunctionTransformer(rbf_kernel,\n kw_args=dict(Y=[sf_coords], gamma=0.1))\nsf_simil = sf_transformer.transform(housing[[\"latitude\", \"longitude\"]])"]},{"cell_type":"code","execution_count":1,"id":"316b2462-473d-4dfd-96cd-18ee1e0c53ef","metadata":{},"outputs":[],"source":["print(sf_simil)"]},{"cell_type":"code","execution_count":1,"id":"bd79f899-1f9e-445f-8376-d01d8a011a44","metadata":{},"outputs":[],"source":["ratio_transformer = FunctionTransformer(lambda X: X[:, [0]] / X[:, [1]])\nprint(ratio_transformer.transform(np.array([[1., 2.], [3., 4.]])))"]},{"cell_type":"code","execution_count":1,"id":"5581967c-3da2-4791-946c-1cb1216640d9","metadata":{},"outputs":[],"source":["from sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.utils.validation import check_array, check_is_fitted\n\nclass StandardScalerClone(BaseEstimator, TransformerMixin):\n def __init__(self, with_mean=True): # no *args or **kwargs!\n self.with_mean = with_mean\n\n def fit(self, X, y=None): # y is required even though we don't use it\n X = check_array(X) # checks that X is an array with finite float values\n self.mean_ = X.mean(axis=0)\n self.scale_ = X.std(axis=0)\n self.n_features_in_ = X.shape[1] # every estimator stores this in fit()\n return self # always return self!\n\n def transform(self, X):\n check_is_fitted(self) # looks for learned attributes (with trailing _)\n X = check_array(X)\n assert self.n_features_in_ == X.shape[1]\n if self.with_mean:\n X = X - self.mean_\n return X / self.scale_"]},{"cell_type":"code","execution_count":1,"id":"c552071c-484b-4eb3-9756-804a1d956e32","metadata":{},"outputs":[],"source":["from sklearn.cluster import KMeans\n\nclass ClusterSimilarity(BaseEstimator, TransformerMixin):\n def __init__(self, n_clusters=10, gamma=1.0, random_state=None):\n self.n_clusters = n_clusters\n self.gamma = gamma\n self.random_state = random_state\n\n def fit(self, X, y=None, sample_weight=None):\n self.kmeans_ = KMeans(self.n_clusters, n_init=10,\n random_state=self.random_state)\n self.kmeans_.fit(X, sample_weight=sample_weight)\n return self # always return self!\n\n def transform(self, X):\n return rbf_kernel(X, self.kmeans_.cluster_centers_, gamma=self.gamma)\n \n def get_feature_names_out(self, names=None):\n return [f\"Cluster {i} similarity\" for i in range(self.n_clusters)]"]},{"cell_type":"code","execution_count":1,"id":"4f038bb3-5e28-4f6d-809f-ffd98f838bdd","metadata":{},"outputs":[],"source":["cluster_simil = ClusterSimilarity(n_clusters=10, gamma=1., random_state=42)\nsimilarities = cluster_simil.fit_transform(housing[[\"latitude\", \"longitude\"]],\n sample_weight=housing_labels)"]},{"cell_type":"code","execution_count":1,"id":"b6490997-b899-4668-8580-1ebfa17ed7f8","metadata":{},"outputs":[],"source":["similarities[:3].round(2)"]},{"cell_type":"code","execution_count":1,"id":"e849563b-8faa-488b-963a-fb0cecaee3d8","metadata":{},"outputs":[],"source":["housing_renamed = housing.rename(columns={\n \"latitude\": \"Latitude\", \"longitude\": \"Longitude\",\n \"population\": \"Population\",\n \"median_house_value\": \"Median house value (ᴜsᴅ)\"})\nhousing_renamed[\"Max cluster similarity\"] = similarities.max(axis=1)\n\nhousing_renamed.plot(kind=\"scatter\", x=\"Longitude\", y=\"Latitude\", grid=True,\n s=housing_renamed[\"Population\"] / 100, label=\"Population\",\n c=\"Max cluster similarity\",\n cmap=\"jet\", colorbar=True,\n legend=True, sharex=False, figsize=(10, 7))\nplt.plot(cluster_simil.kmeans_.cluster_centers_[:, 1],\n cluster_simil.kmeans_.cluster_centers_[:, 0],\n linestyle=\"\", color=\"black\", marker=\"X\", markersize=20,\n label=\"Cluster centers\")\nplt.legend(loc=\"upper right\")\nplt.show()"]},{"cell_type":"markdown","id":"067475ef-fc90-4fb5-9aed-5027d9c9f199","metadata":{},"source":["#### Transformation Pipelines\n\n"]},{"cell_type":"markdown","id":"ba1744aa-9080-45d9-a596-788bc6c11b4b","metadata":{},"source":["Now let's build a pipeline to preprocess the numerical attributes:\n\n"]},{"cell_type":"code","execution_count":1,"id":"32a7400d-2120-46a2-b131-188d3ad89bbc","metadata":{},"outputs":[],"source":["from sklearn.pipeline import Pipeline\n\nnum_pipeline = Pipeline([\n (\"impute\", SimpleImputer(strategy=\"median\")),\n (\"standardize\", StandardScaler()),\n])"]},{"cell_type":"code","execution_count":1,"id":"dbcbd78f-b5b1-451a-8b9f-455fd801f652","metadata":{},"outputs":[],"source":["from sklearn.pipeline import make_pipeline\n\nnum_pipeline = make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler())"]},{"cell_type":"code","execution_count":1,"id":"72f4d8e7-55de-4abf-bfcd-e8e4d3a8adee","metadata":{},"outputs":[],"source":["from sklearn import set_config\n\nset_config(display='diagram')\n\nnum_pipeline"]},{"cell_type":"code","execution_count":1,"id":"00536211-5305-41d5-b474-bd715989b0b1","metadata":{},"outputs":[],"source":["housing_num_prepared = num_pipeline.fit_transform(housing_num)\nprint(housing_num_prepared[:2].round(2))"]},{"cell_type":"code","execution_count":1,"id":"04985d06-146f-4fe3-a8b3-8bba2e0d2439","metadata":{},"outputs":[],"source":["def monkey_patch_get_signature_names_out():\n \"\"\"Monkey patch some classes which did not handle get_feature_names_out()\n correctly in Scikit-Learn 1.0.*.\"\"\"\n from inspect import Signature, signature, Parameter\n import pandas as pd\n from sklearn.impute import SimpleImputer\n from sklearn.pipeline import make_pipeline, Pipeline\n from sklearn.preprocessing import FunctionTransformer, StandardScaler\n\n default_get_feature_names_out = StandardScaler.get_feature_names_out\n\n if not hasattr(SimpleImputer, \"get_feature_names_out\"):\n print(\"Monkey-patching SimpleImputer.get_feature_names_out()\")\n SimpleImputer.get_feature_names_out = default_get_feature_names_out\n\n if not hasattr(FunctionTransformer, \"get_feature_names_out\"):\n print(\"Monkey-patching FunctionTransformer.get_feature_names_out()\")\n orig_init = FunctionTransformer.__init__\n orig_sig = signature(orig_init)\n\n def __init__(*args, feature_names_out=None, **kwargs):\n orig_sig.bind(*args, **kwargs)\n orig_init(*args, **kwargs)\n args[0].feature_names_out = feature_names_out\n\n __init__.__signature__ = Signature(\n list(signature(orig_init).parameters.values()) + [\n Parameter(\"feature_names_out\", Parameter.KEYWORD_ONLY)])\n\n def get_feature_names_out(self, names=None):\n if callable(self.feature_names_out):\n return self.feature_names_out(self, names)\n assert self.feature_names_out == \"one-to-one\"\n return default_get_feature_names_out(self, names)\n\n FunctionTransformer.__init__ = __init__\n FunctionTransformer.get_feature_names_out = get_feature_names_out\n\nmonkey_patch_get_signature_names_out()"]},{"cell_type":"code","execution_count":1,"id":"6f3b8b88-9c36-41f3-baa3-79488ed1a4d7","metadata":{},"outputs":[],"source":["df_housing_num_prepared = pd.DataFrame(\n housing_num_prepared, columns=num_pipeline.get_feature_names_out(),\n index=housing_num.index)"]},{"cell_type":"code","execution_count":1,"id":"3e175a94-5299-445a-9976-1d9efb9c1682","metadata":{},"outputs":[],"source":["df_housing_num_prepared.head(2)"]},{"cell_type":"code","execution_count":1,"id":"0382feb8-2aa7-46d8-92f6-a1e4c8cc922e","metadata":{},"outputs":[],"source":["print(num_pipeline.steps)"]},{"cell_type":"code","execution_count":1,"id":"ac244b34-2588-49d2-80cd-8f6c8d9f7693","metadata":{},"outputs":[],"source":["num_pipeline[1]"]},{"cell_type":"code","execution_count":1,"id":"85cd4313-655c-48e9-8e17-e7d839f56382","metadata":{},"outputs":[],"source":["num_pipeline[:-1]"]},{"cell_type":"code","execution_count":1,"id":"6f73e775-8fa2-4995-a9b8-c7e971102482","metadata":{},"outputs":[],"source":["num_pipeline.named_steps[\"simpleimputer\"]"]},{"cell_type":"code","execution_count":1,"id":"2e7a399d-02de-4ced-ac17-f807a8d0f632","metadata":{},"outputs":[],"source":["num_pipeline.set_params(simpleimputer__strategy=\"median\")"]},{"cell_type":"code","execution_count":1,"id":"ebf56adc-409f-4341-a499-8a68dc62635f","metadata":{},"outputs":[],"source":["from sklearn.compose import ColumnTransformer\n\nnum_attribs = [\"longitude\", \"latitude\", \"housing_median_age\", \"total_rooms\",\n \"total_bedrooms\", \"population\", \"households\", \"median_income\"]\ncat_attribs = [\"ocean_proximity\"]\n\ncat_pipeline = make_pipeline(\n SimpleImputer(strategy=\"most_frequent\"),\n OneHotEncoder(handle_unknown=\"ignore\"))\n\npreprocessing = ColumnTransformer([\n (\"num\", num_pipeline, num_attribs),\n (\"cat\", cat_pipeline, cat_attribs),\n])"]},{"cell_type":"code","execution_count":1,"id":"75e298ee-9ce7-472a-8aa5-f52b8bf68033","metadata":{},"outputs":[],"source":["from sklearn.compose import make_column_selector, make_column_transformer\n\npreprocessing = make_column_transformer(\n (num_pipeline, make_column_selector(dtype_include=np.number)),\n (cat_pipeline, make_column_selector(dtype_include=object)),\n)"]},{"cell_type":"code","execution_count":1,"id":"fc9c70d3-91b0-4c74-8950-a083dd7c66d2","metadata":{},"outputs":[],"source":["housing_prepared = preprocessing.fit_transform(housing)"]},{"cell_type":"code","execution_count":1,"id":"4b280062-a710-43fa-be9d-7591221653fd","metadata":{},"outputs":[],"source":["housing_prepared_fr = pd.DataFrame(\n housing_prepared,\n columns=preprocessing.get_feature_names_out(),\n index=housing.index)\nhousing_prepared_fr.head(2)"]},{"cell_type":"code","execution_count":1,"id":"3b2aa392-4de1-4840-a78a-2baa38d24924","metadata":{},"outputs":[],"source":["def column_ratio(X):\n return X[:, [0]] / X[:, [1]]\n\ndef ratio_name(function_transformer, feature_names_in):\n return [\"ratio\"] # feature names out\n\ndef ratio_pipeline():\n return make_pipeline(\n SimpleImputer(strategy=\"median\"),\n FunctionTransformer(column_ratio, feature_names_out=ratio_name),\n StandardScaler())\n\nlog_pipeline = make_pipeline(\n SimpleImputer(strategy=\"median\"),\n FunctionTransformer(np.log, feature_names_out=\"one-to-one\"),\n StandardScaler())\ncluster_simil = ClusterSimilarity(n_clusters=10, gamma=1., random_state=42)\ndefault_num_pipeline = make_pipeline(SimpleImputer(strategy=\"median\"),\n StandardScaler())\npreprocessing = ColumnTransformer([\n (\"bedrooms\", ratio_pipeline(), [\"total_bedrooms\", \"total_rooms\"]),\n (\"rooms_per_house\", ratio_pipeline(), [\"total_rooms\", \"households\"]),\n (\"people_per_house\", ratio_pipeline(), [\"population\", \"households\"]),\n (\"log\", log_pipeline, [\"total_bedrooms\", \"total_rooms\", \"population\",\n \"households\", \"median_income\"]),\n (\"geo\", cluster_simil, [\"latitude\", \"longitude\"]),\n (\"cat\", cat_pipeline, make_column_selector(dtype_include=object)),\n ],\n remainder=default_num_pipeline) # one column remaining: housing_median_age"]},{"cell_type":"code","execution_count":1,"id":"4bb66e19-1c22-4a9e-8617-f29f78bdc928","metadata":{},"outputs":[],"source":["housing_prepared = preprocessing.fit_transform(housing)\nprint(housing_prepared.shape)"]},{"cell_type":"code","execution_count":1,"id":"32482f88-40f9-4a2d-b103-392a0d11a2ba","metadata":{},"outputs":[],"source":["print(preprocessing.get_feature_names_out())"]},{"cell_type":"markdown","id":"ee6db9de-ccee-443c-8bb5-07dbbd547a38","metadata":{},"source":["### Select and Train a Model\n\n"]},{"cell_type":"markdown","id":"b83b0075-8252-4707-92fc-e64a979d1bef","metadata":{},"source":["#### Training and Evaluating on the Training Set\n\n"]},{"cell_type":"code","execution_count":1,"id":"2dc52557-7d68-4f98-a1ca-9e85b570a0f4","metadata":{},"outputs":[],"source":["from sklearn.linear_model import LinearRegression\n\nlin_reg = make_pipeline(preprocessing, LinearRegression())\nprint(lin_reg.fit(housing, housing_labels))"]},{"cell_type":"code","execution_count":1,"id":"1abc833c-4fde-4e6a-9023-01afe0e43cc0","metadata":{},"outputs":[],"source":["housing_predictions = lin_reg.predict(housing)\nprint(housing_predictions[:5].round(-2)) # -2 = rounded to the nearest hundred"]},{"cell_type":"code","execution_count":1,"id":"da7c88f2-f2cc-4106-bb07-0490673fb254","metadata":{},"outputs":[],"source":["print(housing_labels.iloc[:5].values)"]},{"cell_type":"code","execution_count":1,"id":"39b93942-9a0e-484a-a4fe-f24ba4731e8b","metadata":{},"outputs":[],"source":["error_ratios = housing_predictions[:5].round(-2) / housing_labels.iloc[:5].values - 1\nprint(\", \".join([f\"{100 * ratio:.1f}%\" for ratio in error_ratios]))"]},{"cell_type":"code","execution_count":1,"id":"c1e8aa10-b882-4303-bec7-01c2291953e2","metadata":{},"outputs":[],"source":["from sklearn.metrics import mean_squared_error\n\nlin_rmse = mean_squared_error(housing_labels, housing_predictions,\n squared=False)\nlin_rmse"]},{"cell_type":"code","execution_count":1,"id":"46c41cfa-962e-4a12-9c09-f5e2c61fa016","metadata":{},"outputs":[],"source":["from sklearn.tree import DecisionTreeRegressor\n\ntree_reg = make_pipeline(preprocessing, DecisionTreeRegressor(random_state=42))\ntree_reg.fit(housing, housing_labels)"]},{"cell_type":"code","execution_count":1,"id":"57858028-d778-4785-b7ef-779178859725","metadata":{},"outputs":[],"source":["housing_predictions = tree_reg.predict(housing)\ntree_rmse = mean_squared_error(housing_labels, housing_predictions,\n squared=False)\ntree_rmse"]},{"cell_type":"markdown","id":"0fc17b3a-46cd-4b91-8be4-a510058f0b92","metadata":{},"source":["#### Better Evaluation using Cross-Validation\n\n"]},{"cell_type":"code","execution_count":1,"id":"db6efa5a-2117-43e5-9575-d569ae080fd4","metadata":{},"outputs":[],"source":["from sklearn.model_selection import cross_val_score\n\ntree_rmses = -cross_val_score(tree_reg, housing, housing_labels,\n scoring=\"neg_root_mean_squared_error\", cv=10)"]},{"cell_type":"code","execution_count":1,"id":"b786c64c-ab64-42e6-81b0-daa80d8f653b","metadata":{},"outputs":[],"source":["pd.Series(tree_rmses).describe()"]},{"cell_type":"code","execution_count":1,"id":"52b5e478-7b85-4e9f-9c09-13e9bf7eec28","metadata":{},"outputs":[],"source":["lin_rmses = -cross_val_score(lin_reg, housing, housing_labels,\n scoring=\"neg_root_mean_squared_error\", cv=10)\npd.Series(lin_rmses).describe()"]},{"cell_type":"code","execution_count":1,"id":"e4ad4d26-08fb-4ffc-9a53-d65acd4afbd7","metadata":{},"outputs":[],"source":["from sklearn.ensemble import RandomForestRegressor\n\nforest_reg = make_pipeline(preprocessing,\n RandomForestRegressor(random_state=42))\nforest_rmses = -cross_val_score(forest_reg, housing, housing_labels,\n scoring=\"neg_root_mean_squared_error\", cv=10)"]},{"cell_type":"code","execution_count":1,"id":"c95c198a-6ed0-4898-8242-ef9ba889481b","metadata":{},"outputs":[],"source":["pd.Series(forest_rmses).describe()"]},{"cell_type":"code","execution_count":1,"id":"16a009b8-7684-4214-bb33-ce4320164df8","metadata":{},"outputs":[],"source":["forest_reg.fit(housing, housing_labels)\nhousing_predictions = forest_reg.predict(housing)\nforest_rmse = mean_squared_error(housing_labels, housing_predictions,\n squared=False)\nforest_rmse"]},{"cell_type":"markdown","id":"034c105a-3d6c-4e0f-84a1-0b5d11ac9d2a","metadata":{},"source":["### Fine-Tuning the Model\n\n"]},{"cell_type":"markdown","id":"a7dcf159-453d-465e-be00-3265b734bd50","metadata":{},"source":["#### Using Grid Search\n\n"]},{"cell_type":"code","execution_count":1,"id":"7b3a3de3-0979-4986-9d55-d2622216c32b","metadata":{},"outputs":[],"source":["from sklearn.model_selection import GridSearchCV\n\nfull_pipeline = Pipeline([\n (\"preprocessing\", preprocessing),\n (\"random_forest\", RandomForestRegressor(random_state=42)),\n])\nparam_grid = [\n {'preprocessing__geo__n_clusters': [5, 8, 10],\n 'random_forest__max_features': [4, 6, 8]},\n {'preprocessing__geo__n_clusters': [10, 15],\n 'random_forest__max_features': [6, 8, 10]},\n]\ngrid_search = GridSearchCV(full_pipeline, param_grid, cv=3,\n scoring='neg_root_mean_squared_error')\ngrid_search.fit(housing, housing_labels)"]},{"cell_type":"code","execution_count":1,"id":"9dd5bf3b-a3db-4e48-b2b5-e3a16ecaadbe","metadata":{},"outputs":[],"source":["print(str(full_pipeline.get_params().keys())[:1000] + \"...\")"]},{"cell_type":"code","execution_count":1,"id":"38543cc7-f06d-497b-8d2e-31c36d35e4b3","metadata":{},"outputs":[],"source":["print(grid_search.best_params_)"]},{"cell_type":"code","execution_count":1,"id":"a2795fed-0b7c-4121-aa56-fd911614fdfc","metadata":{},"outputs":[],"source":["grid_search.best_estimator_"]},{"cell_type":"code","execution_count":1,"id":"5911c074-d555-443f-9104-abd31b380d48","metadata":{},"outputs":[],"source":["cv_res = pd.DataFrame(grid_search.cv_results_)\ncv_res.sort_values(by=\"mean_test_score\", ascending=False, inplace=True)\n\n# extra code – these few lines of code just make the DataFrame look nicer\ncv_res = cv_res[[\"param_preprocessing__geo__n_clusters\",\n \"param_random_forest__max_features\", \"split0_test_score\",\n \"split1_test_score\", \"split2_test_score\", \"mean_test_score\"]]\nscore_cols = [\"split0\", \"split1\", \"split2\", \"mean_test_rmse\"]\ncv_res.columns = [\"n_clusters\", \"max_features\"] + score_cols\ncv_res[score_cols] = -cv_res[score_cols].round().astype(np.int64)\n\ncv_res.head()"]},{"cell_type":"markdown","id":"f162278b-78a1-49d0-8429-42094c839748","metadata":{},"source":["#### Randomised Search\n\n"]},{"cell_type":"code","execution_count":1,"id":"9d8c8499-47d6-428f-89ac-eb0a50c72a81","metadata":{},"outputs":[],"source":["from sklearn.experimental import enable_halving_search_cv\nfrom sklearn.model_selection import HalvingRandomSearchCV"]},{"cell_type":"code","execution_count":1,"id":"21b04a70-c9c1-4042-9e7c-64f8a7d3a8da","metadata":{},"outputs":[],"source":["from sklearn.model_selection import RandomizedSearchCV\nfrom scipy.stats import randint\n\nparam_distribs = {'preprocessing__geo__n_clusters': randint(low=3, high=50),\n 'random_forest__max_features': randint(low=2, high=20)}\n\nrnd_search = RandomizedSearchCV(\n full_pipeline, param_distributions=param_distribs, n_iter=10, cv=3,\n scoring='neg_root_mean_squared_error', random_state=42)\n\nrnd_search.fit(housing, housing_labels)"]},{"cell_type":"code","execution_count":1,"id":"06ab70b6-ef41-485b-a7e2-c7e3a3668087","metadata":{},"outputs":[],"source":["cv_res = pd.DataFrame(rnd_search.cv_results_)\ncv_res.sort_values(by=\"mean_test_score\", ascending=False, inplace=True)\ncv_res = cv_res[[\"param_preprocessing__geo__n_clusters\",\n \"param_random_forest__max_features\", \"split0_test_score\",\n \"split1_test_score\", \"split2_test_score\", \"mean_test_score\"]]\ncv_res.columns = [\"n_clusters\", \"max_features\"] + score_cols\ncv_res[score_cols] = -cv_res[score_cols].round().astype(np.int64)\ncv_res.head()"]},{"cell_type":"code","execution_count":1,"id":"76b00b41-5d0f-4d66-a5a0-ea2277425b25","metadata":{},"outputs":[],"source":["from scipy.stats import randint, uniform, geom, expon\n\nxs1 = np.arange(0, 7 + 1)\nrandint_distrib = randint(0, 7 + 1).pmf(xs1)\n\nxs2 = np.linspace(0, 7, 500)\nuniform_distrib = uniform(0, 7).pdf(xs2)\n\nxs3 = np.arange(0, 7 + 1)\ngeom_distrib = geom(0.5).pmf(xs3)\n\nxs4 = np.linspace(0, 7, 500)\nexpon_distrib = expon(scale=1).pdf(xs4)\n\nplt.figure(figsize=(12, 7))\n\nplt.subplot(2, 2, 1)\nplt.bar(xs1, randint_distrib, label=\"scipy.randint(0, 7 + 1)\")\nplt.ylabel(\"Probability\")\nplt.legend()\nplt.axis([-1, 8, 0, 0.2])\n\nplt.subplot(2, 2, 2)\nplt.fill_between(xs2, uniform_distrib, label=\"scipy.uniform(0, 7)\")\nplt.ylabel(\"PDF\")\nplt.legend()\nplt.axis([-1, 8, 0, 0.2])\n\nplt.subplot(2, 2, 3)\nplt.bar(xs3, geom_distrib, label=\"scipy.geom(0.5)\")\nplt.xlabel(\"Hyperparameter value\")\nplt.ylabel(\"Probability\")\nplt.legend()\nplt.axis([0, 7, 0, 1])\n\nplt.subplot(2, 2, 4)\nplt.fill_between(xs4, expon_distrib, label=\"scipy.expon(scale=1)\")\nplt.xlabel(\"Hyperparameter value\")\nplt.ylabel(\"PDF\")\nplt.legend()\nplt.axis([0, 7, 0, 1])\n\nplt.show()"]},{"cell_type":"code","execution_count":1,"id":"4fa6393d-6663-42f3-a186-d36ca85d3f88","metadata":{},"outputs":[],"source":["from scipy.stats import loguniform\n\nxs1 = np.linspace(0, 7, 500)\nexpon_distrib = expon(scale=1).pdf(xs1)\n\nlog_xs2 = np.linspace(-5, 3, 500)\nlog_expon_distrib = np.exp(log_xs2 - np.exp(log_xs2))\n\nxs3 = np.linspace(0.001, 1000, 500)\nloguniform_distrib = loguniform(0.001, 1000).pdf(xs3)\n\nlog_xs4 = np.linspace(np.log(0.001), np.log(1000), 500)\nlog_loguniform_distrib = uniform(np.log(0.001), np.log(1000)).pdf(log_xs4)\n\nplt.figure(figsize=(12, 7))\n\nplt.subplot(2, 2, 1)\nplt.fill_between(xs1, expon_distrib,\n label=\"scipy.expon(scale=1)\")\nplt.ylabel(\"PDF\")\nplt.legend()\nplt.axis([0, 7, 0, 1])\n\nplt.subplot(2, 2, 2)\nplt.fill_between(log_xs2, log_expon_distrib,\n label=\"log(X) with X ~ expon\")\nplt.legend()\nplt.axis([-5, 3, 0, 1])\n\nplt.subplot(2, 2, 3)\nplt.fill_between(xs3, loguniform_distrib,\n label=\"scipy.loguniform(0.001, 1000)\")\nplt.xlabel(\"Hyperparameter value\")\nplt.ylabel(\"PDF\")\nplt.legend()\nplt.axis([0.001, 1000, 0, 0.005])\n\nplt.subplot(2, 2, 4)\nplt.fill_between(log_xs4, log_loguniform_distrib,\n label=\"log(X) with X ~ loguniform\")\nplt.xlabel(\"Log of hyperparameter value\")\nplt.legend()\nplt.axis([-8, 1, 0, 0.2])\n\nplt.show()"]},{"cell_type":"markdown","id":"88d29386-6722-4a79-a803-5259dbacbbe3","metadata":{},"source":["#### Analyse the Best Models and Errors\n\n"]},{"cell_type":"code","execution_count":1,"id":"b4f1a138-0e7b-4e6d-bc9c-e062eb4ca362","metadata":{},"outputs":[],"source":["final_model = rnd_search.best_estimator_ # includes preprocessing\nfeature_importances = final_model[\"random_forest\"].feature_importances_\nprint(feature_importances.round(2))"]},{"cell_type":"code","execution_count":1,"id":"60f41b49-acda-4e6a-ac3d-100bf87fe9ec","metadata":{},"outputs":[],"source":["sorted(zip(feature_importances,\n final_model[\"preprocessing\"].get_feature_names_out()),\n reverse=True)"]},{"cell_type":"markdown","id":"d7cc711a-5ce7-4a25-845a-2a7807a3e07c","metadata":{},"source":["\n| 0.19087378222226137|log_medianincome|\n| 0.07625632853052883|cat_oceanproximityINLAND|\n| 0.06365028932207333|bedrooms_ratio|\n| 0.057834740538722625|roomsperhouse_ratio|\n| 0.04907003277818634|peopleperhouse_ratio|\n| 0.038165489600129165|geo_Cluster 3 similarity|\n| 0.025700861301416925|geo_Cluster 22 similarity|\n| 0.02186407550147744|geo_Cluster 17 similarity|\n| 0.021818299311019237|geo_Cluster 6 similarity|\n| 0.018249904787654904|geo_Cluster 2 similarity|\n| 0.017263517651784216|geo_Cluster 32 similarity|\n| 0.015649725317935348|geo_Cluster 18 similarity|\n| 0.015236556682888558|geo_Cluster 40 similarity|\n| 0.014160249342841777|geo_Cluster 43 similarity|\n| 0.014113856232349186|geo_Cluster 7 similarity|\n| 0.013968406769681294|geo_Cluster 21 similarity|\n| 0.013781633271007265|geo_Cluster 38 similarity|\n| 0.013515022744382842|geo_Cluster 34 similarity|\n| 0.013508738042902313|geo_Cluster 41 similarity|\n| 0.012844820424121687|geo_Cluster 24 similarity|\n| 0.01236427981858226|geo_Cluster 10 similarity|\n| 0.01176408158393247|remainder_housingmedianage|\n| 0.011436849025886087|geo_Cluster 31 similarity|\n| 0.011430032718708965|geo_Cluster 30 similarity|\n| 0.011262888671999243|geo_Cluster 42 similarity|\n| 0.011082126500672662|geo_Cluster 16 similarity|\n| 0.01087991522984511|geo_Cluster 1 similarity|\n| 0.0106352262787596|geo_Cluster 25 similarity|\n| 0.010629636976156976|geo_Cluster 26 similarity|\n| 0.010325438106958176|geo_Cluster 20 similarity|\n| 0.009978597341631139|geo_Cluster 35 similarity|\n| 0.009811902116084414|geo_Cluster 14 similarity|\n| 0.00926835411026417|geo_Cluster 39 similarity|\n| 0.009210910491673824|geo_Cluster 37 similarity|\n| 0.008838219938405523|geo_Cluster 0 similarity|\n| 0.00883623406533351|geo_Cluster 9 similarity|\n| 0.008743931217845727|geo_Cluster 8 similarity|\n| 0.008563362393325231|geo_Cluster 36 similarity|\n| 0.008465719960196051|geo_Cluster 28 similarity|\n| 0.008001576292023282|geo_Cluster 44 similarity|\n| 0.007942690751495287|geo_Cluster 4 similarity|\n| 0.00792848112158647|geo_Cluster 11 similarity|\n| 0.007724755678419276|log_totalrooms|\n| 0.0071161520040372486|log_population|\n| 0.006792638958967365|log_totalbedrooms|\n| 0.006504955481581277|log_households|\n| 0.006215189140929538|geo_Cluster 23 similarity|\n| 0.0056299034599644185|geo_Cluster 19 similarity|\n| 0.005544728303210014|geo_Cluster 27 similarity|\n| 0.00526201040506395|geo_Cluster 33 similarity|\n| 0.004834036365364593|geo_Cluster 15 similarity|\n| 0.004177160622478634|geo_Cluster 12 similarity|\n| 0.0040378619656822245|geo_Cluster 13 similarity|\n| 0.0036513301086410445|geo_Cluster 29 similarity|\n| 0.0033595680753400604|cat_oceanproximity_<1H OCEAN|\n| 0.001969988014822343|geo_Cluster 5 similarity|\n| 0.001955486511135513|cat_oceanproximityNEAR OCEAN|\n| 0.0002362031046240973|cat_oceanproximityNEAR BAY|\n| 6.124671500751693e-05|cat_oceanproximityISLAND|\n\n"]},{"cell_type":"markdown","id":"d50ca333-5ace-4b7b-935e-0f2824e04a71","metadata":{},"source":["#### Evaluate using the Test Set\n\n"]},{"cell_type":"markdown","id":"33fa25f5-a166-4d97-a169-724db63fa18f","metadata":{},"source":["Time to evaluate the model with the test set:\n\n1. get the predictors and the labels from the test set,\n2. run finalmode to transform data,\n3. make predictions,\n4. evaluate the predictions.\n\n"]},{"cell_type":"code","execution_count":1,"id":"0f291023-2da8-4413-b0fd-a4f319aa7a39","metadata":{},"outputs":[],"source":["X_test = strat_test_set.drop(\"median_house_value\", axis=1)\ny_test = strat_test_set[\"median_house_value\"].copy()\n\nfinal_predictions = final_model.predict(X_test)\n\nfinal_rmse = mean_squared_error(y_test, final_predictions, squared=False)\nfinal_rmse"]},{"cell_type":"markdown","id":"0b8a089a-3c5b-4be8-a8c1-21a5fb729c1e","metadata":{},"source":["We need to know How good is the model. For this, compute\nthe %95 [confidence interval](https://en.wikipedia.org/wiki/Confidence_interval) for the generalisation error using\nscipy.stats.t.interval().\n\n"]},{"cell_type":"code","execution_count":1,"id":"5b831bad-dbd3-485f-ada2-4c60c1365913","metadata":{},"outputs":[],"source":["from scipy import stats\n\nconfidence = 0.95\nsquared_errors = (final_predictions - y_test) ** 2\nresult= np.sqrt(stats.t.interval(confidence, len(squared_errors) - 1,\n loc=squared_errors.mean(),\n scale=stats.sem(squared_errors)))\nprint(result)"]},{"cell_type":"markdown","id":"6543d7d2-e7e1-4566-92f7-7a5e87f89781","metadata":{},"source":["The value is roughly in the middle of it.\n\nThere are different ways of calculating it as well.\n\nBelow is the manual way of writing the confidence interval.\n\n"]},{"cell_type":"code","execution_count":1,"id":"e6691159-a1e0-488d-88d9-2512c98ac83c","metadata":{},"outputs":[],"source":["m = len(squared_errors)\nmean = squared_errors.mean()\ntscore = stats.t.ppf((1 + confidence) / 2, df=m - 1)\ntmargin = tscore * squared_errors.std(ddof=1) / np.sqrt(m)\nprint(np.sqrt(mean - tmargin), np.sqrt(mean + tmargin))"]},{"cell_type":"markdown","id":"6c413639-1623-493b-85ea-84c8212e4f87","metadata":{},"source":["Alternatively, we can use z-score (i.e., [Standard score](https://en.wikipedia.org/wiki/Standard_score)) to calculate as the\ndata set is relatively big.\n\n"]},{"cell_type":"code","execution_count":1,"id":"9bff0745-66b5-416e-a9d3-506e58854d01","metadata":{},"outputs":[],"source":["zscore = stats.norm.ppf((1 + confidence) / 2)\nzmargin = zscore * squared_errors.std(ddof=1) / np.sqrt(m)\nprint(np.sqrt(mean - zmargin), np.sqrt(mean + zmargin))"]},{"cell_type":"markdown","id":"650c46be-5d23-4e07-bf32-a2d1659bf0d0","metadata":{},"source":["NOTE: Doing a lot of hyperparameter tuning can make the model behave worse as\nit would be highly tuned to the train data and may not be as performant as on test data.\n\n"]},{"cell_type":"markdown","id":"35fb2060-961c-4098-94a5-2c66cc3b6ed9","metadata":{},"source":["#### Saving the Model\n\n"]},{"cell_type":"markdown","id":"90b21863-50ee-43da-8f99-9c47aaa3bbcb","metadata":{},"source":["Time to get the model into production and the easiest way to do is to save the best model.\n\nTo save it, you can use the joblib module [[More Info]​](https://joblib.readthedocs.io/en/stable/), which allows pipelining.\n\n"]},{"cell_type":"code","execution_count":1,"id":"a98f2311-6452-4c28-b1e4-79bf192757c0","metadata":{},"outputs":[],"source":["import joblib\n\njoblib.dump(final_model, \"my_california_housing_model.pkl\")"]},{"cell_type":"markdown","id":"7e1164be-b3dd-4234-a344-0e12d84c38a4","metadata":{},"source":["Once saved, you can load and use it, with all the necessary dependencies loaded.\n\n"]},{"cell_type":"code","execution_count":1,"id":"074cb001-a603-48c1-af2e-80d44131be93","metadata":{},"outputs":[],"source":["import joblib\n\n# extra code – excluded for conciseness\nfrom sklearn.cluster import KMeans\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.metrics.pairwise import rbf_kernel\n\ndef column_ratio(X):\n return X[:, [0]] / X[:, [1]]\n\n#class ClusterSimilarity(BaseEstimator, TransformerMixin):\n# [...]\n\nfinal_model_reloaded = joblib.load(\"my_california_housing_model.pkl\")\n\nnew_data = housing.iloc[:5] # pretend these are new districts\npredictions = final_model_reloaded.predict(new_data)\nprint(predictions)"]}],"metadata":{"org":null,"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":"3.5.2"}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a09ddb01-96ec-46b9-881e-7ace9e59cb86", + "metadata": {}, + "source": [ + "Code for Machine Learning and Data Science II\n", + "=============================================\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "9807bde7-696c-4372-b6b2-cb806daeb121", + "metadata": {}, + "source": [ + "These are the code snippets used in End to End ML Project\n", + "part of Machine Learning and Data Science II.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "9a201050-bc1e-4bad-82d6-a071acd88b07", + "metadata": {}, + "source": [ + "### Introduction\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "02c51564-628f-4c76-b765-8ef7cb1419b5", + "metadata": {}, + "source": [ + "#### Preamble\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a572842c-0c47-4157-8594-e454cea09a95", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import ChalcedonPy as cp\n", + "\n", + "# Initialise ChalcedonPy\n", + "cp.init(save_path=\"End-to-End-ML-Project\",\n", + " display_mode=\"slide\")" + ] + }, + { + "cell_type": "markdown", + "id": "ab42e7d1-cf5c-4093-a6d3-b12d0a2c634d", + "metadata": {}, + "source": [ + "#### Download Initial Data\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "9f915f99-d82e-4ef5-9f9c-3565678ebfea", + "metadata": {}, + "source": [ + "First lets load the necessary modules for downloading the data so we\n", + "can work on it.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "51dfa77d-eb8c-4972-a27c-e27b4f456a5e", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import pandas as pd # for dataframes\n", + "import tarfile # read/write tar files\n", + "# to access and download data from web\n", + "import urllib.request\n", + "from tabulate import tabulate # for table printing" + ] + }, + { + "cell_type": "markdown", + "id": "6bab24d4-3c98-463f-99bf-a60d2535943d", + "metadata": {}, + "source": [ + "Define a function called loadhousingdata() to access and\n", + "download the data, finally returning a read of it using pandas.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "79bc64a3-4f51-4020-9a5b-8b63868a8a6e", + "metadata": {}, + "outputs": [], + "source": [ + "def load_housing_data():\n", + " # path to save the file\n", + " tarball_path = Path(\"datasets/housing.tgz\")\n", + " # check if the path exists, if not create one\n", + "\n", + " if not tarball_path.is_file():\n", + " Path(\"datasets\").mkdir(parents=True, exist_ok=True)\n", + " url = \"https://github.com/dTmC0945/L-MCI-BSc-Data-Science-II/raw/main/data/housing.tgz\"\n", + " urllib.request.urlretrieve(url, tarball_path)\n", + " with tarfile.open(tarball_path) as housing_tarball:\n", + " housing_tarball.extractall(path=\"datasets\")\n", + " return pd.read_csv(Path(\"datasets/housing/housing.csv\"))" + ] + }, + { + "cell_type": "markdown", + "id": "5d7935d1-f02a-4937-b6c7-72042925ea55", + "metadata": {}, + "source": [ + "Now read the data and assign it to the value housing.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "60df4167-7d6b-4b7a-83ac-43f63b30adbb", + "metadata": {}, + "outputs": [], + "source": [ + "housing = load_housing_data()" + ] + }, + { + "cell_type": "markdown", + "id": "4eeb321a-b4da-44dd-8f59-5e67aa73b0dc", + "metadata": {}, + "source": [ + "Let's have a look at the data\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "854d9470-cf84-48a0-8844-914b1f3f7390", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "0 -122.23 37.88 41.0 880.0 129.0 \n", + "1 -122.22 37.86 21.0 7099.0 1106.0 \n", + "2 -122.24 37.85 52.0 1467.0 190.0 \n", + "3 -122.25 37.85 52.0 1274.0 235.0 \n", + "4 -122.25 37.85 52.0 1627.0 280.0 \n", + "\n", + " population households median_income median_house_value ocean_proximity \n", + "0 322.0 126.0 8.3252 452600.0 NEAR BAY \n", + "1 2401.0 1138.0 8.3014 358500.0 NEAR BAY \n", + "2 496.0 177.0 7.2574 352100.0 NEAR BAY \n", + "3 558.0 219.0 5.6431 341300.0 NEAR BAY \n", + "4 565.0 259.0 3.8462 342200.0 NEAR BAY \n" + ] + } + ], + "source": [ + "print(housing.head())" + ] + }, + { + "cell_type": "markdown", + "id": "40aef187-9215-4b09-932e-da8ca7e7fea5", + "metadata": {}, + "source": [ + "Each row represents one district.\n", + "\n", + "There are 10 attributes\n", + "\n", + "- longitude,\n", + "- latitude,\n", + "- housingmedianage,\n", + "- totalrooms,\n", + "- totalbed rooms,\n", + "- population,\n", + "- households,\n", + "- medianincome,\n", + "- medianhousevalue,\n", + "- and oceanproximity.\n", + "\n", + "To get more information about the data use the info() method.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9a445f72-cb19-4ef9-b761-934ecafe65c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 20640 entries, 0 to 20639\n", + "Data columns (total 10 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 longitude 20640 non-null float64\n", + " 1 latitude 20640 non-null float64\n", + " 2 housing_median_age 20640 non-null float64\n", + " 3 total_rooms 20640 non-null float64\n", + " 4 total_bedrooms 20433 non-null float64\n", + " 5 population 20640 non-null float64\n", + " 6 households 20640 non-null float64\n", + " 7 median_income 20640 non-null float64\n", + " 8 median_house_value 20640 non-null float64\n", + " 9 ocean_proximity 20640 non-null object \n", + "dtypes: float64(9), object(1)\n", + "memory usage: 1.6+ MB\n", + "None\n" + ] + } + ], + "source": [ + "print(housing.info())" + ] + }, + { + "cell_type": "markdown", + "id": "69afa7ac-4542-4e50-aaa9-7ceef2267a46", + "metadata": {}, + "source": [ + "There seems to be some repetition on the oceanproximity parameters.\n", + "Let's have a bit more look into the data.\n", + "\n", + "To get more information on it use the valuecounts() method.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "344d439c-e893-4fd3-9114-2910b32fd6d1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<1H OCEAN 9136\n", + "INLAND 6551\n", + "NEAR OCEAN 2658\n", + "NEAR BAY 2290\n", + "ISLAND 5\n", + "Name: ocean_proximity, dtype: int64\n" + ] + } + ], + "source": [ + "print(housing[\"ocean_proximity\"].value_counts())" + ] + }, + { + "cell_type": "markdown", + "id": "012323dd-000e-43ab-9d0d-c30160461c60", + "metadata": {}, + "source": [ + "Time to look at the the other fields using the describe() method which\n", + "shows their numerical attributes.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "1527e44a-7bf7-4987-a0c4-611c9fc2c9fe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "print(housing.describe)" + ] + }, + { + "cell_type": "markdown", + "id": "e712a87e-44d4-47a7-bf45-64e68496b59e", + "metadata": {}, + "source": [ + "Allows the pretty print of the results.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3387fb2e-62d6-4adc-b32b-dc04194905fb", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "housing.hist(bins=50, figsize=(12, 8))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b84aba26-6038-4e9e-9ea7-9f1a1aaa7b70", + "metadata": {}, + "source": [ + "#### Create a Set for Testing\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7bbcb7fe-560a-4024-bab0-f04361f808cf", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "# Define a function to shuffle and split data\n", + "def shuffle_and_split_data(data, test_ratio):\n", + " shuffled_indices = np.random.permutation(len(data))\n", + " test_set_size = int(len(data) * test_ratio)\n", + " test_indices = shuffled_indices[:test_set_size]\n", + " train_indices = shuffled_indices[test_set_size:]\n", + " return data.iloc[train_indices], data.iloc[test_indices]" + ] + }, + { + "cell_type": "markdown", + "id": "66f7b878-6ee3-4c60-838d-c7150aba1687", + "metadata": {}, + "source": [ + "To use the data we can do the following\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0cfab345-57ab-4271-8a8c-53cca85a11ec", + "metadata": {}, + "outputs": [], + "source": [ + "train_set, test_set = shuffle_and_split_data(housing, 0.2)" + ] + }, + { + "cell_type": "markdown", + "id": "3ec7dfa9-d57e-48cb-9928-076d734a766d", + "metadata": {}, + "source": [ + "Lets see the sizes of the datasets\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d080038-8fcc-4c93-ae23-2404cb402cc3", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"The size of the training set is:\", len(train_set))\n", + "print(\"The size of the test data is:\", len(test_set))" + ] + }, + { + "cell_type": "markdown", + "id": "f09b2630-1f49-435c-993c-535814f5ece3", + "metadata": {}, + "source": [ + "This will shuffle but because of shuffling the program will see\n", + "all the data eventually which is not something good.\n", + "\n", + "To avoid it we set a RNG seed to keep the shuffled indices constant.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a285ac59-8b35-453f-b256-0d0937dbb046", + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(42)" + ] + }, + { + "cell_type": "markdown", + "id": "c5a1ca98-7239-4f55-86d0-c851a280c36d", + "metadata": {}, + "source": [ + "Here is another method in which we can keep the split constant even if\n", + "the dataset is refreshed.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7941262-bb4a-4edb-9b19-a72cfc128321", + "metadata": {}, + "outputs": [], + "source": [ + "from zlib import crc32\n", + "\n", + "def is_id_in_test_set(identifier, test_ratio):\n", + " return crc32(np.int64(identifier)) < test_ratio * 2**32\n", + "\n", + "def split_data_with_id_hash(data, test_ratio, id_column):\n", + " ids = data[id_column]\n", + " in_test_set = ids.apply(\n", + " lambda id_: is_id_in_test_set(id_, test_ratio))\n", + " return data.loc[~in_test_set], data.loc[in_test_set]" + ] + }, + { + "cell_type": "markdown", + "id": "d321d355-41b4-406d-b0b3-61b8bcf20dd8", + "metadata": {}, + "source": [ + "Unfortunately, the housing dataset does not have an identifier column.\n", + "The simplest solution is to use the row index as the ID:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30843266-7afe-4204-b530-76a31a108d3a", + "metadata": {}, + "outputs": [], + "source": [ + "housing_with_id = housing.reset_index() # adds an `index` column\n", + "train_set, test_set = split_data_with_id_hash(housing_with_id, 0.2, \"index\")" + ] + }, + { + "cell_type": "markdown", + "id": "04f92264-8f80-4f7c-8921-2dbdca85f9d8", + "metadata": {}, + "source": [ + "If you use the row index as a unique identifier, you need to make\n", + "sure that new data gets appended to the end of the dataset and that no row ever gets deleted.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed49b3ac-27d1-4b95-b9b5-6aa109bdefc6", + "metadata": {}, + "outputs": [], + "source": [ + "housing_with_id[\"id\"] = housing[\"longitude\"] * 1000 + housing[\"latitude\"]\n", + "train_set, test_set = split_data_with_id_hash(housing_with_id, 0.2, \"id\")" + ] + }, + { + "cell_type": "markdown", + "id": "544a3dba-2156-48f0-8f01-cefa32ffa726", + "metadata": {}, + "source": [ + "Scikit-Learn provides a few functions to split datasets into multiple subsets in various ways.\n", + "An easy function is traintestsplit()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b33932c3-38e0-4089-bb2c-3bc9baff0982", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "id": "a914ea35-1722-4d3d-a54f-eafb9482678e", + "metadata": {}, + "source": [ + "To find the probability that a random sample of 1,000 people contains less than 48.5% female or\n", + "more than 53.5% female when the population's female ratio is 51.1%, we use the binomial distribution.\n", + "\n", + "The cdf() method of the binomial distribution gives us the probability that the\n", + "number of females will be equal or less than the given value.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a94577e1-3194-43e7-a1bf-003badfd4044", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import binom\n", + "\n", + "sample_size = 1000\n", + "ratio_female = 0.511\n", + "proba_too_small = binom(sample_size, ratio_female).cdf(485 - 1)\n", + "proba_too_large = 1 - binom(sample_size, ratio_female).cdf(535)\n", + "print(proba_too_small + proba_too_large)" + ] + }, + { + "cell_type": "markdown", + "id": "f989f5cb-00c3-41cc-9c06-62c21aa16f3d", + "metadata": {}, + "source": [ + "However, for the ones who prefer numerical results over explicit solutions,\n", + "there is also the below method to achieve a similar result.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39d7b95e-8bc7-4deb-9b4c-2bcaa5dd1b9c", + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(42)\n", + "\n", + "samples = (np.random.rand(100_000, sample_size) < ratio_female).sum(axis=1)\n", + "((samples < 485) | (samples > 535)).mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f5bedbf-7bca-44ee-b0cb-3bf4d08ea757", + "metadata": {}, + "outputs": [], + "source": [ + "housing[\"income_cat\"] = pd.cut(housing[\"median_income\"],\n", + " bins=[0., 1.5, 3.0, 4.5, 6., np.inf],\n", + " labels=[1, 2, 3, 4, 5])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab2462b4-0da8-4270-a382-798f9997a650", + "metadata": {}, + "outputs": [], + "source": [ + "plt.xlabel(\"Income category\")\n", + "plt.ylabel(\"Number of districts\")\n", + "housing[\"income_cat\"].value_counts().sort_index().plot.bar(rot=0, grid=True)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "32aecc3f-d478-4225-8299-65973d36e2a1", + "metadata": {}, + "source": [ + "Now you are ready to do stratified sampling based on the income category.\n", + "For this you can use Scikit-Learn’s StratifiedShuffleSplit class:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b5ca3da-2a23-4b22-b7ec-b356f7f1f4a6", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import StratifiedShuffleSplit\n", + "\n", + "splitter = StratifiedShuffleSplit(n_splits=10, test_size=0.2, random_state=42)\n", + "strat_splits = []\n", + "for train_index, test_index in splitter.split(housing, housing[\"income_cat\"]):\n", + " strat_train_set_n = housing.iloc[train_index]\n", + " strat_test_set_n = housing.iloc[test_index]\n", + " strat_splits.append([strat_train_set_n, strat_test_set_n])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5355e4ad-9968-4970-9108-27e3c7f9b717", + "metadata": {}, + "outputs": [], + "source": [ + "strat_train_set, strat_test_set = strat_splits[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33c7878e-0c03-456e-9345-d0582f26020a", + "metadata": {}, + "outputs": [], + "source": [ + "strat_train_set, strat_test_set = train_test_split(\n", + " housing,\n", + " test_size=0.2,\n", + " stratify=housing[\"income_cat\"],\n", + " random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "209ac8bd-a37a-43e2-80fe-cc7c24feca11", + "metadata": {}, + "outputs": [], + "source": [ + "strat_test_set[\"income_cat\"].value_counts() / len(strat_test_set)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "543978b8-b7d3-4dae-9308-7f1e68326ac7", + "metadata": {}, + "outputs": [], + "source": [ + "def income_cat_proportions(data):\n", + " return data[\"income_cat\"].value_counts() / len(data)\n", + "\n", + "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n", + "\n", + "compare_props = pd.DataFrame({\n", + " \"Overall %\": income_cat_proportions(housing),\n", + " \"Stratified %\": income_cat_proportions(strat_test_set),\n", + " \"Random %\": income_cat_proportions(test_set),\n", + "}).sort_index()\n", + "compare_props.index.name = \"Income Category\"\n", + "compare_props[\"Strat. Error %\"] = (compare_props[\"Stratified %\"] /\n", + " compare_props[\"Overall %\"] - 1)\n", + "compare_props[\"Rand. Error %\"] = (compare_props[\"Random %\"] /\n", + " compare_props[\"Overall %\"] - 1)\n", + "(compare_props * 100).round(2)" + ] + }, + { + "cell_type": "markdown", + "id": "063cb0e1-9c0b-47f5-950d-af5b747429e6", + "metadata": {}, + "source": [ + "Time to drop incomecat() attribute to go back to the\n", + "original state.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "233c911d-3ca4-40ed-809e-51b3edda3680", + "metadata": {}, + "outputs": [], + "source": [ + "for set_ in (strat_train_set, strat_test_set):\n", + " set_.drop(\"income_cat\", axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "33b1504a-4b7e-4c6e-bca1-936c6e3fde38", + "metadata": {}, + "source": [ + "### Discover and Visualize the Data to Gain Insights\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "9d20829c-72a6-4506-b85d-59c50f81a811", + "metadata": {}, + "source": [ + "Before we start playing with the data it is a good habit to\n", + "create a copy so as to not tamper with the training set.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9864e03b-bec7-476b-801a-c9ea4c3d95b8", + "metadata": {}, + "outputs": [], + "source": [ + "housing = strat_train_set.copy()" + ] + }, + { + "cell_type": "markdown", + "id": "9f0eaaf3-bfd9-4a1a-974c-1694f8cddbad", + "metadata": {}, + "source": [ + "#### Visualising Geographical Data\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "9dc1ffb3-8ece-4a71-8cc8-647335f14663", + "metadata": {}, + "source": [ + "As data is a bunch of points in 2D space, it is benefical to\n", + "plot it in a scatter plot.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf08ffe4-4638-495f-80eb-f474f8e5d56e", + "metadata": {}, + "outputs": [], + "source": [ + "housing.plot(kind = \"scatter\",\n", + " x = \"longitude\",\n", + " y = \"latitude\",\n", + " title = \"Housing Market in California\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ca3aaa28-9309-484d-ad5b-6fa2435df4aa", + "metadata": {}, + "source": [ + "This might remind you a state in a country but it is currently not\n", + "possible to see a pattern.\n", + "\n", + "Let's set the alpha to 0.2 to see if it helps better.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d967e640-fc64-4e08-ad4a-ce7db40914c7", + "metadata": {}, + "outputs": [], + "source": [ + "housing.plot(kind = \"scatter\",\n", + " x = \"longitude\",\n", + " y = \"latitude\",\n", + " title = r\"Housing Market in California ($\\alpha = 0.2$)\",\n", + " alpha = 0.2)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "619e83f5-88da-4246-b383-9b0537c2853f", + "metadata": {}, + "source": [ + "Let's make it a bit more interesting and create a plot where the higher cluster areas\n", + "would be coloured red and sparser places will be coloured colder as the value of\n", + "the land is less.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f522c2f2-5712-43f9-a9b0-0e53d3515e9d", + "metadata": {}, + "outputs": [], + "source": [ + "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\",\n", + " s=housing[\"population\"] / 100, label=\"population\",\n", + " c=\"median_house_value\", cmap=\"jet\", colorbar=True,\n", + " legend=True, sharex=False, figsize=(10, 7))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2e6829b5-7fcb-4a85-b283-7376b88dc7cc", + "metadata": {}, + "source": [ + "Now we can see the housing prices are very much related to the location\n", + "in this case closer to the ocean. To add a final addition let's superimpose\n", + "the state map over it.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a01a4828-828d-40b2-979c-c98b365f08f7", + "metadata": {}, + "outputs": [], + "source": [ + "filename = \"california.png\"\n", + "\n", + "if not (IMAGES_PATH / filename).is_file():\n", + " homl3_root = \"https://github.com/ageron/handson-ml3/raw/main/\"\n", + " url = homl3_root + \"images/end_to_end_project/\" + filename\n", + " print(\"Downloading\", filename)\n", + " urllib.request.urlretrieve(url, IMAGES_PATH / filename)\n", + "\n", + "housing_renamed = housing.rename(columns={\n", + " \"latitude\": \"Latitude\", \"longitude\": \"Longitude\",\n", + " \"population\": \"Population\",\n", + " \"median_house_value\": \"Median house value (ᴜsᴅ)\"})\n", + "\n", + "plot_settings(style = \"slide\")\n", + "housing_renamed.plot(\n", + " kind=\"scatter\", x=\"Longitude\", y=\"Latitude\",\n", + " s=housing_renamed[\"Population\"] / 100, label=\"Population\",\n", + " c=\"Median house value (ᴜsᴅ)\", cmap=\"jet\", colorbar=True,\n", + " legend=True, sharex=False, figsize=(10, 7))\n", + "\n", + "california_img = plt.imread(IMAGES_PATH / filename)\n", + "axis = -124.55, -113.95, 32.45, 42.05\n", + "plt.axis(axis)\n", + "plt.imshow(california_img, extent=axis)\n", + "\n", + "store_fig(\"california-housing-prices-plot\",\n", + " style = \"slide\",\n", + " close = True)" + ] + }, + { + "cell_type": "markdown", + "id": "b5180c09-48e7-4a35-9669-e9c8c46d602a", + "metadata": {}, + "source": [ + "#### Looking for Correlations\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e956b962-a15e-4275-8e87-0e8d8cd6380c", + "metadata": {}, + "source": [ + "Time to see if there is any correlation between the value of the houses\n", + "and other parameters using pearsons correlation\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41f3260e-e134-46dd-b9f8-89d1489cb085", + "metadata": {}, + "outputs": [], + "source": [ + "corr_matrix = housing.corr(numeric_only=True)\n", + "corr_matrix[\"median_house_value\"].sort_values(ascending=False)" + ] + }, + { + "cell_type": "markdown", + "id": "e8643f6b-f88d-4c95-9b5f-a49f460c07e6", + "metadata": {}, + "source": [ + "Another way to check for correlation between attributes is to\n", + "use the pandas scattermatrix() function,\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b1a4a20-842c-4ad9-ad80-c72dfd5f32fe", + "metadata": {}, + "outputs": [], + "source": [ + "from pandas.plotting import scatter_matrix\n", + "\n", + "attributes = [\"median_house_value\", \"median_income\", \"total_rooms\",\n", + " \"housing_median_age\"]\n", + "\n", + "scatter_matrix(housing[attributes], figsize=(12, 8))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e71614aa-ed9f-4c1b-acb6-f5d81c2214ba", + "metadata": {}, + "source": [ + "The most promising attribute to predict the median house value\n", + "is the median income,\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b6c2696-29f6-40b0-b704-8f0b53ecf3ed", + "metadata": {}, + "outputs": [], + "source": [ + "housing.plot(kind=\"scatter\", x=\"median_income\", y=\"median_house_value\",\n", + " alpha=0.1, grid=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8c90240c-d179-4c18-b61d-65403bc13bf2", + "metadata": {}, + "source": [ + "#### Experimenting with Attribute Combinations\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a741331f-4897-4c83-80d6-a56349bdbae2", + "metadata": {}, + "outputs": [], + "source": [ + "housing[\"rooms_per_house\"] = housing[\"total_rooms\"] / housing[\"households\"]\n", + "housing[\"bedrooms_ratio\"] = housing[\"total_bedrooms\"] / housing[\"total_rooms\"]\n", + "housing[\"people_per_house\"] = housing[\"population\"] / housing[\"households\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1cf72e63-3bfb-405d-8599-fc0ea463a942", + "metadata": {}, + "outputs": [], + "source": [ + "corr_matrix = housing.corr(numeric_only=True)\n", + "corr_matrix[\"median_house_value\"].sort_values(ascending=False)" + ] + }, + { + "cell_type": "markdown", + "id": "d709cab0-feee-4200-91d3-34ff500de3ed", + "metadata": {}, + "source": [ + "### Prepare the Data for Machine Learning Algorithms\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "2ce7e023-142d-41a8-b853-89c4d55fd428", + "metadata": {}, + "source": [ + "It’s time to prepare the data for your Machine Learning algorithms.\n", + "Instead of doing this manually, you should write functions for this\n", + "purpose, \n", + "\n", + "First revert to a clean training set and separate predictors and labels.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "79e5c576-d5ec-4f3a-bd91-fc51d31923d4", + "metadata": {}, + "outputs": [], + "source": [ + "housing = strat_train_set.drop(\"median_house_value\", axis=1)\n", + "housing_labels = strat_train_set[\"median_house_value\"].copy()" + ] + }, + { + "cell_type": "markdown", + "id": "66be89a5-f458-4e2a-bd32-1f5e6f884b18", + "metadata": {}, + "source": [ + "#### Data Cleaning\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "9ea967d8-ac7d-4f58-8c49-d03736902082", + "metadata": {}, + "source": [ + "Not all data comes perfect for ML as some of them may contain NaN or 0 or\n", + "some other value which you don't want ML algorithm to process.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f70d559d-1f63-4645-bb59-b98f015a2702", + "metadata": {}, + "outputs": [], + "source": [ + "null_rows_idx = housing.isnull().any(axis=1)\n", + "housing.loc[null_rows_idx].head()" + ] + }, + { + "cell_type": "markdown", + "id": "1477ebc7-8a75-4ab7-b0c2-8483c9938add", + "metadata": {}, + "source": [ + "Let's look at three (3) options:\n", + "\n", + "1 - Get rid of the corresponding districts\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5031687-97e8-4675-a859-64e9d21fc95d", + "metadata": {}, + "outputs": [], + "source": [ + "housing_option1 = housing.copy()\n", + "housing_option1.dropna(subset=[\"total_bedrooms\"], inplace=True) # option 1\n", + "housing_option1.loc[null_rows_idx].head()" + ] + }, + { + "cell_type": "markdown", + "id": "8546481f-e20f-4b4c-a4f0-a534025e8172", + "metadata": {}, + "source": [ + "2 - Get rid of the whole attribute\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fad8635e-531c-4504-8645-dee201547f00", + "metadata": {}, + "outputs": [], + "source": [ + "housing_option2 = housing.copy()\n", + "housing_option2.drop(\"total_bedrooms\", axis=1, inplace=True) # option 2\n", + "housing_option2.loc[null_rows_idx].head()" + ] + }, + { + "cell_type": "markdown", + "id": "94f072c4-4ed0-417c-a1a3-08fecef06e0a", + "metadata": {}, + "source": [ + "3 - Set the values to some value (zero, the mean, the median, etc.).\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ee62839-7353-432e-b801-8050d4b3fcbd", + "metadata": {}, + "outputs": [], + "source": [ + "housing_option3 = housing.copy()\n", + "median = housing[\"total_bedrooms\"].median()\n", + "housing_option3[\"total_bedrooms\"].fillna(median, inplace=True) # option 3\n", + "\n", + "housing_option3.loc[null_rows_idx].head()" + ] + }, + { + "cell_type": "markdown", + "id": "971e85ff-6f75-4f8a-a2c1-02a8931a93b1", + "metadata": {}, + "source": [ + "Scikit-Learn provides a handy class to take care of missing values: SimpleImputer.\n", + "\n", + "Separating out the numerical attributes to use the \"median\" strategy\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d59219be-5b14-4ade-95e8-0d0b5f47844d", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.impute import SimpleImputer\n", + "\n", + "imputer = SimpleImputer(strategy=\"median\")" + ] + }, + { + "cell_type": "markdown", + "id": "2ad4d7fc-d009-445f-8c34-d0ee67f0dbc0", + "metadata": {}, + "source": [ + "create a copy of the data without the text attribute oceanproximity:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8e026b51-750a-4edd-999a-014fd466b750", + "metadata": {}, + "outputs": [], + "source": [ + "housing_num = housing.select_dtypes(include=[np.number])" + ] + }, + { + "cell_type": "markdown", + "id": "e4ca43fe-f340-40c7-b364-4489252cd429", + "metadata": {}, + "source": [ + "can fit the imputer instance to the training data using the fit() method:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8327d4e-d2f5-4c4b-b381-55a4bfe358f1", + "metadata": {}, + "outputs": [], + "source": [ + "imputer.fit(housing_num)" + ] + }, + { + "cell_type": "markdown", + "id": "2d2dfbf3-ed67-481d-8609-b783e9c2e5a7", + "metadata": {}, + "source": [ + "we cannot be sure that there won’t be any missing values in new\n", + "data after the system goes live, so it is safer to apply the\n", + "imputer to all the numerical attributes:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b638d41-d4b9-44ce-8790-9544c83c4d9e", + "metadata": {}, + "outputs": [], + "source": [ + "print(imputer.statistics_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f104b66a-a005-4aa5-acc4-d9638443d4c9", + "metadata": {}, + "outputs": [], + "source": [ + "print(housing_num.median().values)" + ] + }, + { + "cell_type": "markdown", + "id": "afd6e8e0-21a2-4e2f-b991-3f97038be8b4", + "metadata": {}, + "source": [ + "use this “trained” imputer to transform the training set by\n", + "replacing missing values with the learned medians:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eede1193-54d3-4894-9173-52331abbaccd", + "metadata": {}, + "outputs": [], + "source": [ + "X = imputer.transform(housing_num)\n", + "# to see the name of the columns\n", + "print(imputer.feature_names_in_)" + ] + }, + { + "cell_type": "markdown", + "id": "8377c4b9-82b6-4709-835e-20e71f954bf0", + "metadata": {}, + "source": [ + "To convert this to pd dataframe:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35926455-4039-4e3a-a05a-0c3a0a394dab", + "metadata": {}, + "outputs": [], + "source": [ + "housing_tr = pd.DataFrame(X, columns=housing_num.columns,\n", + " index=housing_num.index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f9c9605-b99a-48bf-9081-29a21fdb41d2", + "metadata": {}, + "outputs": [], + "source": [ + "housing_tr.loc[null_rows_idx].head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a201fe39-4468-47db-989f-6af1b5cb1447", + "metadata": {}, + "outputs": [], + "source": [ + "imputer.strategy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dae0bd75-7d8d-49a7-a63f-83d0d5167fbb", + "metadata": {}, + "outputs": [], + "source": [ + "housing_tr = pd.DataFrame(X, columns=housing_num.columns,\n", + " index=housing_num.index)\n", + "\n", + "housing_tr.loc[null_rows_idx].head()" + ] + }, + { + "cell_type": "markdown", + "id": "34161f59-0cf7-4895-85b5-69b31747e2ba", + "metadata": {}, + "source": [ + "Time to drop some outliers.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1e1fbc6-61d9-4305-a7b5-a079fb4d222a", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import IsolationForest\n", + "\n", + "isolation_forest = IsolationForest(random_state=42)\n", + "outlier_pred = isolation_forest.fit_predict(X)\n", + "\n", + "print(outlier_pred)" + ] + }, + { + "cell_type": "markdown", + "id": "a043eac3-4da5-430b-963b-4f317970d82b", + "metadata": {}, + "source": [ + "#### Handling Text and Categorical Attributes\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "868b4670-71da-493a-ad1d-0c45f2f6ae1e", + "metadata": {}, + "source": [ + "Time to process text instead of numbers.\n", + "\n", + "In this dataset, there is just one: the oceanproximity attribute.\n", + "Let’s look at its value for the first 10 instances:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c382e678-9186-4d60-8b0a-5d1a0d3878bc", + "metadata": {}, + "outputs": [], + "source": [ + "housing_cat = housing[[\"ocean_proximity\"]]\n", + "housing_cat.head(8)" + ] + }, + { + "cell_type": "markdown", + "id": "776e1ac4-3b25-4df3-96b2-0eb760c2ad15", + "metadata": {}, + "source": [ + "As ML likes numbers instead of text, convert these values to numbers\n", + "using OrdinalEncoder class from Scikit-learn\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8e605ff-0f14-4c61-b945-643294f2c178", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import OrdinalEncoder\n", + "\n", + "ordinal_encoder = OrdinalEncoder()\n", + "housing_cat_encoded = ordinal_encoder.fit_transform(housing_cat)" + ] + }, + { + "cell_type": "markdown", + "id": "b5554519-18fe-4747-a7d0-2bf905ba7d7b", + "metadata": {}, + "source": [ + "Let's see the results\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69a577da-2ef3-4125-8e90-2409b08472c1", + "metadata": {}, + "outputs": [], + "source": [ + "print(housing_cat_encoded[:8])" + ] + }, + { + "cell_type": "markdown", + "id": "21b9dc90-95cc-4b31-b5b1-debe25bf09fd", + "metadata": {}, + "source": [ + "And if we were to see the categories:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fbdaabb5-5f4b-435b-b56b-bcb017621255", + "metadata": {}, + "outputs": [], + "source": [ + "print(ordinal_encoder.categories_)" + ] + }, + { + "cell_type": "markdown", + "id": "e78a4336-e0ed-4077-aa7c-8db3e21592a7", + "metadata": {}, + "source": [ + "To stop from ML algorithm from treating the numbers too literally\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "adc3cf15-d02e-4d2c-a81e-8d2a09590606", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import OneHotEncoder\n", + "\n", + "cat_encoder = OneHotEncoder()\n", + "housing_cat_1hot = cat_encoder.fit_transform(housing_cat)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28bf833b-868d-4226-954d-a098aac5bf56", + "metadata": {}, + "outputs": [], + "source": [ + "type(housing_cat_1hot)" + ] + }, + { + "cell_type": "markdown", + "id": "5409726c-b8c4-4f90-8f83-a276c3fe9010", + "metadata": {}, + "source": [ + "the output is a SciPy sparse matrix, instead of a NumPy array.\n", + "\n", + "convert it to a dense array if needed by calling the toarray() method:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15d076f8-19fe-42a4-8d14-337a638997bc", + "metadata": {}, + "outputs": [], + "source": [ + "print(housing_cat_1hot.toarray())" + ] + }, + { + "cell_type": "markdown", + "id": "f5974873-4982-4e5a-a887-7c9a711e850e", + "metadata": {}, + "source": [ + "It is also possible to list categories using categoreis\\_ instance\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8cbbcae-249d-48c0-8ef8-c4384bba1e6b", + "metadata": {}, + "outputs": [], + "source": [ + "cat_encoder = OneHotEncoder(sparse_output=False)\n", + "housing_cat_1hot = cat_encoder.fit_transform(housing_cat)\n", + "print(housing_cat_1hot)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fcb65ef9-8117-4bb9-af92-c5a2549f3b20", + "metadata": {}, + "outputs": [], + "source": [ + "print(cat_encoder.categories_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4fb09de-a5ef-4ca2-8e2c-f7cdc668f82e", + "metadata": {}, + "outputs": [], + "source": [ + "df_test = pd.DataFrame({\"ocean_proximity\": [\"INLAND\", \"NEAR BAY\"]})\n", + "pd.get_dummies(df_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16e33000-ce4c-4540-acbf-d4766a8cd586", + "metadata": {}, + "outputs": [], + "source": [ + "print(cat_encoder.transform(df_test))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f18bd498-a402-461c-b428-ec61f3c2b5b4", + "metadata": {}, + "outputs": [], + "source": [ + "df_test_unknown = pd.DataFrame({\"ocean_proximity\": [\"<2H OCEAN\", \"ISLAND\"]})\n", + "pd.get_dummies(df_test_unknown)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5775f95-255e-45dd-8b2b-c40d12a9d366", + "metadata": {}, + "outputs": [], + "source": [ + "cat_encoder.handle_unknown = \"ignore\"\n", + "print(cat_encoder.transform(df_test_unknown))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6fa57a8-97c8-4ded-931a-da5e0e30ea84", + "metadata": {}, + "outputs": [], + "source": [ + "print(cat_encoder.feature_names_in_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c293314a-542f-46a3-adb9-4868a85fff5b", + "metadata": {}, + "outputs": [], + "source": [ + "print(cat_encoder.get_feature_names_out())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "305f01a5-f6d7-4a7a-a8c8-9ea5780580e3", + "metadata": {}, + "outputs": [], + "source": [ + "df_output = pd.DataFrame(cat_encoder.transform(df_test_unknown),\n", + " columns=cat_encoder.get_feature_names_out(),\n", + " index=df_test_unknown.index)\n", + "\n", + "df_output" + ] + }, + { + "cell_type": "markdown", + "id": "786f97cc-0d9d-4c4f-af44-561a2d7f3a18", + "metadata": {}, + "source": [ + "#### Feature Scaling\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2800089c-62bd-4524-801e-2c2270d31b9e", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "min_max_scaler = MinMaxScaler(feature_range=(-1, 1))\n", + "housing_num_min_max_scaled = min_max_scaler.fit_transform(housing_num)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aadf51c3-6d72-4e09-8290-62689cb5bdc5", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "std_scaler = StandardScaler()\n", + "housing_num_std_scaled = std_scaler.fit_transform(housing_num)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d62804b-d0f8-47fd-a3ed-d7e9d562e57c", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axs = plt.subplots(1, 2, figsize=(8, 3), sharey=True)\n", + "housing[\"population\"].hist(ax=axs[0], bins=50)\n", + "housing[\"population\"].apply(np.log).hist(ax=axs[1], bins=50)\n", + "axs[0].set_xlabel(\"Population\")\n", + "axs[1].set_xlabel(\"Log of population\")\n", + "axs[0].set_ylabel(\"Number of districts\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c58a3e9-103d-46c4-9e5a-1e8cdd859ba2", + "metadata": {}, + "outputs": [], + "source": [ + "percentiles = [np.percentile(housing[\"median_income\"], p)\n", + " for p in range(1, 100)]\n", + "flattened_median_income = pd.cut(housing[\"median_income\"],\n", + " bins=[-np.inf] + percentiles + [np.inf],\n", + " labels=range(1, 100 + 1))\n", + "flattened_median_income.hist(bins=50)\n", + "plt.xlabel(\"Median income percentile\")\n", + "plt.ylabel(\"Number of districts\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a79c6c63-4e48-48b0-9a1f-7a2182a3f787", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics.pairwise import rbf_kernel\n", + "\n", + "age_simil_35 = rbf_kernel(housing[[\"housing_median_age\"]], [[35]], gamma=0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0df04895-d875-4f33-91de-d03e0e2cef41", + "metadata": {}, + "outputs": [], + "source": [ + "ages = np.linspace(housing[\"housing_median_age\"].min(),\n", + " housing[\"housing_median_age\"].max(),\n", + " 500).reshape(-1, 1)\n", + "gamma1 = 0.1\n", + "gamma2 = 0.03\n", + "rbf1 = rbf_kernel(ages, [[35]], gamma=gamma1)\n", + "rbf2 = rbf_kernel(ages, [[35]], gamma=gamma2)\n", + "\n", + "fig, ax1 = plt.subplots()\n", + "\n", + "ax1.set_xlabel(\"Housing median age\")\n", + "ax1.set_ylabel(\"Number of districts\")\n", + "ax1.hist(housing[\"housing_median_age\"], bins=50)\n", + "\n", + "ax2 = ax1.twinx() # create a twin axis that shares the same x-axis\n", + "color = \"#984ea3\"\n", + "ax2.plot(ages, rbf1, color=color, label=\"gamma = 0.10\")\n", + "ax2.plot(ages, rbf2, color=color, label=\"gamma = 0.03\", linestyle=\"--\")\n", + "ax2.tick_params(axis='y', labelcolor=color)\n", + "ax2.set_ylabel(\"Age similarity\", color=color)\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de7af0e5-b6fd-4d99-acef-4dc37e137d03", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "\n", + "target_scaler = StandardScaler()\n", + "scaled_labels = target_scaler.fit_transform(housing_labels.to_frame())\n", + "\n", + "model = LinearRegression()\n", + "model.fit(housing[[\"median_income\"]], scaled_labels)\n", + "some_new_data = housing[[\"median_income\"]].iloc[:5] # pretend this is new data\n", + "\n", + "scaled_predictions = model.predict(some_new_data)\n", + "predictions = target_scaler.inverse_transform(scaled_predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa1fc2b5-5fa6-4f92-ad08-0b920ec50083", + "metadata": {}, + "outputs": [], + "source": [ + "print(predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5f966b7-7d97-411f-a77e-3bae14958143", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.compose import TransformedTargetRegressor\n", + "\n", + "model = TransformedTargetRegressor(LinearRegression(),\n", + " transformer=StandardScaler())\n", + "model.fit(housing[[\"median_income\"]], housing_labels)\n", + "predictions = model.predict(some_new_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d864ccca-0ee9-4445-94a5-09b8d614fc85", + "metadata": {}, + "outputs": [], + "source": [ + "print(predictions)" + ] + }, + { + "cell_type": "markdown", + "id": "1f232151-d01a-4774-9251-04e8c03f180d", + "metadata": {}, + "source": [ + "#### Custom Transformers\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b07e3a15-b90d-457b-81d6-4023269d955c", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import FunctionTransformer\n", + "\n", + "log_transformer = FunctionTransformer(np.log, inverse_func=np.exp)\n", + "log_pop = log_transformer.transform(housing[[\"population\"]])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "816433a9-1c22-48a5-8b9a-f76687f2e39c", + "metadata": {}, + "outputs": [], + "source": [ + "rbf_transformer = FunctionTransformer(rbf_kernel,\n", + " kw_args=dict(Y=[[35.]], gamma=0.1))\n", + "age_simil_35 = rbf_transformer.transform(housing[[\"housing_median_age\"]])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2adc7904-9cf7-4fa4-8f40-e41f409354e8", + "metadata": {}, + "outputs": [], + "source": [ + "print(age_simil_35)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a269320-ccd2-4b39-9cc5-1bae514b83ed", + "metadata": {}, + "outputs": [], + "source": [ + "sf_coords = 37.7749, -122.41\n", + "sf_transformer = FunctionTransformer(rbf_kernel,\n", + " kw_args=dict(Y=[sf_coords], gamma=0.1))\n", + "sf_simil = sf_transformer.transform(housing[[\"latitude\", \"longitude\"]])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "316b2462-473d-4dfd-96cd-18ee1e0c53ef", + "metadata": {}, + "outputs": [], + "source": [ + "print(sf_simil)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd79f899-1f9e-445f-8376-d01d8a011a44", + "metadata": {}, + "outputs": [], + "source": [ + "ratio_transformer = FunctionTransformer(lambda X: X[:, [0]] / X[:, [1]])\n", + "print(ratio_transformer.transform(np.array([[1., 2.], [3., 4.]])))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5581967c-3da2-4791-946c-1cb1216640d9", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.base import BaseEstimator, TransformerMixin\n", + "from sklearn.utils.validation import check_array, check_is_fitted\n", + "\n", + "class StandardScalerClone(BaseEstimator, TransformerMixin):\n", + " def __init__(self, with_mean=True): # no *args or **kwargs!\n", + " self.with_mean = with_mean\n", + "\n", + " def fit(self, X, y=None): # y is required even though we don't use it\n", + " X = check_array(X) # checks that X is an array with finite float values\n", + " self.mean_ = X.mean(axis=0)\n", + " self.scale_ = X.std(axis=0)\n", + " self.n_features_in_ = X.shape[1] # every estimator stores this in fit()\n", + " return self # always return self!\n", + "\n", + " def transform(self, X):\n", + " check_is_fitted(self) # looks for learned attributes (with trailing _)\n", + " X = check_array(X)\n", + " assert self.n_features_in_ == X.shape[1]\n", + " if self.with_mean:\n", + " X = X - self.mean_\n", + " return X / self.scale_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c552071c-484b-4eb3-9756-804a1d956e32", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.cluster import KMeans\n", + "\n", + "class ClusterSimilarity(BaseEstimator, TransformerMixin):\n", + " def __init__(self, n_clusters=10, gamma=1.0, random_state=None):\n", + " self.n_clusters = n_clusters\n", + " self.gamma = gamma\n", + " self.random_state = random_state\n", + "\n", + " def fit(self, X, y=None, sample_weight=None):\n", + " self.kmeans_ = KMeans(self.n_clusters, n_init=10,\n", + " random_state=self.random_state)\n", + " self.kmeans_.fit(X, sample_weight=sample_weight)\n", + " return self # always return self!\n", + "\n", + " def transform(self, X):\n", + " return rbf_kernel(X, self.kmeans_.cluster_centers_, gamma=self.gamma)\n", + " \n", + " def get_feature_names_out(self, names=None):\n", + " return [f\"Cluster {i} similarity\" for i in range(self.n_clusters)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f038bb3-5e28-4f6d-809f-ffd98f838bdd", + "metadata": {}, + "outputs": [], + "source": [ + "cluster_simil = ClusterSimilarity(n_clusters=10, gamma=1., random_state=42)\n", + "similarities = cluster_simil.fit_transform(housing[[\"latitude\", \"longitude\"]],\n", + " sample_weight=housing_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6490997-b899-4668-8580-1ebfa17ed7f8", + "metadata": {}, + "outputs": [], + "source": [ + "similarities[:3].round(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e849563b-8faa-488b-963a-fb0cecaee3d8", + "metadata": {}, + "outputs": [], + "source": [ + "housing_renamed = housing.rename(columns={\n", + " \"latitude\": \"Latitude\", \"longitude\": \"Longitude\",\n", + " \"population\": \"Population\",\n", + " \"median_house_value\": \"Median house value (ᴜsᴅ)\"})\n", + "housing_renamed[\"Max cluster similarity\"] = similarities.max(axis=1)\n", + "\n", + "housing_renamed.plot(kind=\"scatter\", x=\"Longitude\", y=\"Latitude\", grid=True,\n", + " s=housing_renamed[\"Population\"] / 100, label=\"Population\",\n", + " c=\"Max cluster similarity\",\n", + " cmap=\"jet\", colorbar=True,\n", + " legend=True, sharex=False, figsize=(10, 7))\n", + "plt.plot(cluster_simil.kmeans_.cluster_centers_[:, 1],\n", + " cluster_simil.kmeans_.cluster_centers_[:, 0],\n", + " linestyle=\"\", color=\"black\", marker=\"X\", markersize=20,\n", + " label=\"Cluster centers\")\n", + "plt.legend(loc=\"upper right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "067475ef-fc90-4fb5-9aed-5027d9c9f199", + "metadata": {}, + "source": [ + "#### Transformation Pipelines\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "ba1744aa-9080-45d9-a596-788bc6c11b4b", + "metadata": {}, + "source": [ + "Now let's build a pipeline to preprocess the numerical attributes:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32a7400d-2120-46a2-b131-188d3ad89bbc", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.pipeline import Pipeline\n", + "\n", + "num_pipeline = Pipeline([\n", + " (\"impute\", SimpleImputer(strategy=\"median\")),\n", + " (\"standardize\", StandardScaler()),\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dbcbd78f-b5b1-451a-8b9f-455fd801f652", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.pipeline import make_pipeline\n", + "\n", + "num_pipeline = make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "72f4d8e7-55de-4abf-bfcd-e8e4d3a8adee", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import set_config\n", + "\n", + "set_config(display='diagram')\n", + "\n", + "num_pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "00536211-5305-41d5-b474-bd715989b0b1", + "metadata": {}, + "outputs": [], + "source": [ + "housing_num_prepared = num_pipeline.fit_transform(housing_num)\n", + "print(housing_num_prepared[:2].round(2))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04985d06-146f-4fe3-a8b3-8bba2e0d2439", + "metadata": {}, + "outputs": [], + "source": [ + "def monkey_patch_get_signature_names_out():\n", + " \"\"\"Monkey patch some classes which did not handle get_feature_names_out()\n", + " correctly in Scikit-Learn 1.0.*.\"\"\"\n", + " from inspect import Signature, signature, Parameter\n", + " import pandas as pd\n", + " from sklearn.impute import SimpleImputer\n", + " from sklearn.pipeline import make_pipeline, Pipeline\n", + " from sklearn.preprocessing import FunctionTransformer, StandardScaler\n", + "\n", + " default_get_feature_names_out = StandardScaler.get_feature_names_out\n", + "\n", + " if not hasattr(SimpleImputer, \"get_feature_names_out\"):\n", + " print(\"Monkey-patching SimpleImputer.get_feature_names_out()\")\n", + " SimpleImputer.get_feature_names_out = default_get_feature_names_out\n", + "\n", + " if not hasattr(FunctionTransformer, \"get_feature_names_out\"):\n", + " print(\"Monkey-patching FunctionTransformer.get_feature_names_out()\")\n", + " orig_init = FunctionTransformer.__init__\n", + " orig_sig = signature(orig_init)\n", + "\n", + " def __init__(*args, feature_names_out=None, **kwargs):\n", + " orig_sig.bind(*args, **kwargs)\n", + " orig_init(*args, **kwargs)\n", + " args[0].feature_names_out = feature_names_out\n", + "\n", + " __init__.__signature__ = Signature(\n", + " list(signature(orig_init).parameters.values()) + [\n", + " Parameter(\"feature_names_out\", Parameter.KEYWORD_ONLY)])\n", + "\n", + " def get_feature_names_out(self, names=None):\n", + " if callable(self.feature_names_out):\n", + " return self.feature_names_out(self, names)\n", + " assert self.feature_names_out == \"one-to-one\"\n", + " return default_get_feature_names_out(self, names)\n", + "\n", + " FunctionTransformer.__init__ = __init__\n", + " FunctionTransformer.get_feature_names_out = get_feature_names_out\n", + "\n", + "monkey_patch_get_signature_names_out()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f3b8b88-9c36-41f3-baa3-79488ed1a4d7", + "metadata": {}, + "outputs": [], + "source": [ + "df_housing_num_prepared = pd.DataFrame(\n", + " housing_num_prepared, columns=num_pipeline.get_feature_names_out(),\n", + " index=housing_num.index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3e175a94-5299-445a-9976-1d9efb9c1682", + "metadata": {}, + "outputs": [], + "source": [ + "df_housing_num_prepared.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0382feb8-2aa7-46d8-92f6-a1e4c8cc922e", + "metadata": {}, + "outputs": [], + "source": [ + "print(num_pipeline.steps)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ac244b34-2588-49d2-80cd-8f6c8d9f7693", + "metadata": {}, + "outputs": [], + "source": [ + "num_pipeline[1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85cd4313-655c-48e9-8e17-e7d839f56382", + "metadata": {}, + "outputs": [], + "source": [ + "num_pipeline[:-1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f73e775-8fa2-4995-a9b8-c7e971102482", + "metadata": {}, + "outputs": [], + "source": [ + "num_pipeline.named_steps[\"simpleimputer\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e7a399d-02de-4ced-ac17-f807a8d0f632", + "metadata": {}, + "outputs": [], + "source": [ + "num_pipeline.set_params(simpleimputer__strategy=\"median\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebf56adc-409f-4341-a499-8a68dc62635f", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.compose import ColumnTransformer\n", + "\n", + "num_attribs = [\"longitude\", \"latitude\", \"housing_median_age\", \"total_rooms\",\n", + " \"total_bedrooms\", \"population\", \"households\", \"median_income\"]\n", + "cat_attribs = [\"ocean_proximity\"]\n", + "\n", + "cat_pipeline = make_pipeline(\n", + " SimpleImputer(strategy=\"most_frequent\"),\n", + " OneHotEncoder(handle_unknown=\"ignore\"))\n", + "\n", + "preprocessing = ColumnTransformer([\n", + " (\"num\", num_pipeline, num_attribs),\n", + " (\"cat\", cat_pipeline, cat_attribs),\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75e298ee-9ce7-472a-8aa5-f52b8bf68033", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.compose import make_column_selector, make_column_transformer\n", + "\n", + "preprocessing = make_column_transformer(\n", + " (num_pipeline, make_column_selector(dtype_include=np.number)),\n", + " (cat_pipeline, make_column_selector(dtype_include=object)),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc9c70d3-91b0-4c74-8950-a083dd7c66d2", + "metadata": {}, + "outputs": [], + "source": [ + "housing_prepared = preprocessing.fit_transform(housing)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b280062-a710-43fa-be9d-7591221653fd", + "metadata": {}, + "outputs": [], + "source": [ + "housing_prepared_fr = pd.DataFrame(\n", + " housing_prepared,\n", + " columns=preprocessing.get_feature_names_out(),\n", + " index=housing.index)\n", + "housing_prepared_fr.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b2aa392-4de1-4840-a78a-2baa38d24924", + "metadata": {}, + "outputs": [], + "source": [ + "def column_ratio(X):\n", + " return X[:, [0]] / X[:, [1]]\n", + "\n", + "def ratio_name(function_transformer, feature_names_in):\n", + " return [\"ratio\"] # feature names out\n", + "\n", + "def ratio_pipeline():\n", + " return make_pipeline(\n", + " SimpleImputer(strategy=\"median\"),\n", + " FunctionTransformer(column_ratio, feature_names_out=ratio_name),\n", + " StandardScaler())\n", + "\n", + "log_pipeline = make_pipeline(\n", + " SimpleImputer(strategy=\"median\"),\n", + " FunctionTransformer(np.log, feature_names_out=\"one-to-one\"),\n", + " StandardScaler())\n", + "cluster_simil = ClusterSimilarity(n_clusters=10, gamma=1., random_state=42)\n", + "default_num_pipeline = make_pipeline(SimpleImputer(strategy=\"median\"),\n", + " StandardScaler())\n", + "preprocessing = ColumnTransformer([\n", + " (\"bedrooms\", ratio_pipeline(), [\"total_bedrooms\", \"total_rooms\"]),\n", + " (\"rooms_per_house\", ratio_pipeline(), [\"total_rooms\", \"households\"]),\n", + " (\"people_per_house\", ratio_pipeline(), [\"population\", \"households\"]),\n", + " (\"log\", log_pipeline, [\"total_bedrooms\", \"total_rooms\", \"population\",\n", + " \"households\", \"median_income\"]),\n", + " (\"geo\", cluster_simil, [\"latitude\", \"longitude\"]),\n", + " (\"cat\", cat_pipeline, make_column_selector(dtype_include=object)),\n", + " ],\n", + " remainder=default_num_pipeline) # one column remaining: housing_median_age" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4bb66e19-1c22-4a9e-8617-f29f78bdc928", + "metadata": {}, + "outputs": [], + "source": [ + "housing_prepared = preprocessing.fit_transform(housing)\n", + "print(housing_prepared.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32482f88-40f9-4a2d-b103-392a0d11a2ba", + "metadata": {}, + "outputs": [], + "source": [ + "print(preprocessing.get_feature_names_out())" + ] + }, + { + "cell_type": "markdown", + "id": "ee6db9de-ccee-443c-8bb5-07dbbd547a38", + "metadata": {}, + "source": [ + "### Select and Train a Model\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "b83b0075-8252-4707-92fc-e64a979d1bef", + "metadata": {}, + "source": [ + "#### Training and Evaluating on the Training Set\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2dc52557-7d68-4f98-a1ca-9e85b570a0f4", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "\n", + "lin_reg = make_pipeline(preprocessing, LinearRegression())\n", + "print(lin_reg.fit(housing, housing_labels))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1abc833c-4fde-4e6a-9023-01afe0e43cc0", + "metadata": {}, + "outputs": [], + "source": [ + "housing_predictions = lin_reg.predict(housing)\n", + "print(housing_predictions[:5].round(-2)) # -2 = rounded to the nearest hundred" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da7c88f2-f2cc-4106-bb07-0490673fb254", + "metadata": {}, + "outputs": [], + "source": [ + "print(housing_labels.iloc[:5].values)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39b93942-9a0e-484a-a4fe-f24ba4731e8b", + "metadata": {}, + "outputs": [], + "source": [ + "error_ratios = housing_predictions[:5].round(-2) / housing_labels.iloc[:5].values - 1\n", + "print(\", \".join([f\"{100 * ratio:.1f}%\" for ratio in error_ratios]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1e8aa10-b882-4303-bec7-01c2291953e2", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import mean_squared_error\n", + "\n", + "lin_rmse = mean_squared_error(housing_labels, housing_predictions,\n", + " squared=False)\n", + "lin_rmse" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46c41cfa-962e-4a12-9c09-f5e2c61fa016", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeRegressor\n", + "\n", + "tree_reg = make_pipeline(preprocessing, DecisionTreeRegressor(random_state=42))\n", + "tree_reg.fit(housing, housing_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57858028-d778-4785-b7ef-779178859725", + "metadata": {}, + "outputs": [], + "source": [ + "housing_predictions = tree_reg.predict(housing)\n", + "tree_rmse = mean_squared_error(housing_labels, housing_predictions,\n", + " squared=False)\n", + "tree_rmse" + ] + }, + { + "cell_type": "markdown", + "id": "0fc17b3a-46cd-4b91-8be4-a510058f0b92", + "metadata": {}, + "source": [ + "#### Better Evaluation using Cross-Validation\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "db6efa5a-2117-43e5-9575-d569ae080fd4", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import cross_val_score\n", + "\n", + "tree_rmses = -cross_val_score(tree_reg, housing, housing_labels,\n", + " scoring=\"neg_root_mean_squared_error\", cv=10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b786c64c-ab64-42e6-81b0-daa80d8f653b", + "metadata": {}, + "outputs": [], + "source": [ + "pd.Series(tree_rmses).describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "52b5e478-7b85-4e9f-9c09-13e9bf7eec28", + "metadata": {}, + "outputs": [], + "source": [ + "lin_rmses = -cross_val_score(lin_reg, housing, housing_labels,\n", + " scoring=\"neg_root_mean_squared_error\", cv=10)\n", + "pd.Series(lin_rmses).describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4ad4d26-08fb-4ffc-9a53-d65acd4afbd7", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestRegressor\n", + "\n", + "forest_reg = make_pipeline(preprocessing,\n", + " RandomForestRegressor(random_state=42))\n", + "forest_rmses = -cross_val_score(forest_reg, housing, housing_labels,\n", + " scoring=\"neg_root_mean_squared_error\", cv=10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c95c198a-6ed0-4898-8242-ef9ba889481b", + "metadata": {}, + "outputs": [], + "source": [ + "pd.Series(forest_rmses).describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16a009b8-7684-4214-bb33-ce4320164df8", + "metadata": {}, + "outputs": [], + "source": [ + "forest_reg.fit(housing, housing_labels)\n", + "housing_predictions = forest_reg.predict(housing)\n", + "forest_rmse = mean_squared_error(housing_labels, housing_predictions,\n", + " squared=False)\n", + "forest_rmse" + ] + }, + { + "cell_type": "markdown", + "id": "034c105a-3d6c-4e0f-84a1-0b5d11ac9d2a", + "metadata": {}, + "source": [ + "### Fine-Tuning the Model\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "a7dcf159-453d-465e-be00-3265b734bd50", + "metadata": {}, + "source": [ + "#### Using Grid Search\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b3a3de3-0979-4986-9d55-d2622216c32b", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "full_pipeline = Pipeline([\n", + " (\"preprocessing\", preprocessing),\n", + " (\"random_forest\", RandomForestRegressor(random_state=42)),\n", + "])\n", + "param_grid = [\n", + " {'preprocessing__geo__n_clusters': [5, 8, 10],\n", + " 'random_forest__max_features': [4, 6, 8]},\n", + " {'preprocessing__geo__n_clusters': [10, 15],\n", + " 'random_forest__max_features': [6, 8, 10]},\n", + "]\n", + "grid_search = GridSearchCV(full_pipeline, param_grid, cv=3,\n", + " scoring='neg_root_mean_squared_error')\n", + "grid_search.fit(housing, housing_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9dd5bf3b-a3db-4e48-b2b5-e3a16ecaadbe", + "metadata": {}, + "outputs": [], + "source": [ + "print(str(full_pipeline.get_params().keys())[:1000] + \"...\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38543cc7-f06d-497b-8d2e-31c36d35e4b3", + "metadata": {}, + "outputs": [], + "source": [ + "print(grid_search.best_params_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2795fed-0b7c-4121-aa56-fd911614fdfc", + "metadata": {}, + "outputs": [], + "source": [ + "grid_search.best_estimator_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5911c074-d555-443f-9104-abd31b380d48", + "metadata": {}, + "outputs": [], + "source": [ + "cv_res = pd.DataFrame(grid_search.cv_results_)\n", + "cv_res.sort_values(by=\"mean_test_score\", ascending=False, inplace=True)\n", + "\n", + "# extra code – these few lines of code just make the DataFrame look nicer\n", + "cv_res = cv_res[[\"param_preprocessing__geo__n_clusters\",\n", + " \"param_random_forest__max_features\", \"split0_test_score\",\n", + " \"split1_test_score\", \"split2_test_score\", \"mean_test_score\"]]\n", + "score_cols = [\"split0\", \"split1\", \"split2\", \"mean_test_rmse\"]\n", + "cv_res.columns = [\"n_clusters\", \"max_features\"] + score_cols\n", + "cv_res[score_cols] = -cv_res[score_cols].round().astype(np.int64)\n", + "\n", + "cv_res.head()" + ] + }, + { + "cell_type": "markdown", + "id": "f162278b-78a1-49d0-8429-42094c839748", + "metadata": {}, + "source": [ + "#### Randomised Search\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d8c8499-47d6-428f-89ac-eb0a50c72a81", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.experimental import enable_halving_search_cv\n", + "from sklearn.model_selection import HalvingRandomSearchCV" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21b04a70-c9c1-4042-9e7c-64f8a7d3a8da", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import RandomizedSearchCV\n", + "from scipy.stats import randint\n", + "\n", + "param_distribs = {'preprocessing__geo__n_clusters': randint(low=3, high=50),\n", + " 'random_forest__max_features': randint(low=2, high=20)}\n", + "\n", + "rnd_search = RandomizedSearchCV(\n", + " full_pipeline, param_distributions=param_distribs, n_iter=10, cv=3,\n", + " scoring='neg_root_mean_squared_error', random_state=42)\n", + "\n", + "rnd_search.fit(housing, housing_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "06ab70b6-ef41-485b-a7e2-c7e3a3668087", + "metadata": {}, + "outputs": [], + "source": [ + "cv_res = pd.DataFrame(rnd_search.cv_results_)\n", + "cv_res.sort_values(by=\"mean_test_score\", ascending=False, inplace=True)\n", + "cv_res = cv_res[[\"param_preprocessing__geo__n_clusters\",\n", + " \"param_random_forest__max_features\", \"split0_test_score\",\n", + " \"split1_test_score\", \"split2_test_score\", \"mean_test_score\"]]\n", + "cv_res.columns = [\"n_clusters\", \"max_features\"] + score_cols\n", + "cv_res[score_cols] = -cv_res[score_cols].round().astype(np.int64)\n", + "cv_res.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "76b00b41-5d0f-4d66-a5a0-ea2277425b25", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import randint, uniform, geom, expon\n", + "\n", + "xs1 = np.arange(0, 7 + 1)\n", + "randint_distrib = randint(0, 7 + 1).pmf(xs1)\n", + "\n", + "xs2 = np.linspace(0, 7, 500)\n", + "uniform_distrib = uniform(0, 7).pdf(xs2)\n", + "\n", + "xs3 = np.arange(0, 7 + 1)\n", + "geom_distrib = geom(0.5).pmf(xs3)\n", + "\n", + "xs4 = np.linspace(0, 7, 500)\n", + "expon_distrib = expon(scale=1).pdf(xs4)\n", + "\n", + "plt.figure(figsize=(12, 7))\n", + "\n", + "plt.subplot(2, 2, 1)\n", + "plt.bar(xs1, randint_distrib, label=\"scipy.randint(0, 7 + 1)\")\n", + "plt.ylabel(\"Probability\")\n", + "plt.legend()\n", + "plt.axis([-1, 8, 0, 0.2])\n", + "\n", + "plt.subplot(2, 2, 2)\n", + "plt.fill_between(xs2, uniform_distrib, label=\"scipy.uniform(0, 7)\")\n", + "plt.ylabel(\"PDF\")\n", + "plt.legend()\n", + "plt.axis([-1, 8, 0, 0.2])\n", + "\n", + "plt.subplot(2, 2, 3)\n", + "plt.bar(xs3, geom_distrib, label=\"scipy.geom(0.5)\")\n", + "plt.xlabel(\"Hyperparameter value\")\n", + "plt.ylabel(\"Probability\")\n", + "plt.legend()\n", + "plt.axis([0, 7, 0, 1])\n", + "\n", + "plt.subplot(2, 2, 4)\n", + "plt.fill_between(xs4, expon_distrib, label=\"scipy.expon(scale=1)\")\n", + "plt.xlabel(\"Hyperparameter value\")\n", + "plt.ylabel(\"PDF\")\n", + "plt.legend()\n", + "plt.axis([0, 7, 0, 1])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4fa6393d-6663-42f3-a186-d36ca85d3f88", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import loguniform\n", + "\n", + "xs1 = np.linspace(0, 7, 500)\n", + "expon_distrib = expon(scale=1).pdf(xs1)\n", + "\n", + "log_xs2 = np.linspace(-5, 3, 500)\n", + "log_expon_distrib = np.exp(log_xs2 - np.exp(log_xs2))\n", + "\n", + "xs3 = np.linspace(0.001, 1000, 500)\n", + "loguniform_distrib = loguniform(0.001, 1000).pdf(xs3)\n", + "\n", + "log_xs4 = np.linspace(np.log(0.001), np.log(1000), 500)\n", + "log_loguniform_distrib = uniform(np.log(0.001), np.log(1000)).pdf(log_xs4)\n", + "\n", + "plt.figure(figsize=(12, 7))\n", + "\n", + "plt.subplot(2, 2, 1)\n", + "plt.fill_between(xs1, expon_distrib,\n", + " label=\"scipy.expon(scale=1)\")\n", + "plt.ylabel(\"PDF\")\n", + "plt.legend()\n", + "plt.axis([0, 7, 0, 1])\n", + "\n", + "plt.subplot(2, 2, 2)\n", + "plt.fill_between(log_xs2, log_expon_distrib,\n", + " label=\"log(X) with X ~ expon\")\n", + "plt.legend()\n", + "plt.axis([-5, 3, 0, 1])\n", + "\n", + "plt.subplot(2, 2, 3)\n", + "plt.fill_between(xs3, loguniform_distrib,\n", + " label=\"scipy.loguniform(0.001, 1000)\")\n", + "plt.xlabel(\"Hyperparameter value\")\n", + "plt.ylabel(\"PDF\")\n", + "plt.legend()\n", + "plt.axis([0.001, 1000, 0, 0.005])\n", + "\n", + "plt.subplot(2, 2, 4)\n", + "plt.fill_between(log_xs4, log_loguniform_distrib,\n", + " label=\"log(X) with X ~ loguniform\")\n", + "plt.xlabel(\"Log of hyperparameter value\")\n", + "plt.legend()\n", + "plt.axis([-8, 1, 0, 0.2])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "88d29386-6722-4a79-a803-5259dbacbbe3", + "metadata": {}, + "source": [ + "#### Analyse the Best Models and Errors\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b4f1a138-0e7b-4e6d-bc9c-e062eb4ca362", + "metadata": {}, + "outputs": [], + "source": [ + "final_model = rnd_search.best_estimator_ # includes preprocessing\n", + "feature_importances = final_model[\"random_forest\"].feature_importances_\n", + "print(feature_importances.round(2))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "60f41b49-acda-4e6a-ac3d-100bf87fe9ec", + "metadata": {}, + "outputs": [], + "source": [ + "sorted(zip(feature_importances,\n", + " final_model[\"preprocessing\"].get_feature_names_out()),\n", + " reverse=True)" + ] + }, + { + "cell_type": "markdown", + "id": "d7cc711a-5ce7-4a25-845a-2a7807a3e07c", + "metadata": {}, + "source": [ + "\n", + "| 0.19087378222226137|log_medianincome|\n", + "| 0.07625632853052883|cat_oceanproximityINLAND|\n", + "| 0.06365028932207333|bedrooms_ratio|\n", + "| 0.057834740538722625|roomsperhouse_ratio|\n", + "| 0.04907003277818634|peopleperhouse_ratio|\n", + "| 0.038165489600129165|geo_Cluster 3 similarity|\n", + "| 0.025700861301416925|geo_Cluster 22 similarity|\n", + "| 0.02186407550147744|geo_Cluster 17 similarity|\n", + "| 0.021818299311019237|geo_Cluster 6 similarity|\n", + "| 0.018249904787654904|geo_Cluster 2 similarity|\n", + "| 0.017263517651784216|geo_Cluster 32 similarity|\n", + "| 0.015649725317935348|geo_Cluster 18 similarity|\n", + "| 0.015236556682888558|geo_Cluster 40 similarity|\n", + "| 0.014160249342841777|geo_Cluster 43 similarity|\n", + "| 0.014113856232349186|geo_Cluster 7 similarity|\n", + "| 0.013968406769681294|geo_Cluster 21 similarity|\n", + "| 0.013781633271007265|geo_Cluster 38 similarity|\n", + "| 0.013515022744382842|geo_Cluster 34 similarity|\n", + "| 0.013508738042902313|geo_Cluster 41 similarity|\n", + "| 0.012844820424121687|geo_Cluster 24 similarity|\n", + "| 0.01236427981858226|geo_Cluster 10 similarity|\n", + "| 0.01176408158393247|remainder_housingmedianage|\n", + "| 0.011436849025886087|geo_Cluster 31 similarity|\n", + "| 0.011430032718708965|geo_Cluster 30 similarity|\n", + "| 0.011262888671999243|geo_Cluster 42 similarity|\n", + "| 0.011082126500672662|geo_Cluster 16 similarity|\n", + "| 0.01087991522984511|geo_Cluster 1 similarity|\n", + "| 0.0106352262787596|geo_Cluster 25 similarity|\n", + "| 0.010629636976156976|geo_Cluster 26 similarity|\n", + "| 0.010325438106958176|geo_Cluster 20 similarity|\n", + "| 0.009978597341631139|geo_Cluster 35 similarity|\n", + "| 0.009811902116084414|geo_Cluster 14 similarity|\n", + "| 0.00926835411026417|geo_Cluster 39 similarity|\n", + "| 0.009210910491673824|geo_Cluster 37 similarity|\n", + "| 0.008838219938405523|geo_Cluster 0 similarity|\n", + "| 0.00883623406533351|geo_Cluster 9 similarity|\n", + "| 0.008743931217845727|geo_Cluster 8 similarity|\n", + "| 0.008563362393325231|geo_Cluster 36 similarity|\n", + "| 0.008465719960196051|geo_Cluster 28 similarity|\n", + "| 0.008001576292023282|geo_Cluster 44 similarity|\n", + "| 0.007942690751495287|geo_Cluster 4 similarity|\n", + "| 0.00792848112158647|geo_Cluster 11 similarity|\n", + "| 0.007724755678419276|log_totalrooms|\n", + "| 0.0071161520040372486|log_population|\n", + "| 0.006792638958967365|log_totalbedrooms|\n", + "| 0.006504955481581277|log_households|\n", + "| 0.006215189140929538|geo_Cluster 23 similarity|\n", + "| 0.0056299034599644185|geo_Cluster 19 similarity|\n", + "| 0.005544728303210014|geo_Cluster 27 similarity|\n", + "| 0.00526201040506395|geo_Cluster 33 similarity|\n", + "| 0.004834036365364593|geo_Cluster 15 similarity|\n", + "| 0.004177160622478634|geo_Cluster 12 similarity|\n", + "| 0.0040378619656822245|geo_Cluster 13 similarity|\n", + "| 0.0036513301086410445|geo_Cluster 29 similarity|\n", + "| 0.0033595680753400604|cat_oceanproximity_<1H OCEAN|\n", + "| 0.001969988014822343|geo_Cluster 5 similarity|\n", + "| 0.001955486511135513|cat_oceanproximityNEAR OCEAN|\n", + "| 0.0002362031046240973|cat_oceanproximityNEAR BAY|\n", + "| 6.124671500751693e-05|cat_oceanproximityISLAND|\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "d50ca333-5ace-4b7b-935e-0f2824e04a71", + "metadata": {}, + "source": [ + "#### Evaluate using the Test Set\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "33fa25f5-a166-4d97-a169-724db63fa18f", + "metadata": {}, + "source": [ + "Time to evaluate the model with the test set:\n", + "\n", + "1. get the predictors and the labels from the test set,\n", + "2. run finalmode to transform data,\n", + "3. make predictions,\n", + "4. evaluate the predictions.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f291023-2da8-4413-b0fd-a4f319aa7a39", + "metadata": {}, + "outputs": [], + "source": [ + "X_test = strat_test_set.drop(\"median_house_value\", axis=1)\n", + "y_test = strat_test_set[\"median_house_value\"].copy()\n", + "\n", + "final_predictions = final_model.predict(X_test)\n", + "\n", + "final_rmse = mean_squared_error(y_test, final_predictions, squared=False)\n", + "final_rmse" + ] + }, + { + "cell_type": "markdown", + "id": "0b8a089a-3c5b-4be8-a8c1-21a5fb729c1e", + "metadata": {}, + "source": [ + "We need to know How good is the model. For this, compute\n", + "the %95 [confidence interval](https://en.wikipedia.org/wiki/Confidence_interval) for the generalisation error using\n", + "scipy.stats.t.interval().\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b831bad-dbd3-485f-ada2-4c60c1365913", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy import stats\n", + "\n", + "confidence = 0.95\n", + "squared_errors = (final_predictions - y_test) ** 2\n", + "result= np.sqrt(stats.t.interval(confidence, len(squared_errors) - 1,\n", + " loc=squared_errors.mean(),\n", + " scale=stats.sem(squared_errors)))\n", + "print(result)" + ] + }, + { + "cell_type": "markdown", + "id": "6543d7d2-e7e1-4566-92f7-7a5e87f89781", + "metadata": {}, + "source": [ + "The value is roughly in the middle of it.\n", + "\n", + "There are different ways of calculating it as well.\n", + "\n", + "Below is the manual way of writing the confidence interval.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e6691159-a1e0-488d-88d9-2512c98ac83c", + "metadata": {}, + "outputs": [], + "source": [ + "m = len(squared_errors)\n", + "mean = squared_errors.mean()\n", + "tscore = stats.t.ppf((1 + confidence) / 2, df=m - 1)\n", + "tmargin = tscore * squared_errors.std(ddof=1) / np.sqrt(m)\n", + "print(np.sqrt(mean - tmargin), np.sqrt(mean + tmargin))" + ] + }, + { + "cell_type": "markdown", + "id": "6c413639-1623-493b-85ea-84c8212e4f87", + "metadata": {}, + "source": [ + "Alternatively, we can use z-score (i.e., [Standard score](https://en.wikipedia.org/wiki/Standard_score)) to calculate as the\n", + "data set is relatively big.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9bff0745-66b5-416e-a9d3-506e58854d01", + "metadata": {}, + "outputs": [], + "source": [ + "zscore = stats.norm.ppf((1 + confidence) / 2)\n", + "zmargin = zscore * squared_errors.std(ddof=1) / np.sqrt(m)\n", + "print(np.sqrt(mean - zmargin), np.sqrt(mean + zmargin))" + ] + }, + { + "cell_type": "markdown", + "id": "650c46be-5d23-4e07-bf32-a2d1659bf0d0", + "metadata": {}, + "source": [ + "NOTE: Doing a lot of hyperparameter tuning can make the model behave worse as\n", + "it would be highly tuned to the train data and may not be as performant as on test data.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "35fb2060-961c-4098-94a5-2c66cc3b6ed9", + "metadata": {}, + "source": [ + "#### Saving the Model\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "90b21863-50ee-43da-8f99-9c47aaa3bbcb", + "metadata": {}, + "source": [ + "Time to get the model into production and the easiest way to do is to save the best model.\n", + "\n", + "To save it, you can use the joblib module [[More Info]​](https://joblib.readthedocs.io/en/stable/), which allows pipelining.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a98f2311-6452-4c28-b1e4-79bf192757c0", + "metadata": {}, + "outputs": [], + "source": [ + "import joblib\n", + "\n", + "joblib.dump(final_model, \"my_california_housing_model.pkl\")" + ] + }, + { + "cell_type": "markdown", + "id": "7e1164be-b3dd-4234-a344-0e12d84c38a4", + "metadata": {}, + "source": [ + "Once saved, you can load and use it, with all the necessary dependencies loaded.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "074cb001-a603-48c1-af2e-80d44131be93", + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'my_california_housing_model.pkl'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/c2/_hry6n9d5v1527pv4f_gfjxm0000gn/T/ipykernel_76668/3010287116.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;31m# [...]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mfinal_model_reloaded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjoblib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"my_california_housing_model.pkl\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhousing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# pretend these are new districts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/joblib/numpy_pickle.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(filename, mmap_mode)\u001b[0m\n\u001b[1;32m 577\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_unpickle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 578\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 579\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 580\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0m_read_fileobject\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmmap_mode\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfobj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 581\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'my_california_housing_model.pkl'" + ] + } + ], + "source": [ + "import joblib\n", + "\n", + "# extra code – excluded for conciseness\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.base import BaseEstimator, TransformerMixin\n", + "from sklearn.metrics.pairwise import rbf_kernel\n", + "\n", + "def column_ratio(X):\n", + " return X[:, [0]] / X[:, [1]]\n", + "\n", + "#class ClusterSimilarity(BaseEstimator, TransformerMixin):\n", + "# [...]\n", + "\n", + "final_model_reloaded = joblib.load(\"my_california_housing_model.pkl\")\n", + "\n", + "new_data = housing.iloc[:5] # pretend these are new districts\n", + "predictions = final_model_reloaded.predict(new_data)\n", + "print(predictions)" + ] + } + ], + "metadata": { + "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": "3.9.13" + }, + "org": null + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Lecture-Slides/Machine-Learning-Lecture/ipynb/Training-Models.ipynb b/Lecture-Slides/Machine-Learning-Lecture/ipynb/Training-Models.ipynb new file mode 100644 index 0000000..97ae8a7 --- /dev/null +++ b/Lecture-Slides/Machine-Learning-Lecture/ipynb/Training-Models.ipynb @@ -0,0 +1,3506 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9be6bce2-5b5f-4438-9b01-215f4ed47bee", + "metadata": {}, + "source": [ + "Code for Machine Learning and Data Science II\n", + "=============================================\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "a2c3e047-d189-4f5d-a297-ae805d53a39f", + "metadata": {}, + "source": [ + "These are the code snippets used in `Training Models`\n", + "part of `Machine Learning and Data Science II`.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "ba8346d9-0448-4b57-a05b-e8d0cbfc7c51", + "metadata": {}, + "source": [ + "### Introduction\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "6d40f196-dfaa-4b10-a99e-94f526b8c80d", + "metadata": {}, + "source": [ + "The following is a custom package written to handle plotting\n", + "and other functions required by the lecture. it is by no means\n", + "plt.show()\n", + "using plt.show().\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4ff81e92-f83c-4f38-a426-ee9b9cc04fe1", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import ChalcedonPy as cp\n", + "\n", + "# Initialise ChalcedonPy\n", + "cp.init(save_path=\"Training-Models\",\n", + " display_mode=\"slide\")" + ] + }, + { + "cell_type": "markdown", + "id": "1be6aad1-4fd0-4fd3-a2a1-cdb10cd1a534", + "metadata": {}, + "source": [ + "### Linear Regression\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "7e5f5af8-cee9-4975-939b-aa0eb47f354b", + "metadata": {}, + "source": [ + "A linear model makes a prediction by simply computing a\n", + "weighted sum of the input features, plus a constant called\n", + "the bias term (also called the intercept term),\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "992c7126-8d5b-459c-b801-c14c0866aafa", + "metadata": {}, + "source": [ + "#### The Normal Equation\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "039bdec1-6a21-4d92-85c4-231dff328d67", + "metadata": {}, + "source": [ + "To find the value of $\\theta$ that minimizes the MSE, there\n", + "exists a closed-form solution—in other words, a\n", + "mathematical equation that gives the result directly.\n", + "\n", + "This is called the Normal equation.\n", + "\n", + "First generate some data.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "378b81a6-838c-4b0b-a97a-90b2a7181af5", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "np.random.seed(42) # to make this code example reproducible\n", + "m = 100 # number of instances\n", + "X = 2 * np.random.rand(m, 1) # column vector\n", + "y = 4 + 3 * X + np.random.randn(m, 1) # column vector" + ] + }, + { + "cell_type": "markdown", + "id": "e68335fa-34cd-4694-a51d-3abee874e9c8", + "metadata": {}, + "source": [ + "The following code plots the data\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0dceb8ba-f264-49ea-8e62-547ab010f2a0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure(figsize=(11, 6))\n", + "plt.plot(X, y, \".\", label=\"Data Points\")\n", + "plt.xlabel(\"$x_1$\")\n", + "plt.ylabel(\"$y$\", rotation=0)\n", + "plt.axis([0, 2, 0, 15])\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3345732d-c533-4ced-a0a3-54e07d95b6df", + "metadata": {}, + "source": [ + "Now time to calculate $\\hat{\\theta}$ using the normal equation:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a4ae1b17-0354-4e18-acb3-2bb0789dd4c4", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import add_dummy_feature\n", + "\n", + "X_b = add_dummy_feature(X) # add x0 = 1 to each instance\n", + "theta_best = np.linalg.inv(X_b.T @ X_b) @ X_b.T @ y" + ] + }, + { + "cell_type": "markdown", + "id": "43cfeecc-edf0-47d8-8b18-93055af702b4", + "metadata": {}, + "source": [ + "\n", + "| FUNCTION|DEFINITION|MORE INFO|\n", + "| linalg.inv(…))}}}|Given a square matrix a, return the matrix ainv satisfying {{{Code(a @ ainv = ainv @ a = eye(a.shape[0])|[a href=\"https://numpy.org/doc/stable/reference/generated/numpy.linalg.inv.html\">LinkLinkdummyfeature(…)|Augment dataset with an additional dummy feature.|[a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.add_dummy_feature.html\">LinkLink" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(11, 6))\n", + "\n", + "plt.plot(X, y, \".\", label=\"Data Points\")\n", + "plt.plot(X_new, y_predict, \"-\", label=\"Predictions\")\n", + "\n", + "plt.xlabel(\"$x_1$\")\n", + "plt.ylabel(\"$y$\", rotation=0)\n", + "\n", + "plt.axis([0, 2, 0, 15])\n", + "\n", + "plt.legend(loc=\"upper left\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f454fc62-1939-4807-8412-a1fe57cca188", + "metadata": {}, + "source": [ + "Performing linear regression using Scikit-Learn is relatively straightforward:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7df0da97-50a9-44c7-a242-3d2f1419b368", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimated coefficients: [[2.77011339]]\n", + "The b0 value is: [4.21509616]\n" + ] + } + ], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "\n", + "lin_reg = LinearRegression()\n", + "lin_reg.fit(X, y)\n", + "\n", + "print(\"Estimated coefficients: \", lin_reg.coef_)\n", + "print(\"The b0 value is: \", lin_reg.intercept_)" + ] + }, + { + "cell_type": "markdown", + "id": "aaa13a5d-8aef-4221-a925-919ca690756e", + "metadata": {}, + "source": [ + "\n", + "| FUNCTION|DEFINITION|MORE INFO|\n", + "| sklearn.linearmodel.LinearRegression(…)|Ordinary least squares Linear Regression. LinearRegression fits a linear model with coefficients w = (w1, …, wp) to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation.|[a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html\">LinkLinkLinkLinkLinkLinkLinkLink" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "np.random.seed(42)\n", + "theta = np.random.randn(2, 1) # random initialization\n", + "plt.figure(figsize=(11, 6))\n", + "plt.subplot(131)\n", + "plot_gradient_descent(theta, eta=0.02)\n", + "plt.ylabel(\"$y$\", rotation=0)\n", + "plt.subplot(132)\n", + "theta_path_bgd = plot_gradient_descent(theta, eta=0.1)\n", + "plt.gca().axes.yaxis.set_ticklabels([])\n", + "plt.subplot(133)\n", + "plt.gca().axes.yaxis.set_ticklabels([])\n", + "plot_gradient_descent(theta, eta=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ec7466ca-e68c-4eb0-986d-4e4e43a6a3a5", + "metadata": {}, + "source": [ + "#### Stochastic Gradient Descent\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "f3532513-f1e9-46da-882c-818242d7b5af", + "metadata": {}, + "source": [ + "stochastic gradient descent picks a random instance in the training\n", + "set at every step and computes the gradients based\n", + "only on that single instance.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "966d6c6e-6e62-434c-ba51-485d4ea4a1e7", + "metadata": {}, + "outputs": [], + "source": [ + "theta_path_sgd = []" + ] + }, + { + "cell_type": "markdown", + "id": "e3269dd3-75a9-4ae5-b918-7cb562315902", + "metadata": {}, + "source": [ + "This code implements stochastic gradient descent using a simple learning schedule:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5ed5ce33-2083-466c-a948-902cbca6c2a4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_epochs = 50\n", + "t0, t1 = 5, 50 # learning schedule hyperparameters\n", + "\n", + "def learning_schedule(t):\n", + " return t0 / (t + t1)\n", + "\n", + "np.random.seed(42)\n", + "theta = np.random.randn(2, 1) # random initialization\n", + "n_shown = 20\n", + "\n", + "plt.figure(figsize=(11, 6)) \n", + "\n", + "for epoch in range(n_epochs):\n", + " for iteration in range(m):\n", + "\n", + " if epoch == 0 and iteration < n_shown:\n", + " y_predict = X_new_b @ theta\n", + " color = mpl.colors.rgb2hex(plt.cm.OrRd(iteration / n_shown + 0.15))\n", + " plt.plot(X_new, y_predict, color=color)\n", + "\n", + " random_index = np.random.randint(m)\n", + " xi = X_b[random_index : random_index + 1]\n", + " yi = y[random_index : random_index + 1]\n", + " gradients = 2 * xi.T @ (xi @ theta - yi) # for SGD, do not divide by m\n", + " eta = learning_schedule(epoch * m + iteration)\n", + " theta = theta - eta * gradients\n", + " theta_path_sgd.append(theta) \n", + "\n", + "plt.plot(X, y, \".\")\n", + "plt.xlabel(\"$x_1$\")\n", + "plt.ylabel(\"$y$\", rotation=0)\n", + "plt.axis([0, 2, 0, 15])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "eb7e829b-055c-43ab-8fa8-ba1ac36c0c61", + "metadata": {}, + "source": [ + "Let's see the solution to our algorithm.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "cbe1aaa1-2e3e-49c9-a1c2-34bbad0ac6a5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[4.21076011]\n", + " [2.74856079]]\n" + ] + } + ], + "source": [ + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "65b24eab-16a9-45c2-8d5d-0a075f01c054", + "metadata": {}, + "source": [ + "To perform linear regression using stochastic GD with Scikit-Learn,\n", + "you can use the SGDRegressor class, which defaults to optimizing\n", + "the MSE cost function.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "fe6d92f3-2681-461a-b93f-df7ae73f35b4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
SGDRegressor(n_iter_no_change=100, penalty=None, random_state=42, tol=1e-05)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "SGDRegressor(n_iter_no_change=100, penalty=None, random_state=42, tol=1e-05)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import SGDRegressor\n", + "\n", + "sgd_reg = SGDRegressor(max_iter=1000, tol=1e-5, penalty=None, eta0=0.01,\n", + " n_iter_no_change=100, random_state=42)\n", + "sgd_reg.fit(X, y.ravel()) # y.ravel() because fit() expects 1D targets" + ] + }, + { + "cell_type": "markdown", + "id": "6deaef1d-48de-456d-a59f-a39c03d1c6b9", + "metadata": {}, + "source": [ + "\n", + "| FUNCTION|DEFINITION|MORE INFO|\n", + "| sklearn.linearmodel.SGDRegressor(…)|Linear model fitted by minimizing a regularized empirical loss with SGD.|[a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html\">LinkLink" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(11, 6))\n", + "plt.plot(theta_path_sgd[:, 0], theta_path_sgd[:, 1], \"-s\", linewidth=1,\n", + " label=\"Stochastic\")\n", + "plt.plot(theta_path_mgd[:, 0], theta_path_mgd[:, 1], \"-+\", linewidth=2,\n", + " label=\"Mini-batch\")\n", + "plt.plot(theta_path_bgd[:, 0], theta_path_bgd[:, 1], \"-o\", linewidth=3,\n", + " label=\"Batch\")\n", + "plt.legend(loc=\"upper left\")\n", + "plt.xlabel(r\"$\\theta_0$\")\n", + "plt.ylabel(r\"$\\theta_1$ \", rotation=0)\n", + "plt.axis([2.6, 4.6, 2.3, 3.4])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2e93f1e2-c542-47df-9a68-53e80dfa8289", + "metadata": {}, + "source": [ + "They all end up near the minimum, but batch GD’s path actually stops at the minimum,\n", + "while both stochastic GD and mini-batch GD continue to walk around.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "c25fcbf1-83a1-4e0b-8b47-b781b825911b", + "metadata": {}, + "source": [ + "### Polynomial Regression\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "ae43ee1a-7af9-4061-b596-24db8b7709bb", + "metadata": {}, + "source": [ + "What if your data is more complex than a straight line?\n", + "\n", + "Surprisingly, you can use a linear model to fit nonlinear data.\n", + "A simple way to do this is to add powers of each feature as new features, then\n", + "train a linear model on this extended set of features. This technique is called polynomial regression.\n", + "\n", + "First generate some random data.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "fda840c2-a119-45cd-a357-dc70a4f78a58", + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(42)\n", + "m = 100\n", + "X = 6 * np.random.rand(m, 1) - 3\n", + "y = 0.5 * X ** 2 + X + 2 + np.random.randn(m, 1)" + ] + }, + { + "cell_type": "markdown", + "id": "f0462e20-d347-439c-9066-08284836e85d", + "metadata": {}, + "source": [ + "And now we plot this data.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "cc74bdc2-97c4-4d37-b404-315aefc29cb5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(11, 6))\n", + " \n", + "plt.plot(X, y, \".\", label=\"Data Points\")\n", + "plt.xlabel(\"$x_1$\")\n", + "plt.ylabel(\"$y$\", rotation=0)\n", + "plt.axis([-3, 3, 0, 10])\n", + "plt.legend(loc=\"upper left\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "47fab207-25e3-47b4-a3aa-6316e8ce7994", + "metadata": {}, + "source": [ + "A single line wont fit. We need to use sklearn.preprocessing.PolynomialFeatures()\n", + "for this:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "b3083abf-c9a8-4378-addf-ed8de3c31779", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.75275929]\n", + "[-0.75275929 0.56664654]\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import PolynomialFeatures\n", + "\n", + "poly_features = PolynomialFeatures(degree=2, include_bias=False)\n", + "X_poly = poly_features.fit_transform(X)\n", + "\n", + "print(X[0])\n", + "print(X_poly[0])" + ] + }, + { + "cell_type": "markdown", + "id": "7839a22b-6d4a-47f8-a5ff-73688cf7c426", + "metadata": {}, + "source": [ + "\n", + "| FUNCTION|DEFINITION|MORE INFO|\n", + "| sklearn.preprocessing.PolynomialFeatures(…)|Generate polynomial and interaction features.|[a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html\">LinkLink" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(11, 6))\n", + "\n", + "plt.plot(X, y, \".\", label=\"Data Points\")\n", + "plt.plot(X_new, y_new, \"-\", label=\"Predictions\")\n", + "plt.xlabel(\"$x_1$\")\n", + "plt.ylabel(\"$y$\", rotation=0)\n", + "plt.legend(loc=\"upper left\")\n", + "plt.axis([-3, 3, 0, 10])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "81e5f386-baf4-4978-b352-631384d2ae1b", + "metadata": {}, + "source": [ + "#### Learning Curves\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "261e54c5-b654-4367-ad6b-791b41a897ff", + "metadata": {}, + "source": [ + "Let's see if increasing the degree number improves\n", + "our model.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "4f9a3b5a-6735-424f-b136-4f84945d44e8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.pipeline import make_pipeline\n", + "\n", + "plt.figure(figsize=(11, 6))\n", + "for style, width, degree in ((\"-+\", 2, 1), (\"--\", 2, 2), (\"-\", 1, 300)):\n", + " polybig_features = PolynomialFeatures(degree=degree, include_bias=False)\n", + " std_scaler = StandardScaler()\n", + " lin_reg = LinearRegression()\n", + " polynomial_regression = make_pipeline(polybig_features, std_scaler, lin_reg)\n", + " polynomial_regression.fit(X, y)\n", + " y_newbig = polynomial_regression.predict(X_new)\n", + " label = f\"{degree} degree{'s' if degree > 1 else ''}\"\n", + " plt.plot(X_new, y_newbig, style, label=label)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "6b1b9c78-877e-4a7c-b0a2-8a721a594661", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(X, y, \".\", label=\"Data Points\")\n", + "plt.legend(loc=\"upper left\")\n", + "plt.xlabel(\"$x_1$\")\n", + "plt.ylabel(\"$y$\", rotation=0)\n", + "plt.axis([-3, 3, 0, 10])\n", + "plt.show()\n", + "plt.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "38c8aecb-4dec-4621-bc1c-64256a709f2f", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import learning_curve\n", + "\n", + "train_sizes, train_scores, valid_scores = learning_curve(\n", + " LinearRegression(), X, y, train_sizes=np.linspace(0.01, 1.0, 40), cv=5,\n", + " scoring=\"neg_root_mean_squared_error\")\n", + "train_errors = -train_scores.mean(axis=1)\n", + "valid_errors = -valid_scores.mean(axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "2e97c910-507d-41ac-a88a-0f788277ae5b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(11, 6))\n", + "\n", + "plt.plot(train_sizes, train_errors, \"-+\", label=\"train\")\n", + "plt.plot(train_sizes, valid_errors, \"-\", label=\"valid\")\n", + "\n", + "plt.xlabel(\"Training set size\")\n", + "plt.ylabel(\"RMSE\")\n", + "\n", + "plt.legend(loc=\"upper right\")\n", + "plt.axis([0, 80, 0, 2.5])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "d5749ca1-0938-424e-878c-b391a846187f", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.pipeline import make_pipeline\n", + "\n", + "polynomial_regression = make_pipeline(\n", + " PolynomialFeatures(degree=10, include_bias=False),\n", + " LinearRegression())\n", + "\n", + "train_sizes, train_scores, valid_scores = learning_curve(\n", + " polynomial_regression, X, y, train_sizes=np.linspace(0.01, 1.0, 40), cv=5,\n", + " scoring=\"neg_root_mean_squared_error\")" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "24bd9f02-b110-4571-864a-eabdd1737198", + "metadata": {}, + "outputs": [], + "source": [ + "train_errors = -train_scores.mean(axis=1)\n", + "valid_errors = -valid_scores.mean(axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "6d6e1d52-a59a-425a-bd63-c4e6dc081893", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(11, 6))\n", + "\n", + "plt.plot(train_sizes, train_errors, \"-+\", label=\"train\")\n", + "plt.plot(train_sizes, valid_errors, \"-\", label=\"valid\")\n", + "plt.legend(loc=\"upper right\")\n", + "plt.xlabel(\"Training set size\")\n", + "plt.ylabel(\"RMSE\")\n", + "plt.axis([0, 80, 0, 2.5])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ea972a38-ce88-47e4-bcab-702bf18d026a", + "metadata": {}, + "source": [ + "### Regularised Linear Models\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "9e9e7ab6-450c-4300-b43d-2d547a613966", + "metadata": {}, + "source": [ + "#### Ridge Regression\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "295f177f-02a0-4759-8f84-d1fefda0a75d", + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(42)\n", + "m = 20\n", + "X = 3 * np.random.rand(m, 1)\n", + "y = 1 + 0.5 * X + np.random.randn(m, 1) / 1.5\n", + "X_new = np.linspace(0, 3, 100).reshape(100, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "956b28c6-5147-4928-b4f4-d9fceedb000b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(11, 6))\n", + "\n", + "plt.plot(X, y, \".\", label = \"Data Points\")\n", + "plt.xlabel(\"$x_1$\")\n", + "plt.ylabel(\"$y$ \", rotation=0)\n", + "plt.legend(loc=\"upper right\")\n", + "plt.axis([0, 3, 0, 3.5])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "953a4ccf-ef93-4bb4-b8e5-0aa4f8807c23", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.55325833]])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import Ridge\n", + "\n", + "ridge_reg = Ridge(alpha=0.1, solver=\"cholesky\")\n", + "ridge_reg.fit(X, y)\n", + "ridge_reg.predict([[1.5]])" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "50064457-bf04-4a68-b8f3-5aa05fcae7a8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_model(model_class, polynomial, alphas, **model_kwargs):\n", + " plt.plot(X, y, \".\")\n", + " for alpha, style in zip(alphas, (\":\", \"--\", \"-\")):\n", + " if alpha > 0:\n", + " model = model_class(alpha, **model_kwargs)\n", + " else:\n", + " model = LinearRegression()\n", + " if polynomial:\n", + " model = make_pipeline(\n", + " PolynomialFeatures(degree=10, include_bias=False),\n", + " StandardScaler(),\n", + " model)\n", + " model.fit(X, y)\n", + " y_new_regul = model.predict(X_new)\n", + " plt.plot(X_new, y_new_regul, style, linewidth=2,\n", + " label=fr\"$\\alpha = {alpha}$\")\n", + " plt.legend(loc=\"upper left\")\n", + " plt.xlabel(\"$x_1$\")\n", + " plt.axis([0, 3, 0, 3.5])\n", + "\n", + "plt.figure(figsize=(11, 6))\n", + " \n", + "plt.subplot(121)\n", + "plot_model(Ridge, polynomial=False, alphas=(0, 10, 100), random_state=42)\n", + "plt.ylabel(\"$y$ \", rotation=0)\n", + "plt.subplot(122)\n", + "plot_model(Ridge, polynomial=True, alphas=(0, 10**-5, 1), random_state=42)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "2573941e-3d77-46cd-a3d3-6fd488e3b103", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1.55302613]\n" + ] + } + ], + "source": [ + "sgd_reg = SGDRegressor(penalty=\"l2\", alpha=0.1 / m, tol=None,\n", + " max_iter=1000, eta0=0.01, random_state=42)\n", + "sgd_reg.fit(X, y.ravel()) # y.ravel() because fit() expects 1D targets\n", + "print(sgd_reg.predict([[1.5]]))" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "8ed7a28e-f7e7-41b0-adf0-f001602eead3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1.55326019]]\n" + ] + } + ], + "source": [ + "ridge_reg = Ridge(alpha=0.1, solver=\"sag\", random_state=42)\n", + "ridge_reg.fit(X, y)\n", + "print(ridge_reg.predict([[1.5]]))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "cbfb606f-a333-45e8-97ec-036ff6b13fe5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.97898394]\n", + " [0.3828496 ]]\n", + "[0.97896386] [[0.38286422]]\n" + ] + } + ], + "source": [ + "alpha = 0.1\n", + "A = np.array([[0., 0.], [0., 1.]])\n", + "X_b = np.c_[np.ones(m), X]\n", + "print(np.linalg.inv(X_b.T @ X_b + alpha * A) @ X_b.T @ y)\n", + "\n", + "print(ridge_reg.intercept_, ridge_reg.coef_ )" + ] + }, + { + "cell_type": "markdown", + "id": "769dbae2-0ab9-4e66-9ee0-ce814fcda772", + "metadata": {}, + "source": [ + "#### Lasso Regression\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "064e63e9-f971-4a4e-8742-9002e893b4a1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.53788174])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import Lasso\n", + "\n", + "lasso_reg = Lasso(alpha=0.1)\n", + "lasso_reg.fit(X, y)\n", + "lasso_reg.predict([[1.5]])" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "032f3054-3e7c-490a-a365-595eb3aff5a7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(11, 6))\n", + "\n", + "plt.subplot(121)\n", + "\n", + "plot_model(Lasso, polynomial=False, alphas=(0, 0.1, 1), random_state=42)\n", + "plt.ylabel(\"$y$ \", rotation=0)\n", + "plt.subplot(122)\n", + "plot_model(Lasso, polynomial=True, alphas=(0, 1e-2, 1), random_state=42)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "77db3178-5fdb-439b-8c60-fd6ba16f1943", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t1a, t1b, t2a, t2b = -1, 3, -1.5, 1.5\n", + "\n", + "t1s = np.linspace(t1a, t1b, 500)\n", + "t2s = np.linspace(t2a, t2b, 500)\n", + "t1, t2 = np.meshgrid(t1s, t2s)\n", + "T = np.c_[t1.ravel(), t2.ravel()]\n", + "Xr = np.array([[1, 1], [1, -1], [1, 0.5]])\n", + "yr = 2 * Xr[:, :1] + 0.5 * Xr[:, 1:]\n", + "\n", + "J = (1 / len(Xr) * ((T @ Xr.T - yr.T) ** 2).sum(axis=1)).reshape(t1.shape)\n", + "\n", + "N1 = np.linalg.norm(T, ord=1, axis=1).reshape(t1.shape)\n", + "N2 = np.linalg.norm(T, ord=2, axis=1).reshape(t1.shape)\n", + "\n", + "t_min_idx = np.unravel_index(J.argmin(), J.shape)\n", + "t1_min, t2_min = t1[t_min_idx], t2[t_min_idx]\n", + "\n", + "t_init = np.array([[0.25], [-1]])\n", + "\n", + "def bgd_path(theta, X, y, l1, l2, core=1, eta=0.05, n_iterations=200):\n", + " path = [theta]\n", + " for iteration in range(n_iterations):\n", + " gradients = (core * 2 / len(X) * X.T @ (X @ theta - y)\n", + " + l1 * np.sign(theta) + l2 * theta)\n", + " theta = theta - eta * gradients\n", + " path.append(theta)\n", + " return np.array(path)\n", + "\n", + "fig, axes = plt.subplots(2, 2, sharex=True, sharey=True, figsize=(11, 6))\n", + "\n", + "for i, N, l1, l2, title in ((0, N1, 2.0, 0, \"Lasso\"), (1, N2, 0, 2.0, \"Ridge\")):\n", + " JR = J + l1 * N1 + l2 * 0.5 * N2 ** 2\n", + "\n", + " tr_min_idx = np.unravel_index(JR.argmin(), JR.shape)\n", + " t1r_min, t2r_min = t1[tr_min_idx], t2[tr_min_idx]\n", + "\n", + " levels = np.exp(np.linspace(0, 1, 20)) - 1\n", + " levelsJ = levels * (J.max() - J.min()) + J.min()\n", + " levelsJR = levels * (JR.max() - JR.min()) + JR.min()\n", + " levelsN = np.linspace(0, N.max(), 10)\n", + "\n", + " path_J = bgd_path(t_init, Xr, yr, l1=0, l2=0)\n", + " path_JR = bgd_path(t_init, Xr, yr, l1, l2)\n", + " path_N = bgd_path(theta=np.array([[2.0], [0.5]]), X=Xr, y=yr,\n", + " l1=np.sign(l1) / 3, l2=np.sign(l2), core=0)\n", + " ax = axes[i, 0]\n", + " ax.grid()\n", + " ax.axhline(y=0, color=\"k\")\n", + " ax.axvline(x=0, color=\"k\")\n", + " ax.contourf(t1, t2, N / 2.0, levels=levelsN)\n", + " ax.plot(path_N[:, 0], path_N[:, 1], \"y--\")\n", + " ax.plot(0, 0, \"ys\")\n", + " ax.plot(t1_min, t2_min, \"ys\")\n", + " ax.set_title(fr\"$\\ell_{i + 1}$ penalty\")\n", + " ax.axis([t1a, t1b, t2a, t2b])\n", + " if i == 1:\n", + " ax.set_xlabel(r\"$\\theta_1$\")\n", + " ax.set_ylabel(r\"$\\theta_2$\", rotation=0)\n", + "\n", + " ax = axes[i, 1]\n", + " ax.grid()\n", + " ax.axhline(y=0, color=\"k\")\n", + " ax.axvline(x=0, color=\"k\")\n", + " ax.contourf(t1, t2, JR, levels=levelsJR, alpha=0.9)\n", + " ax.plot(path_JR[:, 0], path_JR[:, 1], \"w-o\")\n", + " ax.plot(path_N[:, 0], path_N[:, 1], \"y--\")\n", + " ax.plot(0, 0, \"ys\")\n", + " ax.plot(t1_min, t2_min, \"ys\")\n", + " ax.plot(t1r_min, t2r_min, \"rs\")\n", + " ax.set_title(title)\n", + " ax.axis([t1a, t1b, t2a, t2b])\n", + " if i == 1:\n", + " ax.set_xlabel(r\"$\\theta_1$\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "41f2fcdd-dd10-4f01-8bfa-b5494ec8b99b", + "metadata": {}, + "source": [ + "#### Elastic Net\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "dae1f8b3-d931-4c25-95d8-a42ac9095cf2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1.54333232]\n" + ] + } + ], + "source": [ + "from sklearn.linear_model import ElasticNet\n", + "\n", + "elastic_net = ElasticNet(alpha=0.1, l1_ratio=0.5)\n", + "elastic_net.fit(X, y)\n", + "print(elastic_net.predict([[1.5]]))" + ] + }, + { + "cell_type": "markdown", + "id": "b8871a7d-418a-4382-84dd-844165c62ada", + "metadata": {}, + "source": [ + "#### Early Stopping\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "b6d32daf-ef34-431d-917f-ee99a6ac8646", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from copy import deepcopy\n", + "from sklearn.metrics import root_mean_squared_error\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "np.random.seed(42)\n", + "m = 100\n", + "X = 6 * np.random.rand(m, 1) - 3\n", + "y = 0.5 * X ** 2 + X + 2 + np.random.randn(m, 1)\n", + "X_train, y_train = X[: m // 2], y[: m // 2, 0]\n", + "X_valid, y_valid = X[m // 2 :], y[m // 2 :, 0]\n", + "\n", + "preprocessing = make_pipeline(PolynomialFeatures(degree=90, include_bias=False),\n", + " StandardScaler())\n", + "X_train_prep = preprocessing.fit_transform(X_train)\n", + "X_valid_prep = preprocessing.transform(X_valid)\n", + "sgd_reg = SGDRegressor(penalty=None, eta0=0.002, random_state=42)\n", + "n_epochs = 500\n", + "best_valid_rmse = float('inf')\n", + "train_errors, val_errors = [], [] \n", + "\n", + "for epoch in range(n_epochs):\n", + " sgd_reg.partial_fit(X_train_prep, y_train)\n", + " y_valid_predict = sgd_reg.predict(X_valid_prep)\n", + " val_error = root_mean_squared_error(y_valid, y_valid_predict)\n", + " if val_error < best_valid_rmse:\n", + " best_valid_rmse = val_error\n", + " best_model = deepcopy(sgd_reg)\n", + "\n", + " y_train_predict = sgd_reg.predict(X_train_prep)\n", + " train_error = root_mean_squared_error(y_train, y_train_predict)\n", + " val_errors.append(val_error)\n", + " train_errors.append(train_error)\n", + "\n", + "best_epoch = np.argmin(val_errors)\n", + "plt.figure(figsize=(11, 6))\n", + "plt.annotate('Best model',\n", + " xy=(best_epoch, best_valid_rmse),\n", + " xytext=(best_epoch, best_valid_rmse + 0.5),\n", + " ha=\"center\",\n", + " arrowprops=dict(facecolor='black', shrink=0.05))\n", + "plt.plot([0, n_epochs], [best_valid_rmse, best_valid_rmse], \"k:\", linewidth=2)\n", + "plt.plot(val_errors, \"-\", linewidth=3, label=\"Validation set\")\n", + "plt.plot(best_epoch, best_valid_rmse, \"bo\")\n", + "plt.plot(train_errors, \"--\", linewidth=2, label=\"Training set\")\n", + "plt.legend(loc=\"upper right\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"RMSE\")\n", + "plt.axis([0, n_epochs, 0, 3.5])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "dce9acfd-2f02-452d-9fc1-f47fdc1d8350", + "metadata": {}, + "source": [ + "### Logistic Regression\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "bd4a6816-67d7-4c35-a968-bc6e95f22bda", + "metadata": {}, + "source": [ + "#### Estimating Probabilities\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "9ce9d68a-1e63-4d4a-ae49-397e03f95190", + "metadata": {}, + "outputs": [], + "source": [ + "lim = 6\n", + "t = np.linspace(-lim, lim, 100)\n", + "sig = 1 / (1 + np.exp(-t))" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "58d915e5-563b-423f-a759-1534740b0162", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(11, 6))\n", + "plt.plot([-lim, lim], [0, 0], \"k-\")\n", + "plt.plot([-lim, lim], [0.5, 0.5], \"k:\")\n", + "plt.plot([-lim, lim], [1, 1], \"r:\")\n", + "plt.plot([0, 0], [-1.1, 1.1], \"-\")\n", + "plt.plot(t, sig, \"-\", linewidth=2, label=r\"$\\sigma(t) = \\dfrac{1}{1 + e^{-t}}$\")\n", + "plt.xlabel(\"t\")\n", + "plt.legend(loc=\"upper left\")\n", + "plt.axis([-lim, lim, -0.1, 1.1])\n", + "plt.gca().set_yticks([0, 0.25, 0.5, 0.75, 1])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c7e037a9-6612-471e-9b6d-dda234e170f6", + "metadata": {}, + "source": [ + "#### Desicion Boundaries\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "718e34b8-9ca8-4a78-bb95-6cb8410b1913", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module']\n" + ] + } + ], + "source": [ + "from sklearn.datasets import load_iris\n", + "\n", + "iris = load_iris(as_frame=True)\n", + "print(list(iris))" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "5648a09b-9b99-4244-adf9-1c4a8be11c15", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "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", + "|details-start|\n", + "**References**\n", + "|details-split|\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", + "\n", + "|details-end|\n", + "\n" + ] + } + ], + "source": [ + "print(iris.DESCR)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "8015ef3c-7f92-48e6-ab32-1a4e5234c1cb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)
05.13.51.40.2
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24.73.21.30.2
\n", + "
" + ], + "text/plain": [ + " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n", + "0 5.1 3.5 1.4 0.2\n", + "1 4.9 3.0 1.4 0.2\n", + "2 4.7 3.2 1.3 0.2" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris.data.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "2f76e5bf-09e9-4dff-82a8-a27cc26c7b7c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 0\n", + "2 0\n", + "Name: target, dtype: int64" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris.target.head(3) # note that the instances are not shuffled" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "2cecbb9a-7ab6-447c-b6ca-760e88ac690b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['setosa', 'versicolor', 'virginica'], dtype='#sk-container-id-2 {\n", + " /* Definition of color scheme common for light and dark mode */\n", + " --sklearn-color-text: black;\n", + " --sklearn-color-line: gray;\n", + " /* Definition of color scheme for unfitted estimators */\n", + " --sklearn-color-unfitted-level-0: #fff5e6;\n", + " --sklearn-color-unfitted-level-1: #f6e4d2;\n", + " --sklearn-color-unfitted-level-2: #ffe0b3;\n", + " --sklearn-color-unfitted-level-3: chocolate;\n", + " /* Definition of color scheme for fitted estimators */\n", + " --sklearn-color-fitted-level-0: #f0f8ff;\n", + " --sklearn-color-fitted-level-1: #d4ebff;\n", + " --sklearn-color-fitted-level-2: #b3dbfd;\n", + " --sklearn-color-fitted-level-3: cornflowerblue;\n", + "\n", + " /* Specific color for light theme */\n", + " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", + " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n", + " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n", + " --sklearn-color-icon: #696969;\n", + "\n", + " @media (prefers-color-scheme: dark) {\n", + " /* Redefinition of color scheme for dark theme */\n", + " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", + " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n", + " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n", + " --sklearn-color-icon: #878787;\n", + " }\n", + "}\n", + "\n", + "#sk-container-id-2 {\n", + " color: var(--sklearn-color-text);\n", + "}\n", + "\n", + "#sk-container-id-2 pre {\n", + " padding: 0;\n", + "}\n", + "\n", + "#sk-container-id-2 input.sk-hidden--visually {\n", + " border: 0;\n", + " clip: rect(1px 1px 1px 1px);\n", + " clip: rect(1px, 1px, 1px, 1px);\n", + " height: 1px;\n", + " margin: -1px;\n", + " overflow: hidden;\n", + " padding: 0;\n", + " position: absolute;\n", + " width: 1px;\n", + "}\n", + "\n", + "#sk-container-id-2 div.sk-dashed-wrapped {\n", + " border: 1px dashed var(--sklearn-color-line);\n", + " margin: 0 0.4em 0.5em 0.4em;\n", + " box-sizing: border-box;\n", + " padding-bottom: 0.4em;\n", + " background-color: var(--sklearn-color-background);\n", + "}\n", + "\n", + "#sk-container-id-2 div.sk-container {\n", + " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n", + " but bootstrap.min.css set `[hidden] { display: none !important; }`\n", + " so we also need the `!important` here to be able to override the\n", + " default hidden behavior on the sphinx rendered scikit-learn.org.\n", + " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n", + " display: inline-block !important;\n", + " position: relative;\n", + "}\n", + "\n", + "#sk-container-id-2 div.sk-text-repr-fallback {\n", + " display: none;\n", + "}\n", + "\n", + "div.sk-parallel-item,\n", + "div.sk-serial,\n", + "div.sk-item {\n", + " /* draw centered vertical line to link estimators */\n", + " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n", + " background-size: 2px 100%;\n", + " background-repeat: no-repeat;\n", + " background-position: center center;\n", + "}\n", + "\n", + "/* Parallel-specific style estimator block */\n", + "\n", + "#sk-container-id-2 div.sk-parallel-item::after {\n", + " content: \"\";\n", + " width: 100%;\n", + " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n", + " flex-grow: 1;\n", + "}\n", + "\n", + "#sk-container-id-2 div.sk-parallel {\n", + " display: flex;\n", + " align-items: stretch;\n", + " justify-content: center;\n", + " background-color: var(--sklearn-color-background);\n", + " position: relative;\n", + "}\n", + "\n", + "#sk-container-id-2 div.sk-parallel-item {\n", + " display: flex;\n", + " flex-direction: column;\n", + "}\n", + "\n", + "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n", + " align-self: flex-end;\n", + " width: 50%;\n", + "}\n", + "\n", + "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n", + " align-self: flex-start;\n", + " width: 50%;\n", + "}\n", + "\n", + "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n", + " width: 0;\n", + "}\n", + "\n", + "/* Serial-specific style estimator block */\n", + "\n", + "#sk-container-id-2 div.sk-serial {\n", + " display: flex;\n", + " flex-direction: column;\n", + " align-items: center;\n", + " background-color: var(--sklearn-color-background);\n", + " padding-right: 1em;\n", + " padding-left: 1em;\n", + "}\n", + "\n", + "\n", + "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n", + "clickable and can be expanded/collapsed.\n", + "- Pipeline and ColumnTransformer use this feature and define the default style\n", + "- Estimators will overwrite some part of the style using the `sk-estimator` class\n", + "*/\n", + "\n", + "/* Pipeline and ColumnTransformer style (default) */\n", + "\n", + "#sk-container-id-2 div.sk-toggleable {\n", + " /* Default theme specific background. It is overwritten whether we have a\n", + " specific estimator or a Pipeline/ColumnTransformer */\n", + " background-color: var(--sklearn-color-background);\n", + "}\n", + "\n", + "/* Toggleable label */\n", + "#sk-container-id-2 label.sk-toggleable__label {\n", + " cursor: pointer;\n", + " display: block;\n", + " width: 100%;\n", + " margin-bottom: 0;\n", + " padding: 0.5em;\n", + " box-sizing: border-box;\n", + " text-align: center;\n", + "}\n", + "\n", + "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n", + " /* Arrow on the left of the label */\n", + " content: \"▸\";\n", + " float: left;\n", + " margin-right: 0.25em;\n", + " color: var(--sklearn-color-icon);\n", + "}\n", + "\n", + "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n", + " color: var(--sklearn-color-text);\n", + "}\n", + "\n", + "/* Toggleable content - dropdown */\n", + "\n", + "#sk-container-id-2 div.sk-toggleable__content {\n", + " max-height: 0;\n", + " max-width: 0;\n", + " overflow: hidden;\n", + " text-align: left;\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-2 div.sk-toggleable__content.fitted {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-2 div.sk-toggleable__content pre {\n", + " margin: 0.2em;\n", + " border-radius: 0.25em;\n", + " color: var(--sklearn-color-text);\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n", + " /* Expand drop-down */\n", + " max-height: 200px;\n", + " max-width: 100%;\n", + " overflow: auto;\n", + "}\n", + "\n", + "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n", + " content: \"▾\";\n", + "}\n", + "\n", + "/* Pipeline/ColumnTransformer-specific style */\n", + "\n", + "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Estimator-specific style */\n", + "\n", + "/* Colorize estimator box */\n", + "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n", + "#sk-container-id-2 div.sk-label label {\n", + " /* The background is the default theme color */\n", + " color: var(--sklearn-color-text-on-default-background);\n", + "}\n", + "\n", + "/* On hover, darken the color of the background */\n", + "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "/* Label box, darken color on hover, fitted */\n", + "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n", + " color: var(--sklearn-color-text);\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Estimator label */\n", + "\n", + "#sk-container-id-2 div.sk-label label {\n", + " font-family: monospace;\n", + " font-weight: bold;\n", + " display: inline-block;\n", + " line-height: 1.2em;\n", + "}\n", + "\n", + "#sk-container-id-2 div.sk-label-container {\n", + " text-align: center;\n", + "}\n", + "\n", + "/* Estimator-specific */\n", + "#sk-container-id-2 div.sk-estimator {\n", + " font-family: monospace;\n", + " border: 1px dotted var(--sklearn-color-border-box);\n", + " border-radius: 0.25em;\n", + " box-sizing: border-box;\n", + " margin-bottom: 0.5em;\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-0);\n", + "}\n", + "\n", + "#sk-container-id-2 div.sk-estimator.fitted {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-0);\n", + "}\n", + "\n", + "/* on hover */\n", + "#sk-container-id-2 div.sk-estimator:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-2);\n", + "}\n", + "\n", + "#sk-container-id-2 div.sk-estimator.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-2);\n", + "}\n", + "\n", + "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n", + "\n", + "/* Common style for \"i\" and \"?\" */\n", + "\n", + ".sk-estimator-doc-link,\n", + "a:link.sk-estimator-doc-link,\n", + "a:visited.sk-estimator-doc-link {\n", + " float: right;\n", + " font-size: smaller;\n", + " line-height: 1em;\n", + " font-family: monospace;\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1em;\n", + " height: 1em;\n", + " width: 1em;\n", + " text-decoration: none !important;\n", + " margin-left: 1ex;\n", + " /* unfitted */\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + "}\n", + "\n", + ".sk-estimator-doc-link.fitted,\n", + "a:link.sk-estimator-doc-link.fitted,\n", + "a:visited.sk-estimator-doc-link.fitted {\n", + " /* fitted */\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/* On hover */\n", + "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n", + ".sk-estimator-doc-link:hover,\n", + "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n", + ".sk-estimator-doc-link:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n", + ".sk-estimator-doc-link.fitted:hover,\n", + "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n", + ".sk-estimator-doc-link.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "/* Span, style for the box shown on hovering the info icon */\n", + ".sk-estimator-doc-link span {\n", + " display: none;\n", + " z-index: 9999;\n", + " position: relative;\n", + " font-weight: normal;\n", + " right: .2ex;\n", + " padding: .5ex;\n", + " margin: .5ex;\n", + " width: min-content;\n", + " min-width: 20ex;\n", + " max-width: 50ex;\n", + " color: var(--sklearn-color-text);\n", + " box-shadow: 2pt 2pt 4pt #999;\n", + " /* unfitted */\n", + " background: var(--sklearn-color-unfitted-level-0);\n", + " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n", + "}\n", + "\n", + ".sk-estimator-doc-link.fitted span {\n", + " /* fitted */\n", + " background: var(--sklearn-color-fitted-level-0);\n", + " border: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "\n", + ".sk-estimator-doc-link:hover span {\n", + " display: block;\n", + "}\n", + "\n", + "/* \"?\"-specific style due to the `` HTML tag */\n", + "\n", + "#sk-container-id-2 a.estimator_doc_link {\n", + " float: right;\n", + " font-size: 1rem;\n", + " line-height: 1em;\n", + " font-family: monospace;\n", + " background-color: var(--sklearn-color-background);\n", + " border-radius: 1rem;\n", + " height: 1rem;\n", + " width: 1rem;\n", + " text-decoration: none;\n", + " /* unfitted */\n", + " color: var(--sklearn-color-unfitted-level-1);\n", + " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n", + "}\n", + "\n", + "#sk-container-id-2 a.estimator_doc_link.fitted {\n", + " /* fitted */\n", + " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n", + " color: var(--sklearn-color-fitted-level-1);\n", + "}\n", + "\n", + "/* On hover */\n", + "#sk-container-id-2 a.estimator_doc_link:hover {\n", + " /* unfitted */\n", + " background-color: var(--sklearn-color-unfitted-level-3);\n", + " color: var(--sklearn-color-background);\n", + " text-decoration: none;\n", + "}\n", + "\n", + "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n", + " /* fitted */\n", + " background-color: var(--sklearn-color-fitted-level-3);\n", + "}\n", + "" + ], + "text/plain": [ + "LogisticRegression(random_state=42)" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "X = iris.data[[\"petal width (cm)\"]].values\n", + "y = iris.target_names[iris.target] == 'virginica'\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n", + "\n", + "log_reg = LogisticRegression(random_state=42)\n", + "log_reg.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "39bc0337-ab7c-4d0d-8df3-97baf00e15f6", + "metadata": {}, + "outputs": [], + "source": [ + "X_new = np.linspace(0, 3, 1000).reshape(-1, 1) \n", + "y_proba = log_reg.predict_proba(X_new)\n", + "decision_boundary = X_new[y_proba[:, 1] >= 0.5][0, 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "d92dac60-88e8-4988-9e7c-dbd6803f393f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "plt.plot(X_new, y_proba[:, 0], \"b--\", linewidth=2,\n", + " label=\"Not Iris virginica proba\")\n", + "plt.plot(X_new, y_proba[:, 1], \"g-\", linewidth=2, label=\"Iris virginica proba\")\n", + "plt.plot([decision_boundary, decision_boundary], [0, 1], \"k:\", linewidth=2,\n", + " label=\"Decision boundary\")\n", + "plt.arrow(x=decision_boundary, y=0.08, dx=-0.3, dy=0,\n", + " head_width=0.05, head_length=0.1, fc=\"b\", ec=\"b\")\n", + "plt.arrow(x=decision_boundary, y=0.92, dx=0.3, dy=0,\n", + " head_width=0.05, head_length=0.1, fc=\"g\", ec=\"g\")\n", + "plt.plot(X_train[y_train == 0], y_train[y_train == 0], \"bs\")\n", + "plt.plot(X_train[y_train == 1], y_train[y_train == 1], \"g^\")\n", + "plt.xlabel(\"Petal width (cm)\"), plt.ylabel(\"Probability\")\n", + "plt.legend(loc=\"center left\"), plt.axis([0, 3, -0.02, 1.02])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "caae5a00-4073-4299-94f0-3bbd4f7d7c6f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.6516516516516517" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "decision_boundary" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "c6f7bee4-e4b6-4c67-99a3-ae94099bfcda", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, False])" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "log_reg.predict([[1.7], [1.5]])" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "6b464e11-fc9c-4669-a59c-8ee850297a00", + "metadata": {}, + "outputs": [], + "source": [ + "X = iris.data[[\"petal length (cm)\", \"petal width (cm)\"]].values\n", + "y = iris.target_names[iris.target] == 'virginica'\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n", + "\n", + "log_reg = LogisticRegression(C=2, random_state=42)\n", + "log_reg.fit(X_train, y_train)\n", + "\n", + "# for the contour plot\n", + "x0, x1 = np.meshgrid(np.linspace(2.9, 7, 500).reshape(-1, 1),\n", + " np.linspace(0.8, 2.7, 200).reshape(-1, 1))\n", + "X_new = np.c_[x0.ravel(), x1.ravel()] # one instance per point on the figure\n", + "y_proba = log_reg.predict_proba(X_new)\n", + "zz = y_proba[:, 1].reshape(x0.shape)\n", + "\n", + "# for the decision boundary\n", + "left_right = np.array([2.9, 7])\n", + "boundary = -((log_reg.coef_[0, 0] * left_right + log_reg.intercept_[0])\n", + " / log_reg.coef_[0, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "80ada171-4097-4bec-8335-9e6ba41e155c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "plt.plot(X_train[y_train == 0, 0], X_train[y_train == 0, 1], \"bs\")\n", + "plt.plot(X_train[y_train == 1, 0], X_train[y_train == 1, 1], \"g^\")\n", + "contour = plt.contour(x0, x1, zz, cmap=plt.cm.brg)\n", + "plt.clabel(contour, inline=1)\n", + "plt.plot(left_right, boundary, \"k--\", linewidth=3)\n", + "plt.text(3.5, 1.27, \"Not Iris virginica\", color=\"b\", ha=\"center\")\n", + "plt.text(6.5, 2.3, \"Iris virginica\", color=\"g\", ha=\"center\")\n", + "plt.xlabel(\"Petal length\")\n", + "plt.ylabel(\"Petal width\")\n", + "plt.axis([2.9, 7, 0.8, 2.7])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "66a9374a-c7d0-41a5-9727-adea62445d88", + "metadata": {}, + "source": [ + "#### Softmax Regression\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "fd1b6227-4248-4df4-bbee-dc80b23981f8", + "metadata": {}, + "source": [ + "Softmax regression (or multinomial logistic regression) is a generalization\n", + "of logistic regression to the case where we want to handle multiple classes.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "c4717346-1608-4477-a1b1-ddbb464a8128", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
LogisticRegression(C=30, random_state=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LogisticRegression(C=30, random_state=42)" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = iris.data[[\"petal length (cm)\", \"petal width (cm)\"]].values\n", + "y = iris[\"target\"]\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n", + "\n", + "softmax_reg = LogisticRegression(C=30, random_state=42)\n", + "softmax_reg.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "ed1e61ce-af67-4d35-a9f5-aece879ca2ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0. , 0.04, 0.96]])" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "softmax_reg.predict([[5, 2]])\n", + "softmax_reg.predict_proba([[5, 2]]).round(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "5177e33e-7299-4a1a-802f-38120334e83f", + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib.colors import ListedColormap\n", + "\n", + "custom_cmap = ListedColormap([\"#fafab0\", \"#9898ff\", \"#a0faa0\"])\n", + "\n", + "x0, x1 = np.meshgrid(np.linspace(0, 8, 500).reshape(-1, 1),\n", + " np.linspace(0, 3.5, 200).reshape(-1, 1))\n", + "X_new = np.c_[x0.ravel(), x1.ravel()]\n", + "\n", + "y_proba = softmax_reg.predict_proba(X_new)\n", + "y_predict = softmax_reg.predict(X_new)\n", + "\n", + "zz1 = y_proba[:, 1].reshape(x0.shape)\n", + "zz = y_predict.reshape(x0.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "5ff18641-ab87-4d4c-b64b-bbbaaf586a82", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "plt.plot(X[y == 2, 0], X[y == 2, 1], \"g^\", label=\"Iris virginica\")\n", + "plt.plot(X[y == 1, 0], X[y == 1, 1], \"bs\", label=\"Iris versicolor\")\n", + "plt.plot(X[y == 0, 0], X[y == 0, 1], \"yo\", label=\"Iris setosa\")\n", + "\n", + "plt.contourf(x0, x1, zz, cmap=custom_cmap)\n", + "contour = plt.contour(x0, x1, zz1, cmap=\"hot\")\n", + "plt.clabel(contour, inline=1)\n", + "plt.xlabel(\"Petal length\")\n", + "plt.ylabel(\"Petal width\")\n", + "plt.legend(loc=\"center left\")\n", + "plt.axis([0.5, 7, 0, 3.5])\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": { + "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": 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+-117.99,34.13,37.0,1568.0,371.0,1618.0,350.0,2.9605,129400.0,INLAND +-117.06,32.8,17.0,2247.0,340.0,973.0,318.0,5.5,222000.0,<1H OCEAN +-118.16,34.03,41.0,1377.0,293.0,1142.0,272.0,3.1724,141600.0,<1H OCEAN +-117.33,34.06,42.0,530.0,123.0,390.0,124.0,1.0469,67000.0,INLAND +-121.27,38.11,15.0,2039.0,384.0,1178.0,375.0,3.8672,120100.0,INLAND +-118.24,34.2,41.0,2067.0,452.0,1282.0,455.0,5.5756,309900.0,<1H OCEAN +-122.15,37.74,52.0,2898.0,557.0,1338.0,550.0,3.851,183500.0,NEAR BAY +-122.07,37.72,26.0,3204.0,477.0,1411.0,484.0,5.4834,295200.0,NEAR BAY +-118.19,33.79,43.0,1823.0,600.0,2339.0,560.0,1.6792,130600.0,NEAR OCEAN +-120.98,37.65,40.0,422.0,63.0,158.0,63.0,7.3841,172200.0,INLAND +-122.26,38.15,26.0,3699.0,671.0,2388.0,699.0,4.0515,121900.0,NEAR BAY +-118.39,34.08,28.0,833.0,230.0,349.0,210.0,3.067,375000.0,<1H OCEAN +-121.04,39.08,8.0,2870.0,526.0,1307.0,451.0,3.463,201700.0,INLAND +-117.61,34.34,18.0,5210.0,912.0,1301.0,464.0,4.8623,176900.0,INLAND 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OCEAN +-118.39,34.04,52.0,1492.0,277.0,666.0,289.0,4.7386,340400.0,<1H OCEAN +-122.23,40.57,18.0,1633.0,243.0,750.0,252.0,5.1585,150800.0,INLAND +-119.89,36.64,34.0,1422.0,237.0,716.0,222.0,2.975,90000.0,INLAND +-117.12,32.96,15.0,2869.0,405.0,1526.0,402.0,6.0175,238300.0,<1H OCEAN +-121.79,38.01,17.0,4032.0,814.0,1749.0,618.0,3.1728,146800.0,INLAND +-117.9,34.11,23.0,4776.0,1316.0,4797.0,1187.0,2.1667,142600.0,<1H OCEAN +-119.2,34.28,22.0,2362.0,601.0,1127.0,499.0,3.4006,219400.0,NEAR OCEAN +-117.96,33.94,31.0,2397.0,518.0,1407.0,476.0,2.6641,185200.0,<1H OCEAN +-120.55,38.46,16.0,1443.0,249.0,435.0,181.0,3.2031,129200.0,INLAND +-118.45,37.25,20.0,1468.0,283.0,721.0,270.0,3.0817,118800.0,INLAND +-117.86,34.24,52.0,803.0,267.0,628.0,225.0,4.1932,14999.0,INLAND +-117.04,32.72,24.0,5474.0,955.0,3020.0,904.0,4.0813,137000.0,NEAR OCEAN +-118.17,33.98,36.0,627.0,177.0,834.0,175.0,2.9844,163600.0,<1H OCEAN +-122.41,37.78,52.0,1928.0,836.0,2124.0,739.0,1.1185,55000.0,NEAR BAY +-119.35,36.21,26.0,2481.0,586.0,1445.0,498.0,1.6378,60300.0,INLAND +-118.18,33.79,27.0,1580.0,510.0,1896.0,448.0,2.0186,130000.0,NEAR OCEAN +-124.01,40.89,28.0,1470.0,336.0,811.0,314.0,2.4559,75600.0,<1H OCEAN +-122.39,40.6,22.0,2195.0,489.0,1021.0,460.0,1.4125,66500.0,INLAND +-118.26,34.16,20.0,3407.0,885.0,1883.0,870.0,3.7321,351100.0,<1H OCEAN +-117.22,32.8,23.0,1906.0,525.0,1029.0,491.0,2.93,183300.0,NEAR OCEAN +-121.75,36.91,32.0,1461.0,422.0,1494.0,416.0,2.8056,173200.0,<1H OCEAN +-117.14,32.8,35.0,1267.0,212.0,710.0,204.0,2.5368,169600.0,NEAR OCEAN +-121.27,37.96,52.0,583.0,114.0,310.0,93.0,2.5625,54200.0,INLAND +-117.83,33.77,22.0,2956.0,642.0,1342.0,558.0,4.1151,203200.0,<1H OCEAN +-117.96,33.78,33.0,1520.0,,658.0,242.0,4.875,269300.0,<1H OCEAN +-117.14,32.7,48.0,510.0,180.0,545.0,132.0,1.8008,86500.0,NEAR OCEAN +-118.6,34.16,32.0,3999.0,667.0,1628.0,631.0,6.0794,338500.0,<1H OCEAN +-118.19,33.78,21.0,2741.0,1029.0,2924.0,969.0,1.3274,218800.0,NEAR OCEAN 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+-122.45,37.71,46.0,2559.0,506.0,1562.0,498.0,4.3846,270600.0,NEAR OCEAN +-116.25,33.81,24.0,880.0,187.0,507.0,169.0,3.4583,67500.0,INLAND +-118.46,34.18,27.0,2582.0,719.0,2038.0,718.0,3.0877,174200.0,<1H OCEAN +-118.37,33.77,26.0,6339.0,876.0,2540.0,880.0,10.1447,500001.0,NEAR OCEAN +-118.43,34.28,30.0,1384.0,308.0,2054.0,301.0,3.0132,142600.0,<1H OCEAN +-119.82,34.44,22.0,2239.0,475.0,1016.0,434.0,4.875,295400.0,NEAR OCEAN +-121.98,37.31,34.0,2034.0,359.0,1016.0,375.0,5.8127,288300.0,<1H OCEAN +-122.35,37.56,52.0,1659.0,191.0,519.0,201.0,14.4219,500001.0,NEAR OCEAN +-118.44,34.15,44.0,1778.0,251.0,641.0,251.0,10.0549,500001.0,<1H OCEAN +-117.65,34.07,52.0,1041.0,252.0,558.0,231.0,1.9236,117200.0,INLAND +-118.16,34.6,2.0,11008.0,1549.0,4098.0,1367.0,6.4865,204400.0,INLAND +-118.3,33.94,36.0,2041.0,531.0,1390.0,464.0,2.0114,99300.0,<1H OCEAN +-118.06,34.06,28.0,2127.0,625.0,3160.0,620.0,2.5763,173900.0,<1H OCEAN +-118.32,34.06,43.0,2808.0,584.0,1654.0,569.0,4.125,436800.0,<1H OCEAN 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+-119.81,36.51,31.0,1241.0,254.0,767.0,226.0,2.7321,83600.0,INLAND +-118.33,33.83,5.0,13038.0,2679.0,5272.0,2523.0,5.5023,286400.0,<1H OCEAN +-117.82,33.73,24.0,845.0,190.0,482.0,190.0,4.7039,225000.0,<1H OCEAN +-117.11,32.97,9.0,1531.0,242.0,850.0,240.0,6.0862,263600.0,<1H OCEAN +-120.07,36.96,34.0,1457.0,239.0,557.0,226.0,3.6181,96500.0,INLAND +-119.3,36.31,16.0,2234.0,357.0,1150.0,361.0,4.2778,97300.0,INLAND +-117.23,32.8,28.0,3379.0,918.0,1849.0,849.0,3.0293,241700.0,NEAR OCEAN +-116.94,33.74,19.0,2901.0,445.0,1414.0,475.0,4.6406,118900.0,INLAND +-117.09,32.66,46.0,844.0,147.0,423.0,161.0,3.375,136300.0,NEAR OCEAN +-122.09,37.39,43.0,2065.0,535.0,1029.0,500.0,3.7318,327700.0,NEAR BAY +-121.97,37.55,17.0,4924.0,1247.0,3080.0,1182.0,3.168,189400.0,<1H OCEAN +-118.34,34.06,52.0,2089.0,309.0,883.0,281.0,7.4574,500001.0,<1H OCEAN +-120.43,34.93,10.0,2980.0,585.0,1593.0,562.0,3.285,218300.0,<1H OCEAN +-118.36,33.96,37.0,2146.0,573.0,2009.0,592.0,3.6583,177300.0,<1H OCEAN 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OCEAN +-118.37,33.95,32.0,1067.0,286.0,1053.0,277.0,2.8438,181700.0,<1H OCEAN +-122.43,37.72,50.0,2912.0,562.0,1989.0,537.0,3.6667,252600.0,NEAR BAY +-118.3,33.77,18.0,3821.0,795.0,2831.0,769.0,2.9571,346200.0,<1H OCEAN +-117.31,33.16,4.0,5846.0,894.0,2282.0,801.0,5.5956,247800.0,NEAR OCEAN +-118.38,33.98,25.0,7105.0,1012.0,2519.0,1004.0,6.8112,500001.0,<1H OCEAN +-121.51,38.55,45.0,3032.0,631.0,1341.0,597.0,2.8417,137900.0,INLAND +-121.42,37.73,2.0,2682.0,393.0,883.0,271.0,5.9934,196700.0,INLAND +-117.58,34.11,14.0,11635.0,2055.0,6443.0,2009.0,4.7547,157600.0,INLAND +-117.85,33.74,19.0,1248.0,357.0,1214.0,328.0,2.7059,159800.0,<1H OCEAN +-120.7,38.75,19.0,2325.0,430.0,967.0,376.0,2.9,158700.0,INLAND +-117.01,32.7,25.0,2321.0,398.0,1434.0,386.0,3.5341,120800.0,NEAR OCEAN +-118.64,34.22,25.0,2762.0,410.0,1166.0,439.0,6.8643,333700.0,<1H OCEAN +-118.04,33.85,18.0,3628.0,546.0,1922.0,544.0,7.5057,328500.0,<1H OCEAN +-122.0,37.58,6.0,4405.0,717.0,2071.0,688.0,5.8151,295600.0,<1H OCEAN 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+-121.59,39.1,24.0,1107.0,261.0,768.0,205.0,1.7167,48800.0,INLAND +-121.56,39.11,18.0,2171.0,480.0,1527.0,447.0,2.3011,57500.0,INLAND +-121.56,39.1,28.0,2130.0,484.0,1195.0,439.0,1.3631,45500.0,INLAND +-121.55,39.1,27.0,1783.0,441.0,1163.0,409.0,1.2857,47000.0,INLAND +-121.56,39.08,26.0,1377.0,289.0,761.0,267.0,1.4934,48300.0,INLAND +-121.55,39.09,31.0,1728.0,365.0,1167.0,384.0,1.4958,53400.0,INLAND +-121.54,39.08,26.0,2276.0,460.0,1455.0,474.0,2.4695,58000.0,INLAND +-121.54,39.08,23.0,1076.0,216.0,724.0,197.0,2.3598,57500.0,INLAND +-121.53,39.08,15.0,1810.0,441.0,1157.0,375.0,2.0469,55100.0,INLAND +-121.53,39.06,20.0,561.0,109.0,308.0,114.0,3.3021,70800.0,INLAND +-121.55,39.06,25.0,1332.0,247.0,726.0,226.0,2.25,63400.0,INLAND +-121.56,39.01,22.0,1891.0,340.0,1023.0,296.0,2.7303,99100.0,INLAND +-121.48,39.05,40.0,198.0,41.0,151.0,48.0,4.5625,100000.0,INLAND +-121.47,39.01,37.0,1244.0,247.0,484.0,157.0,2.3661,77500.0,INLAND +-121.44,39.0,20.0,755.0,147.0,457.0,157.0,2.4167,67000.0,INLAND +-121.37,39.03,32.0,1158.0,244.0,598.0,227.0,2.8235,65500.0,INLAND +-121.41,39.04,16.0,1698.0,300.0,731.0,291.0,3.0739,87200.0,INLAND +-121.52,39.12,37.0,102.0,17.0,29.0,14.0,4.125,72000.0,INLAND +-121.43,39.18,36.0,1124.0,184.0,504.0,171.0,2.1667,93800.0,INLAND +-121.32,39.13,5.0,358.0,65.0,169.0,59.0,3.0,162500.0,INLAND +-121.48,39.1,19.0,2043.0,421.0,1018.0,390.0,2.5952,92400.0,INLAND +-121.39,39.12,28.0,10035.0,1856.0,6912.0,1818.0,2.0943,108300.0,INLAND +-121.32,39.29,11.0,2640.0,505.0,1257.0,445.0,3.5673,112000.0,INLAND +-121.4,39.33,15.0,2655.0,493.0,1200.0,432.0,3.5179,107200.0,INLAND +-121.45,39.26,15.0,2319.0,416.0,1047.0,385.0,3.125,115600.0,INLAND +-121.53,39.19,27.0,2080.0,412.0,1082.0,382.0,2.5495,98300.0,INLAND +-121.56,39.27,28.0,2332.0,395.0,1041.0,344.0,3.7125,116800.0,INLAND +-121.09,39.48,25.0,1665.0,374.0,845.0,330.0,1.5603,78100.0,INLAND +-121.21,39.49,18.0,697.0,150.0,356.0,114.0,2.5568,77100.0,INLAND +-121.22,39.43,17.0,2254.0,485.0,1007.0,433.0,1.7,92300.0,INLAND +-121.32,39.43,18.0,1860.0,409.0,741.0,349.0,1.8672,84700.0,INLAND +-121.24,39.37,16.0,2785.0,616.0,1387.0,530.0,2.3886,89400.0,INLAND diff --git a/docs/Classification.html b/docs/Classification.html new file mode 100644 index 0000000..c088dbd --- /dev/null +++ b/docs/Classification.html @@ -0,0 +1,957 @@ + + + + + + +Code for Machine Learning and Data Science II Classification + + + + + + + + + + +
+ UP + | + HOME +
+

Code for Machine Learning and Data Science II Classification

+ +

+These are the code snippets used in Classification +part of Machine Learning and Data Science II. +

+ +
+

Introduction

+
+

+The following code uses the standard matplotlib along with the +custom ChalcedonPy which is detailed in its source code here. +

+ +

+The code is used primarily in the following code as a means to +save figures for use in slides. +

+ +
+
import matplotlib.pyplot as plt
+import ChalcedonPy as cp
+
+# Initialise ChalcedonPy
+cp.init(save_path="Classification",
+        display_mode="slide") 
+
+
+
+
+ +
+

MNIST

+
+
+
+

Download Initial Data

+
+
+
from sklearn.datasets import fetch_openml
+
+mnist = fetch_openml('mnist_784', as_frame=False)
+
+
+ +

+For more info on the sklearn.datasets.fetch_openml click here. +However for our application two parameters are used. +

+ + + + +++ ++ + + + + + + + + + + + + + + + + + +
PARAMETERDESCRIPTION
nameString identifier of the dataset. Note that OpenML can have multiple datasets with the same name.
as_frameIf True, the data is a pandas DataFrame including columns with appropriate dtypes, if false e data and target will be NumPy arrays and the data will only contain numerical values
+ +
+
mnist.keys()
+
+
+ +
+dict_keys(['data', 'target', 'frame', 'categories', 'feature_names', 'target_names', 'DESCR', 'details', 'url'])
+
+ +
+
X, y = mnist.data, mnist.target
+print(X)
+
+
+ +
+[[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]]
+
+ +
+
print(X.shape)
+
+
+ +
+(70000, 784)
+
+ +
+
print(y.shape)
+
+
+ +
+(70000,)
+
+ +
+
import matplotlib.pyplot as plt
+
+def plot_digit(image_data):
+    image = image_data.reshape(28, 28)
+    plt.imshow(image, cmap="binary")
+    plt.axis("off")
+
+some_digit = X[0]
+plot_digit(some_digit)
+cp.store_fig("some-digits-plot", close = True)
+
+
+ + +
+

some-digits-plot.png +

+
+ +
+
print(y[0])
+
+
+ +
+5
+
+ +
+
plt.figure(figsize=(9, 9))
+for idx, image_data in enumerate(X[:100]):
+    plt.subplot(10, 10, idx + 1)
+    plot_digit(image_data)
+plt.subplots_adjust(wspace=0, hspace=0)
+
+cp.store_fig("more-digits-plot",
+          close = True)
+
+
+ + +
+

more-digits-plot.png +

+
+ +
+
X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]
+
+
+
+
+ +
+

Training a Binary Classifier

+
+
+
y_train_5 = (y_train == '5')  # True for all 5s, False for all other digits
+y_test_5 = (y_test == '5')
+
+
+ +
+
from sklearn.linear_model import SGDClassifier
+
+sgd_clf = SGDClassifier(random_state=42)
+sgd_clf.fit(X_train, y_train_5)
+
+
+ +
+SGDClassifier(random_state=42)
+
+ +
+
sgd_clf.predict([some_digit])
+
+
+ +
+[ True]
+
+
+
+
+ +
+

Performance Measures

+
+
+
+

Measuring Accuracy Using Cross-Validation

+
+
+
from sklearn.model_selection import cross_val_score
+
+cross_val_score(sgd_clf, X = X_train,y =  y_train_5, cv=3, scoring="accuracy")
+
+
+ +
+[0.95035 0.96035 0.9604 ]
+
+ + + + +++ ++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
PARAMETERDESCRIPTION
estimatorThe object to use to fit the data.
XThe data to fit. Can be for example a list, or an array.
yThe target variable to try to predict in the case of supervised learning.
cvDetermines the cross-validation splitting strategy.
scoringA str (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y) which should return only a single value.
+ +
+
from sklearn.model_selection import StratifiedKFold
+from sklearn.base import clone
+
+skfolds = StratifiedKFold(n_splits=3)  # add shuffle=True if the dataset is not
+                                       # already shuffled
+for train_index, test_index in skfolds.split(X_train, y_train_5):
+    clone_clf = clone(sgd_clf)
+    X_train_folds = X_train[train_index]
+    y_train_folds = y_train_5[train_index]
+    X_test_fold = X_train[test_index]
+    y_test_fold = y_train_5[test_index]
+
+    clone_clf.fit(X_train_folds, y_train_folds)
+    y_pred = clone_clf.predict(X_test_fold)
+    n_correct = sum(y_pred == y_test_fold)
+    print(n_correct / len(y_pred))
+
+
+ +
+0.95035
+0.96035
+0.9604
+
+ +
+
from sklearn.dummy import DummyClassifier
+
+dummy_clf = DummyClassifier()
+dummy_clf.fit(X_train, y_train_5)
+print(any(dummy_clf.predict(X_train)))
+
+
+ +
+False
+
+ +
+
cross_val_score(dummy_clf, X_train, y_train_5, cv=3, scoring="accuracy")
+
+
+ +
+[0.90965 0.90965 0.90965]
+
+
+
+ +
+

Confusion Matrix

+
+
+
from sklearn.model_selection import cross_val_predict
+
+y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)
+
+
+ +
+
from sklearn.metrics import confusion_matrix
+
+cm = confusion_matrix(y_train_5, y_train_pred)
+cm
+
+
+ +
+[[53892   687]
+ [ 1891  3530]]
+
+ +
+
y_train_perfect_predictions = y_train_5  # pretend we reached perfection
+confusion_matrix(y_train_5, y_train_perfect_predictions)
+
+
+ +
+[[54579     0]
+ [    0  5421]]
+
+
+
+ +
+

Presicion and Recall

+
+
+
from sklearn.metrics import precision_score, recall_score
+
+precision_score(y_train_5, y_train_pred)  # == 3530 / (687 + 3530)
+
+
+ +
+0.8370879772350012
+
+ +
+
print(cm[1, 1] / (cm[0, 1] + cm[1, 1]))
+
+
+ +
+0.8370879772350012
+
+ +
+
print(recall_score(y_train_5, y_train_pred))
+
+
+ +
+0.6511713705958311
+
+ +
+
print(cm[1, 1] / (cm[1, 0] + cm[1, 1]))
+
+
+ +
+0.6511713705958311
+
+ +
+
from sklearn.metrics import f1_score
+
+print(f1_score(y_train_5, y_train_pred))
+
+
+ +
+0.7325171197343847
+
+ +
+
print(cm[1, 1] / (cm[1, 1] + (cm[1, 0] + cm[0, 1]) / 2))
+
+
+ +
+0.7325171197343847
+
+
+
+ +
+

Precision-Recall Trade-off

+
+
+
y_scores = sgd_clf.decision_function([some_digit])
+print(y_scores)
+
+
+ +
+[2164.22030239]
+
+ +
+
threshold = 0
+y_some_digit_pred = (y_scores > threshold)
+print(y_some_digit_pred)
+
+
+ +
+[ True]
+
+ +
+
threshold = 3000
+y_some_digit_pred = (y_scores > threshold)
+print(y_some_digit_pred)
+
+
+ +
+[False]
+
+ +
+
y_scores = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3,
+                             method="decision_function")
+
+
+ +
+
from sklearn.metrics import precision_recall_curve
+
+precisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)
+
+
+ +
+
plt.figure(figsize=(8, 4))  # extra code – it's not needed, just formatting
+plt.plot(thresholds, precisions[:-1], "b--", label="Precision", linewidth=2)
+plt.plot(thresholds, recalls[:-1], "g-", label="Recall", linewidth=2)
+plt.vlines(threshold, 0, 1.0, "k", "dotted", label="threshold")
+
+# extra code – this section just beautifies and saves Figure 3–5
+idx = (thresholds >= threshold).argmax()  # first index ≥ threshold
+plt.plot(thresholds[idx], precisions[idx], "bo")
+plt.plot(thresholds[idx], recalls[idx], "go")
+plt.axis([-50000, 50000, 0, 1])
+plt.xlabel("Threshold")
+plt.legend(loc="center right")
+
+cp.store_fig("precision-recall-comparison", close=True)
+
+
+ + +
+

precision-recall-comparison.png +

+
+ +
+
import matplotlib.patches as patches  # extra code – for the curved arrow
+
+plt.figure(figsize=(6, 5))  # extra code – not needed, just formatting
+
+plt.plot(recalls, precisions, linewidth=2, label="Precision/Recall curve")
+
+# extra code – just beautifies and saves Figure 3–6
+plt.plot([recalls[idx], recalls[idx]], [0., precisions[idx]], "k:")
+plt.plot([0.0, recalls[idx]], [precisions[idx], precisions[idx]], "k:")
+plt.plot([recalls[idx]], [precisions[idx]], "ko",
+         label="Point at threshold 3,000")
+plt.gca().add_patch(patches.FancyArrowPatch(
+    (0.79, 0.60), (0.61, 0.78),
+    connectionstyle="arc3,rad=.2",
+    arrowstyle="Simple, tail_width=1.5, head_width=8, head_length=10",
+    color="#444444"))
+plt.text(0.56, 0.62, "Higher\nthreshold", color="#333333")
+plt.xlabel("Recall")
+plt.ylabel("Precision")
+plt.axis([0, 1, 0, 1])
+
+plt.legend(loc="lower left")
+
+cp.store_fig("precision-recall-comparison-2", close=True)
+
+
+ + +
+

precision-recall-comparison-2.png +

+
+ +
+
idx_for_90_precision = (precisions >= 0.90).argmax()
+threshold_for_90_precision = thresholds[idx_for_90_precision]
+print(threshold_for_90_precision)
+
+
+ +
+3370.019499144183
+
+ +
+
y_train_pred_90 = (y_scores >= threshold_for_90_precision)
+print(precision_score(y_train_5, y_train_pred_90))
+
+
+ +
+0.9000345901072293
+
+ +
+
recall_at_90_precision = recall_score(y_train_5, y_train_pred_90)
+print(recall_at_90_precision)
+
+
+ +
+0.4799852425751706
+
+
+
+ +
+

The ROC Curve

+
+
+
from sklearn.metrics import roc_curve
+
+fpr, tpr, thresholds = roc_curve(y_train_5, y_scores)
+
+
+ +
+
idx_for_threshold_at_90 = (thresholds <= threshold_for_90_precision).argmax()
+tpr_90, fpr_90 = tpr[idx_for_threshold_at_90], fpr[idx_for_threshold_at_90]
+
+plt.figure(figsize=(6, 5))  # extra code – not needed, just formatting
+plt.plot(fpr, tpr, linewidth=2, label="ROC curve")
+plt.plot([0, 1], [0, 1], 'k:', label="Random classifier's ROC curve")
+plt.plot([fpr_90], [tpr_90], "ko", label="Threshold for 90% precision")
+
+# extra code – just beautifies and saves Figure 3–7
+plt.gca().add_patch(patches.FancyArrowPatch(
+    (0.20, 0.89), (0.07, 0.70),
+    connectionstyle="arc3,rad=.4",
+    arrowstyle="Simple, tail_width=1.5, head_width=8, head_length=10",
+    color="#444444"))
+plt.text(0.12, 0.71, "Higher\nthreshold", color="#333333")
+plt.xlabel('False Positive Rate (Fall-Out)')
+plt.ylabel('True Positive Rate (Recall)')
+plt.axis([0, 1, 0, 1])
+plt.legend(loc="lower right", fontsize=13)
+cp.store_fig("roc-curve-plot", close=True)
+
+
+ + +
+

roc-curve-plot.png +

+
+ +
+
from sklearn.metrics import roc_auc_score
+
+print(roc_auc_score(y_train_5, y_scores))
+
+
+ +
+0.9604938554008616
+
+ +
+
from sklearn.ensemble import RandomForestClassifier
+
+forest_clf = RandomForestClassifier(random_state=42)
+
+
+
+
+ +
+

Multi-Class Classification

+
+
+
from sklearn.svm import SVC
+
+svm_clf = SVC(random_state=42)
+svm_clf.fit(X_train[:2000], y_train[:2000])  
+
+
+ +
+
print(svm_clf.predict([some_digit]))
+
+
+ +
+['5']
+
+ +
+
some_digit_scores = svm_clf.decision_function([some_digit])
+print(some_digit_scores.round(2))
+
+
+ +
+[[ 3.79  0.73  6.06  8.3  -0.29  9.3   1.75  2.77  7.21  4.82]]
+
+ +
+
print(svm_clf.classes_)
+
+
+ +
+['0' '1' '2' '3' '4' '5' '6' '7' '8' '9']
+
+ + +
+
from sklearn.multiclass import OneVsRestClassifier
+
+ovr_clf = OneVsRestClassifier(SVC(random_state=42))
+ovr_clf.fit(X_train[:2000], y_train[:2000])
+
+
+ +
+
print(ovr_clf.predict([some_digit]))
+print(len(ovr_clf.estimators_))
+
+
+ +
+['5']
+10
+
+ +
+
sgd_clf = SGDClassifier(random_state=42)
+sgd_clf.fit(X_train, y_train)
+print(sgd_clf.predict([some_digit]))
+
+
+ +
+['3']
+
+ +
+
sgd_clf.decision_function([some_digit]).round()
+
+
+ +
+[[-31893. -34420.  -9531.   1824. -22320.  -1386. -26189. -16148.  -4604. -12051.]]
+
+ +
+
cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring="accuracy")
+
+
+ +
+[0.87365 0.85835 0.8689 ]
+
+
+
+
+
+
+

Author: Daniel McGuiness

+
+ + diff --git a/docs/Desicion-Trees.html b/docs/Desicion-Trees.html new file mode 100644 index 0000000..19abd97 --- /dev/null +++ b/docs/Desicion-Trees.html @@ -0,0 +1,392 @@ + + + + + + +Code for Data Science & Machine Learning II Classification + + + + + + + + + + +
+ UP + | + HOME +
+

Code for Data Science & Machine Learning II Classification

+ +

+These are the code snippets used in Lambda Calculus part of Python Programming Language. +

+ +
+

+Back to Main Menu +

+
+ + +
+

Training and Visualizing a Decision Tree

+
+
+
from sklearn.datasets import load_iris
+from sklearn.tree import DecisionTreeClassifier
+
+iris = load_iris(as_frame=True)
+X_iris = iris.data[["petal length (cm)", "petal width (cm)"]].values
+y_iris = iris.target
+
+tree_clf = DecisionTreeClassifier(max_depth=2, random_state=42)
+tree_clf.fit(X_iris, y_iris)
+
+
+ +
+
from sklearn.tree import export_graphviz
+import pydotplus
+
+dot_data = export_graphviz(
+    tree_clf,
+    out_file=str("images/iris-tree.dot"),  # path differs in the book
+    feature_names=["petal length (cm)", "petal width (cm)"],
+    class_names=iris.target_names,
+    rounded=True,
+    filled=True
+)
+
+#Export to pdf
+pydot_graph = pydotplus.graph_from_dot_data(dot_data)
+pydot_graph.write_pdf('images/iris-tree.pdf')
+
+
+ +

+images/iris-tree.pdf +

+
+
+ +
+

Making Predictions

+
+
+
import numpy as np
+import matplotlib.pyplot as plt
+
+# extra code – just formatting details
+from matplotlib.colors import ListedColormap
+custom_cmap = ListedColormap(['#fafab0', '#9898ff', '#a0faa0'])
+plt.figure(figsize=(8, 4))
+
+lengths, widths = np.meshgrid(np.linspace(0, 7.2, 100), np.linspace(0, 3, 100))
+X_iris_all = np.c_[lengths.ravel(), widths.ravel()]
+y_pred = tree_clf.predict(X_iris_all).reshape(lengths.shape)
+plt.contourf(lengths, widths, y_pred, alpha=0.3, cmap=custom_cmap)
+
+for idx, (name, style) in enumerate(zip(iris.target_names, ("yo", "bs", "g^"))):
+    plt.plot(X_iris[:, 0][y_iris == idx], X_iris[:, 1][y_iris == idx],
+             style, label=f"Iris {name}")
+
+# extra code – this section beautifies and saves Figure 6–2
+tree_clf_deeper = DecisionTreeClassifier(max_depth=3, random_state=42)
+tree_clf_deeper.fit(X_iris, y_iris)
+th0, th1, th2a, th2b = tree_clf_deeper.tree_.threshold[[0, 2, 3, 6]]
+plt.xlabel("Petal length (cm)")
+plt.ylabel("Petal width (cm)")
+plt.plot([th0, th0], [0, 3], "k-", linewidth=2)
+plt.plot([th0, 7.2], [th1, th1], "k--", linewidth=2)
+plt.plot([th2a, th2a], [0, th1], "k:", linewidth=2)
+plt.plot([th2b, th2b], [th1, 3], "k:", linewidth=2)
+plt.text(th0 - 0.05, 1.0, "Depth=0", horizontalalignment="right", fontsize=15)
+plt.text(3.2, th1 + 0.02, "Depth=1", verticalalignment="bottom", fontsize=13)
+plt.text(th2a + 0.05, 0.5, "(Depth=2)", fontsize=11)
+plt.axis([0, 7.2, 0, 3])
+plt.legend()
+plt.savefig("images/desicion-tree-boundaries-plot.pdf")
+
+
+ +

+images/desicion-tree-boundaries-plot.pdf +

+
+ +
+

Regularization Hyperparameters

+
+
+
from sklearn.datasets import make_moons
+
+X_moons, y_moons = make_moons(n_samples=150, noise=0.2, random_state=42)
+
+tree_clf1 = DecisionTreeClassifier(random_state=42)
+tree_clf2 = DecisionTreeClassifier(min_samples_leaf=5, random_state=42)
+tree_clf1.fit(X_moons, y_moons)
+tree_clf2.fit(X_moons, y_moons)
+
+
+ +
+
def plot_decision_boundary(clf, X, y, axes, cmap):
+    x1, x2 = np.meshgrid(np.linspace(axes[0], axes[1], 100),
+                         np.linspace(axes[2], axes[3], 100))
+    X_new = np.c_[x1.ravel(), x2.ravel()]
+    y_pred = clf.predict(X_new).reshape(x1.shape)
+
+    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=cmap)
+    plt.contour(x1, x2, y_pred, cmap="Greys", alpha=0.8)
+    colors = {"Wistia": ["#78785c", "#c47b27"], "Pastel1": ["red", "blue"]}
+    markers = ("o", "^")
+    for idx in (0, 1):
+        plt.plot(X[:, 0][y == idx], X[:, 1][y == idx],
+                 color=colors[cmap][idx], marker=markers[idx], linestyle="none")
+    plt.axis(axes)
+    plt.xlabel(r"$x_1$")
+    plt.ylabel(r"$x_2$", rotation=0)
+
+fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)
+plt.sca(axes[0])
+plot_decision_boundary(tree_clf1, X_moons, y_moons,
+                       axes=[-1.5, 2.4, -1, 1.5], cmap="Wistia")
+plt.title("No restrictions")
+plt.sca(axes[1])
+plot_decision_boundary(tree_clf2, X_moons, y_moons,
+                       axes=[-1.5, 2.4, -1, 1.5], cmap="Wistia")
+plt.title(f"min_samples_leaf = {tree_clf2.min_samples_leaf}")
+plt.ylabel("")
+plt.savefig("images/min-samples-leaf-plot.pdf")
+
+
+ +

+images/min-samples-leaf-plot.pdf +

+
+
+
+
+
+

Author: Daniel McGuiness

+
+ + diff --git a/docs/Dimensionality-Reduction.html b/docs/Dimensionality-Reduction.html new file mode 100644 index 0000000..19d992b --- /dev/null +++ b/docs/Dimensionality-Reduction.html @@ -0,0 +1,508 @@ + + + + + + +Code for Machine Learning and Data Science II Dimensionality Reduction + + + + + + + + + + +
+ UP + | + HOME +
+

Code for Machine Learning and Data Science II Dimensionality Reduction

+
+

Table of Contents

+
+ +
+
+

+These are the code snippets used in Dimensionality Reduction +part of Machine Learning and Data Science II. +

+ +
+

Introduction

+
+

+The following is a custom package written to handle plotting +and other functions required by the lecture. +

+ +
+
import ChalcedonPy as cp # custom-pakcage for lecture materials and publications
+SAVE_PATH = "Dimensionality-Reduction" # sets the default save path
+style="web" # sets the default rcParams stylce sheet
+
+
+
+ +
+

Principle Component Analysis

+
+
+
import numpy as np
+from scipy.spatial.transform import Rotation
+
+m = 60
+X = np.zeros((m, 3))  # initialize 3D dataset
+np.random.seed(42)
+angles = (np.random.rand(m) ** 3 + 0.5) * 2 * np.pi  # uneven distribution
+X[:, 0], X[:, 1] = np.cos(angles), np.sin(angles) * 0.5  # oval
+X += 0.28 * np.random.randn(m, 3)  # add more noise
+X = Rotation.from_rotvec([np.pi / 29, -np.pi / 20, np.pi / 4]).apply(X)
+X += [0.2, 0, 0.2]  # shift a bit
+
+
+ +

+Plot the 3D dataset, with the projection plane. +

+ +
+
import matplotlib.pyplot as plt
+from sklearn.decomposition import PCA
+
+pca = PCA(n_components=2)
+X2D = pca.fit_transform(X)  # dataset reduced to 2D
+X3D_inv = pca.inverse_transform(X2D)  # 3D position of the projected samples
+X_centered = X - X.mean(axis=0)
+U, s, Vt = np.linalg.svd(X_centered)
+
+axes = [-1.4, 1.4, -1.4, 1.4, -1.1, 1.1]
+x1, x2 = np.meshgrid(np.linspace(axes[0], axes[1], 10),
+                     np.linspace(axes[2], axes[3], 10))
+w1, w2 = np.linalg.solve(Vt[:2, :2], Vt[:2, 2])  # projection plane coefs
+z = w1 * (x1 - pca.mean_[0]) + w2 * (x2 - pca.mean_[1]) - pca.mean_[2]  # plane
+X3D_above = X[X[:, 2] >= X3D_inv[:, 2]]  # samples above plane
+X3D_below = X[X[:, 2] < X3D_inv[:, 2]]  # samples below plane
+
+fig = plt.figure(figsize=(9, 9))
+ax = fig.add_subplot(111, projection="3d")
+
+# plot samples and projection lines below plane first
+ax.plot(X3D_below[:, 0], X3D_below[:, 1], X3D_below[:, 2], "ro", alpha=0.3)
+for i in range(m):
+    if X[i, 2] < X3D_inv[i, 2]:
+        ax.plot([X[i][0], X3D_inv[i][0]],
+                [X[i][1], X3D_inv[i][1]],
+                [X[i][2], X3D_inv[i][2]], ":", color="#F88")
+
+ax.plot_surface(x1, x2, z, alpha=0.1, color="b")  # projection plane
+ax.plot(X3D_inv[:, 0], X3D_inv[:, 1], X3D_inv[:, 2], "b+")  # projected samples
+ax.plot(X3D_inv[:, 0], X3D_inv[:, 1], X3D_inv[:, 2], "b.")
+
+# now plot projection lines and samples above plane
+for i in range(m):
+    if X[i, 2] >= X3D_inv[i, 2]:
+        ax.plot([X[i][0], X3D_inv[i][0]],
+                [X[i][1], X3D_inv[i][1]],
+                [X[i][2], X3D_inv[i][2]], "r--")
+
+ax.plot(X3D_above[:, 0], X3D_above[:, 1], X3D_above[:, 2], "ro")
+
+def set_xyz_axes(ax, axes):
+    ax.xaxis.set_rotate_label(False)
+    ax.yaxis.set_rotate_label(False)
+    ax.zaxis.set_rotate_label(False)
+    ax.set_xlabel("$x_1$", labelpad=8, rotation=0)
+    ax.set_ylabel("$x_2$", labelpad=8, rotation=0)
+    ax.set_zlabel("$x_3$", labelpad=8, rotation=0)
+    ax.set_xlim(axes[0:2])
+    ax.set_ylim(axes[2:4])
+    ax.set_zlim(axes[4:6])
+
+set_xyz_axes(ax, axes)
+ax.set_zticks([-1, -0.5, 0, 0.5, 1])
+cp.store_fig("dataset-3d-plot",
+             filepath = SAVE_PATH,
+             style = style,
+             close = True)
+
+
+ + +
+

dataset-3d-plot.png +

+
+ +
+
fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1, aspect='equal')
+ax.plot(X2D[:, 0], X2D[:, 1], "b+")
+ax.plot(X2D[:, 0], X2D[:, 1], "b.")
+ax.plot([0], [0], "bo")
+ax.arrow(0, 0, 1, 0, head_width=0.05, length_includes_head=True,
+         head_length=0.1, fc='b', ec='b', linewidth=4)
+ax.arrow(0, 0, 0, 1, head_width=0.05, length_includes_head=True,
+         head_length=0.1, fc='b', ec='b', linewidth=1)
+ax.set_xlabel("$z_1$")
+ax.set_yticks([-0.5, 0, 0.5, 1])
+ax.set_ylabel("$z_2$", rotation=0)
+
+cp.store_fig("dataset-2d-plot",
+             filepath = SAVE_PATH,
+             style = style,
+             close = True)
+
+
+ + +
+

dataset-2d-plot.png +

+
+ +
+
from sklearn.datasets import make_swiss_roll
+
+X_swiss, t = make_swiss_roll(n_samples=1000, noise=0.2, random_state=42)
+
+
+ +
+
from matplotlib.colors import ListedColormap
+
+darker_hot = ListedColormap(plt.cm.hot(np.linspace(0, 0.8, 256)))
+
+axes = [-11.5, 14, -2, 23, -12, 15]
+
+fig = plt.figure(figsize=(6, 5))
+ax = fig.add_subplot(111, projection='3d')
+
+ax.scatter(X_swiss[:, 0], X_swiss[:, 1], X_swiss[:, 2], c=t, cmap=darker_hot)
+ax.view_init(10, -70)
+set_xyz_axes(ax, axes)
+
+cp.store_fig("swiss-roll-plot",
+             filepath = SAVE_PATH,
+             style = style,
+             close = True)
+
+
+ + +
+

swiss-roll-plot.png +

+
+ +
+
plt.figure(figsize=(10, 4))
+
+plt.subplot(121)
+plt.scatter(X_swiss[:, 0], X_swiss[:, 1], c=t, cmap=darker_hot)
+plt.axis(axes[:4])
+plt.xlabel("$x_1$")
+plt.ylabel("$x_2$", labelpad=10, rotation=0)
+
+plt.subplot(122)
+plt.scatter(t, X_swiss[:, 1], c=t, cmap=darker_hot)
+plt.axis([4, 14.8, axes[2], axes[3]])
+plt.xlabel("$z_1$")
+
+cp.store_fig("squished-swiss-roll-plot",
+             filepath = SAVE_PATH,
+             style = style,
+             close = True)
+
+
+ + +
+

squished-swiss-roll-plot.png +

+
+ +
+
axes = [-11.5, 14, -2, 23, -12, 15]
+x2s = np.linspace(axes[2], axes[3], 10)
+x3s = np.linspace(axes[4], axes[5], 10)
+x2, x3 = np.meshgrid(x2s, x3s)
+
+positive_class = X_swiss[:, 0] > 5
+X_pos = X_swiss[positive_class]
+X_neg = X_swiss[~positive_class]
+
+fig = plt.figure(figsize=(6, 5))
+ax = plt.subplot(1, 1, 1, projection='3d')
+ax.view_init(10, -70)
+ax.plot(X_neg[:, 0], X_neg[:, 1], X_neg[:, 2], "y^")
+ax.plot_wireframe(5, x2, x3, alpha=0.5)
+ax.plot(X_pos[:, 0], X_pos[:, 1], X_pos[:, 2], "gs")
+set_xyz_axes(ax, axes)
+save_fig("manifold_decision_boundary_plot1")
+plt.show()
+
+fig = plt.figure(figsize=(5, 4))
+ax = plt.subplot(1, 1, 1)
+ax.plot(t[positive_class], X_swiss[positive_class, 1], "gs")
+ax.plot(t[~positive_class], X_swiss[~positive_class, 1], "y^")
+ax.axis([4, 15, axes[2], axes[3]])
+ax.set_xlabel("$z_1$")
+ax.set_ylabel("$z_2$", rotation=0, labelpad=8)
+ax.grid(True)
+save_fig("manifold_decision_boundary_plot2")
+plt.show()
+
+positive_class = 2 * (t[:] - 4) > X_swiss[:, 1]
+X_pos = X_swiss[positive_class]
+X_neg = X_swiss[~positive_class]
+
+fig = plt.figure(figsize=(6, 5))
+ax = plt.subplot(1, 1, 1, projection='3d')
+ax.view_init(10, -70)
+ax.plot(X_neg[:, 0], X_neg[:, 1], X_neg[:, 2], "y^")
+ax.plot(X_pos[:, 0], X_pos[:, 1], X_pos[:, 2], "gs")
+ax.xaxis.set_rotate_label(False)
+ax.yaxis.set_rotate_label(False)
+ax.zaxis.set_rotate_label(False)
+ax.set_xlabel("$x_1$", rotation=0)
+ax.set_ylabel("$x_2$", rotation=0)
+ax.set_zlabel("$x_3$", rotation=0)
+ax.set_xlim(axes[0:2])
+ax.set_ylim(axes[2:4])
+ax.set_zlim(axes[4:6])
+save_fig("manifold_decision_boundary_plot3")
+plt.show()
+
+fig = plt.figure(figsize=(5, 4))
+ax = plt.subplot(1, 1, 1)
+ax.plot(t[positive_class], X_swiss[positive_class, 1], "gs")
+ax.plot(t[~positive_class], X_swiss[~positive_class, 1], "y^")
+ax.plot([4, 15], [0, 22], "b-", linewidth=2)
+ax.axis([4, 15, axes[2], axes[3]])
+ax.set_xlabel("$z_1$")
+ax.set_ylabel("$z_2$", rotation=0, labelpad=8)
+ax.grid(True)
+save_fig("manifold_decision_boundary_plot4")
+plt.show()
+
+
+
+
+
+
+
+

Author: Daniel McGuiness

+
+ + diff --git a/docs/End-to-End-ML-Project.html b/docs/End-to-End-ML-Project.html index cf4670a..620352f 100644 --- a/docs/End-to-End-ML-Project.html +++ b/docs/End-to-End-ML-Project.html @@ -343,11 +343,11 @@

Download Initial Data

-
print(housing.head())
+
print(housing.head())
-
+
    longitude  latitude  ...  median_house_value  ocean_proximity
 0    -122.23     37.88  ...            452600.0         NEAR BAY
 1    -122.22     37.86  ...            358500.0         NEAR BAY
@@ -383,11 +383,11 @@ 
Download Initial Data

 

 
 

-
print(housing.info())
+
print(housing.info())
 

 

 
-
+
 <class 'pandas.core.frame.DataFrame'>
 RangeIndex: 20640 entries, 0 to 20639
 Data columns (total 10 columns):
@@ -418,11 +418,11 @@ 
Download Initial Data

 

 
 

-
print(housing["ocean_proximity"].value_counts())
+
print(housing["ocean_proximity"].value_counts())
 

 

 
-
+
 ocean_proximity
 <1H OCEAN     9136
 INLAND        6551
@@ -438,11 +438,11 @@ 
Download Initial Data

 

 
 

-
print(housing.describe)
+
print(housing.describe)
 

 

 
-
+
 <bound method NDFrame.describe of        longitude  latitude  housing_median_age  total_rooms  total_bedrooms  population  households  median_income  median_house_value ocean_proximity
 0        -122.23     37.88                41.0        880.0           129.0       322.0       126.0         8.3252            452600.0        NEAR BAY
 1        -122.22     37.86                21.0       7099.0          1106.0      2401.0      1138.0         8.3014            358500.0        NEAR BAY
@@ -463,7 +463,7 @@ 
Download Initial Data

 

 
 

-
import matplotlib.pyplot as plt
+
import matplotlib.pyplot as plt
 
 housing.hist(bins=50,  figsize=(12, 8))
 
@@ -472,7 +472,7 @@ 
Download Initial Data

 

 
 
-

+

 
attribute-histogram-plots.png
 

 

@@ -510,12 +510,12 @@ 
Create a Set for Testing

 

 
 

-
print("The size of the training set is:", len(train_set))
+
print("The size of the training set is:", len(train_set))
 print("The size of the test data is:", len(test_set))
 

 

 
-
+
 The size of the training set is: 16512
 The size of the test data is: 4128
 

@@ -598,7 +598,7 @@ 
Create a Set for Testing

 

 
 

-
from scipy.stats import binom
+
from scipy.stats import binom
 
 sample_size = 1000
 ratio_female = 0.511
@@ -608,7 +608,7 @@ 
Create a Set for Testing

 

 

 
-
+
 0.10736798530930072
 

 
@@ -618,14 +618,14 @@ 
Create a Set for Testing

 

 
 

-
np.random.seed(42)
+
np.random.seed(42)
 
 samples = (np.random.rand(100_000, sample_size) < ratio_female).sum(axis=1)
 ((samples < 485) | (samples > 535)).mean()
 

 

 
-
+
 0.1071
 

 
@@ -638,7 +638,7 @@ 
Create a Set for Testing

 

 
 

-
plt.xlabel("Income category")
+
plt.xlabel("Income category")
 plt.ylabel("Number of districts")
 housing["income_cat"].value_counts().sort_index().plot.bar(rot=0, grid=True)
 
@@ -647,7 +647,7 @@ 
Create a Set for Testing

 

 
 
-

+

 
housing-income-cat-bar-plot.png
 

 

@@ -685,11 +685,11 @@ 
Create a Set for Testing

 

 
 

-
strat_test_set["income_cat"].value_counts() / len(strat_test_set)
+
strat_test_set["income_cat"].value_counts() / len(strat_test_set)
 

 

 
-
+
 income_cat
 3    0.350533
 2    0.318798
@@ -700,7 +700,7 @@ 
Create a Set for Testing

 

 
 

-
def income_cat_proportions(data):
+
def income_cat_proportions(data):
     return data["income_cat"].value_counts() / len(data)
 
 train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)
@@ -719,7 +719,7 @@ 
Create a Set for Testing

 

 

 
-
+
                  Overall %  Stratified %  Random %  Strat. Error %  Rand. Error %
 Income Category                                                                  
 1                     3.98          4.00      4.24            0.36           6.45
@@ -766,7 +766,7 @@ 
Visualising Geographical Data

 

 
 

-
housing.plot(kind = "scatter",
+
housing.plot(kind = "scatter",
              x = "longitude",
              y = "latitude",
              title = "Housing Market in California")
@@ -776,7 +776,7 @@ 
Visualising Geographical Data

 

 
 
-

+

 
initial-visualisation-plot.png
 

 

@@ -792,7 +792,7 @@ 
Visualising Geographical Data

 

 
 

-
housing.plot(kind = "scatter",
+
housing.plot(kind = "scatter",
              x = "longitude",
              y = "latitude",
              title = r"Housing Market in California ($\alpha = 0.2$)",
@@ -803,7 +803,7 @@ 
Visualising Geographical Data

 

 
 
-

+

 
initial-visualisation-plot-with-alpha.png
 

 

@@ -815,7 +815,7 @@ 
Visualising Geographical Data

 

 
 

-
housing.plot(kind="scatter", x="longitude", y="latitude",
+
housing.plot(kind="scatter", x="longitude", y="latitude",
              s=housing["population"] / 100, label="population",
              c="median_house_value", cmap="jet", colorbar=True,
              legend=True, sharex=False, figsize=(10, 7))
@@ -825,7 +825,7 @@ 
Visualising Geographical Data

 

 
 
-

+

 
housing-prices-scatterplot.png
 

 

@@ -837,7 +837,7 @@ 
Visualising Geographical Data

 

 
 

-
filename = "california.png"
+
filename = "california.png"
 
 if not (IMAGES_PATH / filename).is_file():
     homl3_root = "https://github.com/ageron/handson-ml3/raw/main/"
@@ -869,7 +869,7 @@ 
Visualising Geographical Data

 

 
 
-

+

 
california-housing-prices-plot.png
 

 

@@ -891,12 +891,12 @@ 
Looking for Correlations

 

 
 

-
corr_matrix = housing.corr(numeric_only=True)
+
corr_matrix = housing.corr(numeric_only=True)
 corr_matrix["median_house_value"].sort_values(ascending=False)
 

 

 
-
+
 median_house_value    1.000000
 median_income         0.688380
 total_rooms           0.137455
@@ -915,7 +915,7 @@ 
Looking for Correlations

 

 
 

-
from pandas.plotting import scatter_matrix
+
from pandas.plotting import scatter_matrix
 
 attributes = ["median_house_value", "median_income", "total_rooms",
               "housing_median_age"]
@@ -926,7 +926,7 @@ 
Looking for Correlations

 

 
 
-

+

 
scatter-matrix-plot.png
 

 

@@ -938,14 +938,17 @@ 
Looking for Correlations

 

 
 

-
housing.plot(kind="scatter", x="median_income", y="median_house_value",
+
housing.plot(kind="scatter",
+             x="median_income",
+             y="median_house_value",
              alpha=0.1, grid=True)
-cp.store_fig("income-vs-house-value-scatterplot", close=True)  # extra code
+
+cp.store_fig("income-vs-house-value-scatterplot", close=True) 
 

 

 
 
-

+

 
income-vs-house-value-scatterplot.png
 

 

@@ -963,12 +966,12 @@ 
Experimenting with Attribute Combinations

 

 
 

-
corr_matrix = housing.corr(numeric_only=True)
+
corr_matrix = housing.corr(numeric_only=True)
 corr_matrix["median_house_value"].sort_values(ascending=False)
 

 

 
-
+
 median_house_value    1.000000
 median_income         0.688380
 rooms_per_house       0.143663
@@ -1016,12 +1019,12 @@ 
Data Cleaning

 

 
 

-
null_rows_idx = housing.isnull().any(axis=1)
+
null_rows_idx = housing.isnull().any(axis=1)
 housing.loc[null_rows_idx].head()
 

 

 
-
+
        longitude  latitude  housing_median_age  total_rooms  total_bedrooms  population  households  median_income ocean_proximity
 14452    -120.67     40.50                15.0       5343.0             NaN      2503.0       902.0         3.5962          INLAND
 18217    -117.96     34.03                35.0       2093.0             NaN      1755.0       403.0         3.4115       <1H OCEAN
@@ -1039,13 +1042,13 @@ 
Data Cleaning

 

 
 

-
housing_option1 = housing.copy()
-housing_option1.dropna(subset=["total_bedrooms"], inplace=True)  # option 1
+
housing_option1 = housing.copy()
+housing_option1.dropna(subset=["total_bedrooms"], inplace=True)
 housing_option1.loc[null_rows_idx].head()
 

 

 
-
+
 Empty DataFrame
 Columns: [longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, ocean_proximity]
 Index: []
@@ -1056,13 +1059,13 @@ 
Data Cleaning

 

 
 

-
housing_option2 = housing.copy()
-housing_option2.drop("total_bedrooms", axis=1, inplace=True)  # option 2
+
housing_option2 = housing.copy()
+housing_option2.drop("total_bedrooms", axis=1, inplace=True)
 housing_option2.loc[null_rows_idx].head()
 

 

 
-
+
        longitude  latitude  housing_median_age  total_rooms  population  households  median_income ocean_proximity
 14452    -120.67     40.50                15.0       5343.0      2503.0       902.0         3.5962          INLAND
 18217    -117.96     34.03                35.0       2093.0      1755.0       403.0         3.4115       <1H OCEAN
@@ -1076,15 +1079,15 @@ 
Data Cleaning

 

 
 

-
housing_option3 = housing.copy()
+
housing_option3 = housing.copy()
 median = housing["total_bedrooms"].median()
-housing_option3["total_bedrooms"].fillna(median, inplace=True)  # option 3
+housing_option3["total_bedrooms"].fillna(median, inplace=True)
 
 housing_option3.loc[null_rows_idx].head()
 

 

 
-
+
        longitude  latitude  housing_median_age  total_rooms  total_bedrooms  population  households  median_income ocean_proximity
 14452    -120.67     40.50                15.0       5343.0           434.0      2503.0       902.0         3.5962          INLAND
 18217    -117.96     34.03                35.0       2093.0           434.0      1755.0       403.0         3.4115       <1H OCEAN
@@ -1134,21 +1137,21 @@ 
Data Cleaning

 

 
 

-
print(imputer.statistics_)
+
print(imputer.statistics_)
 

 

 
-
+
 [-118.51     34.26     29.     2125.      434.     1167.      408.
     3.5385]
 

 
 

-
print(housing_num.median().values)
+
print(housing_num.median().values)
 

 

 
-
+
 [-118.51     34.26     29.     2125.      434.     1167.      408.
     3.5385]
 

@@ -1159,13 +1162,13 @@ 
Data Cleaning

 

 
 

-
X = imputer.transform(housing_num)
+
X = imputer.transform(housing_num)
 # to see the name of the columns
 print(imputer.feature_names_in_)
 

 

 
-
+
 ['longitude' 'latitude' 'housing_median_age' 'total_rooms'
  'total_bedrooms' 'population' 'households' 'median_income']
 

@@ -1181,11 +1184,11 @@ 
Data Cleaning

 

 
 

-
housing_tr.loc[null_rows_idx].head()
+
housing_tr.loc[null_rows_idx].head()
 

 

 
-
+
        longitude  latitude  housing_median_age  total_rooms  total_bedrooms  population  households  median_income
 14452    -120.67     40.50                15.0       5343.0           434.0      2503.0       902.0         3.5962
 18217    -117.96     34.03                35.0       2093.0           434.0      1755.0       403.0         3.4115
@@ -1195,23 +1198,23 @@ 
Data Cleaning

 

 
 

-
imputer.strategy
+
imputer.strategy
 

 

 
-
+
 median
 

 
 

-
housing_tr = pd.DataFrame(X, columns=housing_num.columns,
+
housing_tr = pd.DataFrame(X, columns=housing_num.columns,
                           index=housing_num.index)
 
 housing_tr.loc[null_rows_idx].head() 
 

 

 
-
+
        longitude  latitude  housing_median_age  total_rooms  total_bedrooms  population  households  median_income
 14452    -120.67     40.50                15.0       5343.0           434.0      2503.0       902.0         3.5962
 18217    -117.96     34.03                35.0       2093.0           434.0      1755.0       403.0         3.4115
@@ -1225,7 +1228,7 @@ 
Data Cleaning

 

 
 

-
from sklearn.ensemble import IsolationForest
+
from sklearn.ensemble import IsolationForest
 
 isolation_forest = IsolationForest(random_state=42)
 outlier_pred = isolation_forest.fit_predict(X)
@@ -1234,7 +1237,7 @@ 
Data Cleaning

 

 

 
-
+
 [-1  1  1 ...  1  1  1]
 

 

@@ -1253,12 +1256,12 @@ 
Handling Text and Categorical Attributes

 

 
 

-
housing_cat = housing[["ocean_proximity"]]
+
housing_cat = housing[["ocean_proximity"]]
 housing_cat.head(8)
 

 

 
-
+
       ocean_proximity
 13096        NEAR BAY
 14973       <1H OCEAN
@@ -1288,11 +1291,11 @@ 
Handling Text and Categorical Attributes

 

 
 

-
print(housing_cat_encoded[:8])
+
print(housing_cat_encoded[:8])
 

 

 
-
+
 [[3.]
  [0.]
  [1.]
@@ -1308,11 +1311,11 @@ 
Handling Text and Categorical Attributes

 

 
 

-
print(ordinal_encoder.categories_)
+
print(ordinal_encoder.categories_)
 

 

 
-
+
 [array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],
       dtype=object)]
 

@@ -1330,11 +1333,11 @@ 
Handling Text and Categorical Attributes

 

 
 

-
type(housing_cat_1hot)
+
type(housing_cat_1hot)
 

 

 
-
+
 <class 'scipy.sparse._csr.csr_matrix'>
 

 
@@ -1347,11 +1350,11 @@ 
Handling Text and Categorical Attributes

 

 
 

-
print(housing_cat_1hot.toarray())
+
print(housing_cat_1hot.toarray())
 

 

 
-
+
 [[0. 0. 0. 1. 0.]
  [1. 0. 0. 0. 0.]
  [0. 1. 0. 0. 0.]
@@ -1366,13 +1369,13 @@ 
Handling Text and Categorical Attributes

 

 
 

-
cat_encoder = OneHotEncoder(sparse_output=False)
+
cat_encoder = OneHotEncoder(sparse_output=False)
 housing_cat_1hot = cat_encoder.fit_transform(housing_cat)
 print(housing_cat_1hot)
 

 

 
-
+
 [[0. 0. 0. 1. 0.]
  [1. 0. 0. 0. 0.]
  [0. 1. 0. 0. 0.]
@@ -1383,82 +1386,82 @@ 
Handling Text and Categorical Attributes

 

 
 

-
print(cat_encoder.categories_)
+
print(cat_encoder.categories_)
 

 

 
-
+
 [array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],
       dtype=object)]
 

 
 

-
df_test = pd.DataFrame({"ocean_proximity": ["INLAND", "NEAR BAY"]})
+
df_test = pd.DataFrame({"ocean_proximity": ["INLAND", "NEAR BAY"]})
 pd.get_dummies(df_test)
 

 

 
-
+
    ocean_proximity_INLAND  ocean_proximity_NEAR BAY
 0                    True                     False
 1                   False                      True
 

 
 

-
print(cat_encoder.transform(df_test))
+
print(cat_encoder.transform(df_test))
 

 

 
-
+
 [[0. 1. 0. 0. 0.]
  [0. 0. 0. 1. 0.]]
 

 
 

-
df_test_unknown = pd.DataFrame({"ocean_proximity": ["<2H OCEAN", "ISLAND"]})
+
df_test_unknown = pd.DataFrame({"ocean_proximity": ["<2H OCEAN", "ISLAND"]})
 pd.get_dummies(df_test_unknown)
 

 

 
-
+
    ocean_proximity_<2H OCEAN  ocean_proximity_ISLAND
 0                       True                   False
 1                      False                    True
 

 
 

-
cat_encoder.handle_unknown = "ignore"
+
cat_encoder.handle_unknown = "ignore"
 print(cat_encoder.transform(df_test_unknown))
 

 

 
-
+
 [[0. 0. 0. 0. 0.]
  [0. 0. 1. 0. 0.]]
 

 
 

-
print(cat_encoder.feature_names_in_)
+
print(cat_encoder.feature_names_in_)
 

 

 
-
+
 ['ocean_proximity']
 

 
 

-
print(cat_encoder.get_feature_names_out())
+
print(cat_encoder.get_feature_names_out())
 

 

 
-
+
 ['ocean_proximity_<1H OCEAN' 'ocean_proximity_INLAND'
  'ocean_proximity_ISLAND' 'ocean_proximity_NEAR BAY'
  'ocean_proximity_NEAR OCEAN']
 

 
 

-
df_output = pd.DataFrame(cat_encoder.transform(df_test_unknown),
+
df_output = pd.DataFrame(cat_encoder.transform(df_test_unknown),
                          columns=cat_encoder.get_feature_names_out(),
                          index=df_test_unknown.index)
 
@@ -1466,7 +1469,7 @@ 
Handling Text and Categorical Attributes

 

 

 
-
+
    ocean_proximity_<1H OCEAN  ocean_proximity_INLAND  ocean_proximity_ISLAND  ocean_proximity_NEAR BAY  ocean_proximity_NEAR OCEAN
 0                        0.0                     0.0                     0.0                       0.0                         0.0
 1                        0.0                     0.0                     1.0                       0.0                         0.0
@@ -1494,7 +1497,7 @@ 
Feature Scaling

 

 
 

-
fig, axs = plt.subplots(1, 2, figsize=(8, 3), sharey=True)
+
fig, axs = plt.subplots(1, 2, figsize=(8, 3), sharey=True)
 housing["population"].hist(ax=axs[0], bins=50)
 housing["population"].apply(np.log).hist(ax=axs[1], bins=50)
 axs[0].set_xlabel("Population")
@@ -1505,13 +1508,13 @@ 
Feature Scaling

 

 
 
-

+

 
long-tail-plot.png
 

 

 
 

-
percentiles = [np.percentile(housing["median_income"], p)
+
percentiles = [np.percentile(housing["median_income"], p)
                for p in range(1, 100)]
 flattened_median_income = pd.cut(housing["median_income"],
                                  bins=[-np.inf] + percentiles + [np.inf],
@@ -1524,7 +1527,7 @@ 
Feature Scaling

 

 
 
-

+

 
long-tail-dist.png
 

 

@@ -1537,7 +1540,7 @@ 
Feature Scaling

 

 
 

-
ages = np.linspace(housing["housing_median_age"].min(),
+
ages = np.linspace(housing["housing_median_age"].min(),
                    housing["housing_median_age"].max(),
                    500).reshape(-1, 1)
 gamma1 = 0.1
@@ -1564,7 +1567,7 @@ 
Feature Scaling

 

 
 
-

+

 
age-similarity-plot.png
 

 

@@ -1585,11 +1588,11 @@ 
Feature Scaling

 

 
 

-
print(predictions)
+
print(predictions)
 

 

 
-
+
 [[131997.15275877]
  [299359.35844434]
  [146023.37185694]
@@ -1608,11 +1611,11 @@ 
Feature Scaling

 

 
 

-
print(predictions)
+
print(predictions)
 

 

 
-
+
 [131997.15275877 299359.35844434 146023.37185694 138840.33653057
  192016.61557639]
 

@@ -1638,11 +1641,11 @@ 
Custom Transformers

 

 
 

-
print(age_simil_35)
+
print(age_simil_35)
 

 

 
-
+
 [[2.81118530e-13]
  [8.20849986e-02]
  [6.70320046e-01]
@@ -1661,11 +1664,11 @@ 
Custom Transformers

 
 
 

-
print(sf_simil)
+
print(sf_simil)
 

 

 
-
+
 [[0.999927  ]
  [0.05258419]
  [0.94864161]
@@ -1676,12 +1679,12 @@ 
Custom Transformers

 

 
 

-
ratio_transformer = FunctionTransformer(lambda X: X[:, [0]] / X[:, [1]])
+
ratio_transformer = FunctionTransformer(lambda X: X[:, [0]] / X[:, [1]])
 print(ratio_transformer.transform(np.array([[1., 2.], [3., 4.]])))
 

 

 
-
+
 [[0.5 ]
  [0.75]]
 

@@ -1747,7 +1750,7 @@ 
Custom Transformers

 
 
 

-
housing_renamed = housing.rename(columns={
+
housing_renamed = housing.rename(columns={
     "latitude": "Latitude", "longitude": "Longitude",
     "population": "Population",
     "median_house_value": "Median house value (ᴜsᴅ)"})
@@ -1768,7 +1771,7 @@ 
Custom Transformers

 

 
 
-

+

 
district-cluster-plot.png
 

 

@@ -1800,7 +1803,7 @@ 
Transformation Pipelines

 

 
 

-
from sklearn import set_config
+
from sklearn import set_config
 
 set_config(display='diagram')
 
@@ -1808,18 +1811,18 @@ 
Transformation Pipelines

 

 

 
-
+
 Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),
                 ('standardscaler', StandardScaler())])
 

 
 

-
housing_num_prepared = num_pipeline.fit_transform(housing_num)
+
housing_num_prepared = num_pipeline.fit_transform(housing_num)
 print(housing_num_prepared[:2].round(2))
 

 

 
-
+
 [[-1.42  1.01  1.86  0.31  1.37  0.14  1.39 -0.94]
  [ 0.6  -0.7   0.91 -0.31 -0.44 -0.69 -0.37  1.17]]
 

@@ -1875,58 +1878,58 @@ 
Transformation Pipelines

 
 
 

-
df_housing_num_prepared.head(2)  
+
df_housing_num_prepared.head(2)  
 

 

 
-
+
        longitude  latitude  housing_median_age  total_rooms  total_bedrooms  population  households  median_income
 13096  -1.423037  1.013606            1.861119     0.311912        1.368167    0.137460    1.394812      -0.936491
 14973   0.596394 -0.702103            0.907630    -0.308620       -0.435925   -0.693771   -0.373485       1.171942
 

 
 

-
print(num_pipeline.steps)
+
print(num_pipeline.steps)
 

 

 
-
+
 [('simpleimputer', SimpleImputer(strategy='median')), ('standardscaler', StandardScaler())]
 

 
 

-
num_pipeline[1]
+
num_pipeline[1]
 

 

 
-
+
 StandardScaler()
 

 
 

-
num_pipeline[:-1]
+
num_pipeline[:-1]
 

 

 
-
+
 Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median'))])
 

 
 

-
num_pipeline.named_steps["simpleimputer"]
+
num_pipeline.named_steps["simpleimputer"]
 

 

 
-
+
 SimpleImputer(strategy='median')
 

 
 

-
num_pipeline.set_params(simpleimputer__strategy="median")
+
num_pipeline.set_params(simpleimputer__strategy="median")
 

 

 
-
+
 Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),
                 ('standardscaler', StandardScaler())])
 

@@ -1965,7 +1968,7 @@ 
Transformation Pipelines

 
 
 

-
housing_prepared_fr = pd.DataFrame(
+
housing_prepared_fr = pd.DataFrame(
     housing_prepared,
     columns=preprocessing.get_feature_names_out(),
     index=housing.index)
@@ -1973,7 +1976,7 @@ 
Transformation Pipelines

 

 

 
-
+
        pipeline-1__longitude  pipeline-1__latitude  pipeline-1__housing_median_age  ...  pipeline-2__ocean_proximity_ISLAND  pipeline-2__ocean_proximity_NEAR BAY  pipeline-2__ocean_proximity_NEAR OCEAN
 13096              -1.423037              1.013606                        1.861119  ...                                 0.0                                   1.0                                     0.0
 14973               0.596394             -0.702103                        0.907630  ...                                 0.0                                   0.0                                     0.0
@@ -2015,21 +2018,21 @@ 
Transformation Pipelines

 
 
 

-
housing_prepared = preprocessing.fit_transform(housing)
+
housing_prepared = preprocessing.fit_transform(housing)
 print(housing_prepared.shape)
 

 

 
-
+
 (16512, 24)
 

 
 

-
print(preprocessing.get_feature_names_out())
+
print(preprocessing.get_feature_names_out())
 

 

 
-
+
 ['bedrooms__ratio' 'rooms_per_house__ratio' 'people_per_house__ratio'
  'log__total_bedrooms' 'log__total_rooms' 'log__population'
  'log__households' 'log__median_income' 'geo__Cluster 0 similarity'
@@ -2054,14 +2057,14 @@ 
Select and Train a Model

 
Training and Evaluating on the Training Set

 

 

-
from sklearn.linear_model import LinearRegression
+
from sklearn.linear_model import LinearRegression
 
 lin_reg = make_pipeline(preprocessing, LinearRegression())
 print(lin_reg.fit(housing, housing_labels))
 

 

 
-
+
 Pipeline(steps=[('columntransformer',
                  ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',
                                                               SimpleImputer(strategy='median')),
@@ -2086,36 +2089,36 @@ 
Training and Evaluating on the Training Set

 

 
 

-
housing_predictions = lin_reg.predict(housing)
+
housing_predictions = lin_reg.predict(housing)
 print(housing_predictions[:5].round(-2))  # -2 = rounded to the nearest hundred
 

 

 
-
+
 [242800. 375900. 127500.  99400. 324600.]
 

 
 

-
print(housing_labels.iloc[:5].values)
+
print(housing_labels.iloc[:5].values)
 

 

 
-
+
 [458300. 483800. 101700.  96100. 361800.]
 

 
 

-
error_ratios = housing_predictions[:5].round(-2) / housing_labels.iloc[:5].values - 1
+
error_ratios = housing_predictions[:5].round(-2) / housing_labels.iloc[:5].values - 1
 print(", ".join([f"{100 * ratio:.1f}%" for ratio in error_ratios]))
 

 

 
-
+
 -47.0%, -22.3%, 25.4%, 3.4%, -10.3%
 

 
 

-
from sklearn.metrics import mean_squared_error
+
from sklearn.metrics import mean_squared_error
 
 lin_rmse = mean_squared_error(housing_labels, housing_predictions,
                               squared=False)
@@ -2123,19 +2126,19 @@ 
Training and Evaluating on the Training Set

 

 

 
-
+
 68647.95686706656
 

 
 

-
from sklearn.tree import DecisionTreeRegressor
+
from sklearn.tree import DecisionTreeRegressor
 
 tree_reg = make_pipeline(preprocessing, DecisionTreeRegressor(random_state=42))
 tree_reg.fit(housing, housing_labels)
 

 

 
-
+
 Pipeline(steps=[('columntransformer',
                  ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',
                                                               SimpleImputer(strategy='median')),
@@ -2160,14 +2163,14 @@ 
Training and Evaluating on the Training Set

 

 
 

-
housing_predictions = tree_reg.predict(housing)
+
housing_predictions = tree_reg.predict(housing)
 tree_rmse = mean_squared_error(housing_labels, housing_predictions,
                               squared=False)
 tree_rmse
 

 

 
-
+
 0.0
 

 

@@ -2185,11 +2188,11 @@ 
Better Evaluation using Cross-Validation

 
 
 

-
pd.Series(tree_rmses).describe()
+
pd.Series(tree_rmses).describe()
 

 

 
-
+
 count       10.000000
 mean     67153.318273
 std       1963.580924
@@ -2202,13 +2205,13 @@ 
Better Evaluation using Cross-Validation

 

 
 

-
lin_rmses = -cross_val_score(lin_reg, housing, housing_labels,
+
lin_rmses = -cross_val_score(lin_reg, housing, housing_labels,
                               scoring="neg_root_mean_squared_error", cv=10)
 pd.Series(lin_rmses).describe()
 

 

 
-
+
 count       10.000000
 mean     69847.923224
 std       4078.407329
@@ -2231,11 +2234,11 @@ 
Better Evaluation using Cross-Validation

 
 
 

-
pd.Series(forest_rmses).describe()
+
pd.Series(forest_rmses).describe()
 

 

 
-
+
 count       10.000000
 mean     47002.931706
 std       1048.451340
@@ -2248,7 +2251,7 @@ 
Better Evaluation using Cross-Validation

 

 
 

-
forest_reg.fit(housing, housing_labels)
+
forest_reg.fit(housing, housing_labels)
 housing_predictions = forest_reg.predict(housing)
 forest_rmse = mean_squared_error(housing_labels, housing_predictions,
                                  squared=False)
@@ -2256,7 +2259,7 @@ 
Better Evaluation using Cross-Validation

 

 

 
-
+
 17547.52124624957
 

 
@@ -2271,7 +2274,7 @@ 
Fine-Tuning the Model

 
Using Grid Search

 

 

-
from sklearn.model_selection import GridSearchCV
+
from sklearn.model_selection import GridSearchCV
 
 full_pipeline = Pipeline([
     ("preprocessing", preprocessing),
@@ -2289,7 +2292,7 @@ 
Using Grid Search

 

 

 
-
+
 GridSearchCV(cv=3,
              estimator=Pipeline(steps=[('preprocessing',
                                         ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',
@@ -2312,30 +2315,30 @@ 
Using Grid Search

 

 
 

-
print(str(full_pipeline.get_params().keys())[:1000] + "...")
+
print(str(full_pipeline.get_params().keys())[:1000] + "...")
 

 

 
-
+
 dict_keys(['memory', 'steps', 'verbose', 'preprocessing', 'random_forest', 'preprocessing__force_int_remainder_cols', 'preprocessing__n_jobs', 'preprocessing__remainder__memory', 'preprocessing__remainder__steps', 'preprocessing__remainder__verbose', 'preprocessing__remainder__simpleimputer', 'preprocessing__remainder__standardscaler', 'preprocessing__remainder__simpleimputer__add_indicator', 'preprocessing__remainder__simpleimputer__copy', 'preprocessing__remainder__simpleimputer__fill_value', 'preprocessing__remainder__simpleimputer__keep_empty_features', 'preprocessing__remainder__simpleimputer__missing_values', 'preprocessing__remainder__simpleimputer__strategy', 'preprocessing__remainder__standardscaler__copy', 'preprocessing__remainder__standardscaler__with_mean', 'preprocessing__remainder__standardscaler__with_std', 'preprocessing__remainder', 'preprocessing__sparse_threshold', 'preprocessing__transformer_weights', 'preprocessing__transformers', 'preprocessing__verbose', 'prepro...
 

 
 
 

-
print(grid_search.best_params_)
+
print(grid_search.best_params_)
 

 

 
-
+
 {'preprocessing__geo__n_clusters': 15, 'random_forest__max_features': 6}
 

 
 

-
grid_search.best_estimator_
+
grid_search.best_estimator_
 

 

 
-
+
 Pipeline(steps=[('preprocessing',
                  ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',
                                                               SimpleImputer(strategy='median')),
@@ -2360,7 +2363,7 @@ 
Using Grid Search

 

 
 

-
cv_res = pd.DataFrame(grid_search.cv_results_)
+
cv_res = pd.DataFrame(grid_search.cv_results_)
 cv_res.sort_values(by="mean_test_score", ascending=False, inplace=True)
 
 # extra code – these few lines of code just make the DataFrame look nicer
@@ -2375,7 +2378,7 @@ 
Using Grid Search

 

 

 
-
+
     n_clusters  max_features  split0  split1  split2  mean_test_rmse
 12          15             6   43536   43759   44568           43954
 13          15             8   44084   44205   44863           44384
@@ -2396,7 +2399,7 @@ 
Randomised Search

 

 
 

-
from sklearn.model_selection import RandomizedSearchCV
+
from sklearn.model_selection import RandomizedSearchCV
 from scipy.stats import randint
 
 param_distribs = {'preprocessing__geo__n_clusters': randint(low=3, high=50),
@@ -2410,7 +2413,7 @@ 
Randomised Search

 

 

 
-
+
 RandomizedSearchCV(cv=3,
                    estimator=Pipeline(steps=[('preprocessing',
                                               ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',
@@ -2431,7 +2434,7 @@ 
Randomised Search

 

 
 

-
cv_res = pd.DataFrame(rnd_search.cv_results_)
+
cv_res = pd.DataFrame(rnd_search.cv_results_)
 cv_res.sort_values(by="mean_test_score", ascending=False, inplace=True)
 cv_res = cv_res[["param_preprocessing__geo__n_clusters",
                  "param_random_forest__max_features", "split0_test_score",
@@ -2442,7 +2445,7 @@ 
Randomised Search

 

 

 
-
+
    n_clusters  max_features  split0  split1  split2  mean_test_rmse
 1          45             9   41099   42150   42701           41983
 8          32             7   41604   42200   43219           42341
@@ -2452,7 +2455,7 @@ 
Randomised Search

 

 
 

-
from scipy.stats import randint, uniform, geom, expon
+
from scipy.stats import randint, uniform, geom, expon
 
 xs1 = np.arange(0, 7 + 1)
 randint_distrib = randint(0, 7 + 1).pmf(xs1)
@@ -2500,13 +2503,13 @@ 
Randomised Search

 

 
 
-

+

 
choosing-sampling.png
 

 

 
 

-
from scipy.stats import loguniform
+
from scipy.stats import loguniform
 
 xs1 = np.linspace(0, 7, 500)
 expon_distrib = expon(scale=1).pdf(xs1)
@@ -2555,7 +2558,7 @@ 
Randomised Search

 

 
 
-

+

 
choosing-sampling-2.png
 

 

@@ -2566,13 +2569,13 @@ 
Randomised Search

 
Analyse the Best Models and Errors

 

 

-
final_model = rnd_search.best_estimator_  # includes preprocessing
+
final_model = rnd_search.best_estimator_  # includes preprocessing
 feature_importances = final_model["random_forest"].feature_importances_
 print(feature_importances.round(2))
 

 

 
-
+
 [0.06 0.06 0.05 0.01 0.01 0.01 0.01 0.19 0.01 0.01 0.02 0.04 0.01 0.
  0.02 0.01 0.01 0.01 0.01 0.01 0.   0.   0.01 0.   0.01 0.02 0.02 0.01
  0.01 0.01 0.03 0.01 0.01 0.01 0.01 0.01 0.01 0.   0.01 0.01 0.02 0.01
@@ -2910,7 +2913,7 @@ 
Evaluate using the Test Set

 
 
 

-
X_test = strat_test_set.drop("median_house_value", axis=1)
+
X_test = strat_test_set.drop("median_house_value", axis=1)
 y_test = strat_test_set["median_house_value"].copy()
 
 final_predictions = final_model.predict(X_test)
@@ -2920,7 +2923,7 @@ 
Evaluate using the Test Set

 

 

 
-
+
 41536.20954091729
 

 
@@ -2931,7 +2934,7 @@ 
Evaluate using the Test Set

 

 
 

-
from scipy import stats
+
from scipy import stats
 
 confidence = 0.95
 squared_errors = (final_predictions - y_test) ** 2
@@ -2942,7 +2945,7 @@ 
Evaluate using the Test Set

 

 

 
-
+
 [39391.51194455 43575.47696556]
 

 
@@ -2959,7 +2962,7 @@ 
Evaluate using the Test Set

 

 
 

-
m = len(squared_errors)
+
m = len(squared_errors)
 mean = squared_errors.mean()
 tscore = stats.t.ppf((1 + confidence) / 2, df=m - 1)
 tmargin = tscore * squared_errors.std(ddof=1) / np.sqrt(m)
@@ -2967,7 +2970,7 @@ 
Evaluate using the Test Set

 

 

 
-
+
 39391.51194455236 43575.476965561335
 

 
@@ -2977,13 +2980,13 @@ 
Evaluate using the Test Set

 

 
 

-
zscore = stats.norm.ppf((1 + confidence) / 2)
+
zscore = stats.norm.ppf((1 + confidence) / 2)
 zmargin = zscore * squared_errors.std(ddof=1) / np.sqrt(m)
 print(np.sqrt(mean - zmargin), np.sqrt(mean + zmargin))
 

 

 
-
+
 39392.15805502188 43574.89288365666
 

 
@@ -3017,7 +3020,7 @@ 
Saving the Model

 

 
 

-
import joblib
+
import joblib
 
 # extra code – excluded for conciseness
 from sklearn.cluster import KMeans
@@ -3038,7 +3041,7 @@ 
Saving the Model

 

 

 
-
+
 [441558.14 455211.06 109319.    98208.   338068.04]
 

 

diff --git a/docs/Ensemble-Learning-and-Random-Forests.html b/docs/Ensemble-Learning-and-Random-Forests.html
new file mode 100644
index 0000000..d97067f
--- /dev/null
+++ b/docs/Ensemble-Learning-and-Random-Forests.html
@@ -0,0 +1,986 @@
+
+
+
+
+
+
+Code for Machine Learning and Data Science II Ensemble Learning and Random Forests
+
+
+
+
+
+
+
+
+
+
+

+  UP 
+ |
+  HOME 
+

+
Code for Machine Learning and Data Science II Ensemble Learning and Random Forests

+
+

+These are the code snippets used in Ensemble Learning and Random Forests
+part of Machine Learning and Data Science II.
+

+
+

+
Introduction

+

+

+The following is a custom package written to handle plotting
+and other functions required by the lecture.
+

+
+

+
import ChalcedonPy as cp # custom-pakcage for lecture materials and publications
+SAVE_PATH = "Ensemble-Learning-and-Random-Forests" # sets the default save path
+style="web" # sets the default rcParams stylce sheet
+

+

+

+

+
+

+
Voting Classifiers

+

+

+
import matplotlib.pyplot as plt
+import numpy as np
+
+heads_proba = 0.51
+np.random.seed(42)
+coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)
+cumulative_heads = coin_tosses.cumsum(axis=0)
+cumulative_heads_ratio = cumulative_heads / np.arange(1, 10001).reshape(-1, 1)
+

+

+
+

+
plt.figure(figsize=(8, 4))
+
+plt.plot(cumulative_heads_ratio)
+plt.plot([0, 10000], [0.51, 0.51], "k--", linewidth=2, label="51%")
+plt.plot([0, 10000], [0.5, 0.5], "k-", label="50%")
+
+plt.xlabel("Number of coin tosses")
+plt.ylabel("Heads ratio")
+plt.legend(loc="lower right")
+plt.axis([0, 10000, 0.42, 0.58])
+
+cp.store_fig("law-of-large-numbers-plot",
+             filepath = SAVE_PATH,
+             style = style,
+             close = True)
+

+

+
+
+

+
law-of-large-numbers-plot.png
+

+

+
+

+Time to build a voting classifier
+

+
+

+
from sklearn.datasets import make_moons
+from sklearn.ensemble import RandomForestClassifier, VotingClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+from sklearn.svm import SVC
+
+X, y = make_moons(n_samples=500, noise=0.30, random_state=42)
+X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
+
+voting_clf = VotingClassifier(
+    estimators=[
+        ('lr', LogisticRegression(random_state=42)),
+        ('rf', RandomForestClassifier(random_state=42)),
+        ('svc', SVC(random_state=42))
+    ]
+)
+print(voting_clf.fit(X_train, y_train))
+

+

+
+
+VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),
+                             ('rf', RandomForestClassifier(random_state=42)),
+                             ('svc', SVC(random_state=42))])
+

+
+

+
for name, clf in voting_clf.named_estimators_.items():
+    print(name, "=", clf.score(X_test, y_test))
+

+

+
+
+lr = 0.864
rf = 0.896
svc = 0.896
+

+
+

+
voting_clf.predict(X_test[:1])
+

+

+
+

+
[clf.predict(X_test[:1]) for clf in voting_clf.estimators_]
+

+

+
+

+
voting_clf.score(X_test, y_test)
+

+

+
+
+0.912
+

+
+

+Time to use soft voting:
+

+
+

+
voting_clf.voting = "soft"
+voting_clf.named_estimators["svc"].probability = True
+voting_clf.fit(X_train, y_train)
+voting_clf.score(X_test, y_test)
+

+

+
+
+0.92
+

+

+
+

+
Bagging and Pasting

+

+

+
from sklearn.ensemble import BaggingClassifier
+from sklearn.tree import DecisionTreeClassifier
+
+bag_clf = BaggingClassifier(DecisionTreeClassifier(), n_estimators=500,
+                            max_samples=100, n_jobs=-1, random_state=42)
+bag_clf.fit(X_train, y_train)
+

+

+
+
+BaggingClassifier(estimator=DecisionTreeClassifier(), max_samples=100,
+                  n_estimators=500, n_jobs=-1, random_state=42)
+

+
+

+
def plot_decision_boundary(clf, X, y, alpha=1.0):
+    axes=[-1.5, 2.4, -1, 1.5]
+    x1, x2 = np.meshgrid(np.linspace(axes[0], axes[1], 100),
+                         np.linspace(axes[2], axes[3], 100))
+    X_new = np.c_[x1.ravel(), x2.ravel()]
+    y_pred = clf.predict(X_new).reshape(x1.shape)
+
+    plt.contourf(x1, x2, y_pred, alpha=0.3 * alpha, cmap='Wistia')
+    plt.contour(x1, x2, y_pred, cmap="Greys", alpha=0.8 * alpha)
+    colors = ["#78785c", "#c47b27"]
+    markers = ("o", "^")
+    for idx in (0, 1):
+        plt.plot(X[:, 0][y == idx], X[:, 1][y == idx],
+                 color=colors[idx], marker=markers[idx], linestyle="none")
+    plt.axis(axes)
+    plt.xlabel(r"$x_1$")
+    plt.ylabel(r"$x_2$", rotation=0)
+
+tree_clf = DecisionTreeClassifier(random_state=42)
+tree_clf.fit(X_train, y_train)
+

+

+
+

+
fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)
+plt.sca(axes[0])
+plot_decision_boundary(tree_clf, X_train, y_train)
+plt.title("Decision Tree")
+plt.sca(axes[1])
+plot_decision_boundary(bag_clf, X_train, y_train)
+plt.title("Decision Trees with Bagging")
+plt.ylabel("")
+
+cp.store_fig("decision-tree-without-and-with-bagging-plot",
+             filepath = SAVE_PATH,
+             style = style,
+             close = True)
+

+

+
+
+

+
decision-tree-without-and-with-bagging-plot.png
+

+

+

+

+
+

+
Out-of-Bag Evaluation

+

+

+
bag_clf = BaggingClassifier(DecisionTreeClassifier(), n_estimators=500,
+                            oob_score=True, n_jobs=-1, random_state=42)
+bag_clf.fit(X_train, y_train)
+bag_clf.oob_score_
+

+

+
+
+0.896
+

+
+

+
bag_clf.oob_decision_function_[:3]  # probas for the first 3 instances
+

+

+
+
+
+
+++
++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+




0.323529410.67647059
0.33750.6625
10

+
+

+
from sklearn.metrics import accuracy_score
+
+y_pred = bag_clf.predict(X_test)
+accuracy_score(y_test, y_pred)
+

+

+
+
+0.912
+

+
+

+
# extra code – shows how to compute the 63% proba
+print(1 - (1 - 1 / 1000) ** 1000)
+print(1 - np.exp(-1))
+

+

+
+
+0.6323045752290363
+0.6321205588285577
+

+

+

+

+
+

+
Random Forests

+

+

+
from sklearn.ensemble import RandomForestClassifier
+
+rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16,
+                                 n_jobs=-1, random_state=42)
+rnd_clf.fit(X_train, y_train)
+y_pred_rf = rnd_clf.predict(X_test)
+

+

+
+

+A Random Forest is equivalent to a bag of decision trees:
+

+
+

+
bag_clf = BaggingClassifier(
+    DecisionTreeClassifier(max_features="sqrt", max_leaf_nodes=16),
+    n_estimators=500, n_jobs=-1, random_state=42)
+

+

+
+

+
bag_clf.fit(X_train, y_train)
+y_pred_bag = bag_clf.predict(X_test)
+np.all(y_pred_bag == y_pred_rf)  # same predictions
+

+

+
+
+True
+

+

+
+

+
Feature Importance

+

+

+
from sklearn.datasets import load_iris
+
+iris = load_iris(as_frame=True)
+rnd_clf = RandomForestClassifier(n_estimators=500, random_state=42)
+rnd_clf.fit(iris.data, iris.target)
+for score, name in zip(rnd_clf.feature_importances_, iris.data.columns):
+    print(round(score, 2), name)
+

+

+
+
+0.11 sepal length (cm)
+0.02 sepal width (cm)
+0.44 petal length (cm)
+0.42 petal width (cm)
+

+
+

+
from sklearn.datasets import fetch_openml
+
+X_mnist, y_mnist = fetch_openml('mnist_784', return_X_y=True, as_frame=False,
+                                parser='auto')
+
+rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)
+rnd_clf.fit(X_mnist, y_mnist)
+
+heatmap_image = rnd_clf.feature_importances_.reshape(28, 28)
+plt.imshow(heatmap_image, cmap="hot")
+cbar = plt.colorbar(ticks=[rnd_clf.feature_importances_.min(),
+                           rnd_clf.feature_importances_.max()])
+cbar.ax.set_yticklabels(['Not important', 'Very important'], fontsize=14)
+
+cp.store_fig("mnist-feature-importance-plot",
+             filepath = SAVE_PATH,
+             style = style,
+             close = True)
+

+

+
+
+

+
mnist-feature-importance-plot.png
+

+

+

+

+

+
+

+
Boosting

+

+

+

+
AdaBoost

+

+

+
m = len(X_train)
+
+fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)
+for subplot, learning_rate in ((0, 1), (1, 0.5)):
+    sample_weights = np.ones(m) / m
+    plt.sca(axes[subplot])
+    for i in range(5):
+        svm_clf = SVC(C=0.2, gamma=0.6, random_state=42)
+        svm_clf.fit(X_train, y_train, sample_weight=sample_weights * m)
+        y_pred = svm_clf.predict(X_train)
+
+        error_weights = sample_weights[y_pred != y_train].sum()
+        r = error_weights / sample_weights.sum()  # equation 7-1
+        alpha = learning_rate * np.log((1 - r) / r)  # equation 7-2
+        sample_weights[y_pred != y_train] *= np.exp(alpha)  # equation 7-3
+        sample_weights /= sample_weights.sum()  # normalization step
+
+        plot_decision_boundary(svm_clf, X_train, y_train, alpha=0.4)
+        plt.title(f"learning_rate = {learning_rate}")
+    if subplot == 0:
+        plt.text(-0.75, -0.95, "1", fontsize=16)
+        plt.text(-1.05, -0.95, "2", fontsize=16)
+        plt.text(1.0, -0.95, "3", fontsize=16)
+        plt.text(-1.45, -0.5, "4", fontsize=16)
+        plt.text(1.36,  -0.95, "5", fontsize=16)
+    else:
+        plt.ylabel("")
+
+cp.store_fig("boosting-plot",
+             filepath = SAVE_PATH,
+             style = style,
+             close = True)
+

+

+
+
+

+
boosting-plot.png
+

+

+
+

+
from sklearn.ensemble import AdaBoostClassifier
+
+ada_clf = AdaBoostClassifier(
+    DecisionTreeClassifier(max_depth=1), n_estimators=30,
+    learning_rate=0.5, random_state=42, algorithm = 'SAMME')
+ada_clf.fit(X_train, y_train)
+

+

+
+
+AdaBoostClassifier(algorithm='SAMME',
+                   estimator=DecisionTreeClassifier(max_depth=1),
+                   learning_rate=0.5, n_estimators=30, random_state=42)
+

+
+

+
plot_decision_boundary(ada_clf, X_train, y_train)
+
+cp.store_fig("boosting-with-desicion-plot",
+             filepath = SAVE_PATH,
+             style = style,
+             close = True)
+

+

+
+
+

+
boosting-with-desicion-plot.png
+

+

+

+

+
+

+
Gradient Boosting

+

+

+
import numpy as np
+from sklearn.tree import DecisionTreeRegressor
+
+np.random.seed(42)
+X = np.random.rand(100, 1) - 0.5
+y = 3 * X[:, 0] ** 2 + 0.05 * np.random.randn(100)  # y = 3x² + Gaussian noise
+
+tree_reg1 = DecisionTreeRegressor(max_depth=2, random_state=42)
+tree_reg1.fit(X, y)
+

+

+
+
+DecisionTreeRegressor(max_depth=2, random_state=42)
+

+
+
+

+
y2 = y - tree_reg1.predict(X)
+tree_reg2 = DecisionTreeRegressor(max_depth=2, random_state=43)
+tree_reg2.fit(X, y2)
+

+

+
+
+DecisionTreeRegressor(max_depth=2, random_state=43)
+

+
+

+
y3 = y2 - tree_reg2.predict(X)
+tree_reg3 = DecisionTreeRegressor(max_depth=2, random_state=44)
+tree_reg3.fit(X, y3)
+

+

+
+
+DecisionTreeRegressor(max_depth=2, random_state=44)
+

+
+

+
X_new = np.array([[-0.4], [0.], [0.5]])
+sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))
+

+

+
+
+
+
+++
++
++
+
+
+
+
+
+
+
+





0.494840290.040211660.75026781

+
+

+
def plot_predictions(regressors, X, y, axes, style,
+                     label=None, data_style="b.", data_label=None):
+    x1 = np.linspace(axes[0], axes[1], 500)
+    y_pred = sum(regressor.predict(x1.reshape(-1, 1))
+                 for regressor in regressors)
+    plt.plot(X[:, 0], y, data_style, label=data_label)
+    plt.plot(x1, y_pred, style, linewidth=2, label=label)
+    if label or data_label:
+        plt.legend(loc="upper center")
+    plt.axis(axes)
+
+plt.figure(figsize=(11, 11))
+
+plt.subplot(3, 2, 1)
+plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.2, 0.8], style="g-",
+                 label="$h_1(x_1)$", data_label="Training set")
+plt.ylabel("$y$  ", rotation=0)
+plt.title("Residuals and tree predictions")
+
+plt.subplot(3, 2, 2)
+plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.2, 0.8], style="r-",
+                 label="$h(x_1) = h_1(x_1)$", data_label="Training set")
+plt.title("Ensemble predictions")
+
+plt.subplot(3, 2, 3)
+plot_predictions([tree_reg2], X, y2, axes=[-0.5, 0.5, -0.4, 0.6], style="g-",
+                 label="$h_2(x_1)$", data_style="k+",
+                 data_label="Residuals: $y - h_1(x_1)$")
+plt.ylabel("$y$  ", rotation=0)
+
+plt.subplot(3, 2, 4)
+plot_predictions([tree_reg1, tree_reg2], X, y, axes=[-0.5, 0.5, -0.2, 0.8],
+                  style="r-", label="$h(x_1) = h_1(x_1) + h_2(x_1)$")
+
+plt.subplot(3, 2, 5)
+plot_predictions([tree_reg3], X, y3, axes=[-0.5, 0.5, -0.4, 0.6], style="g-",
+                 label="$h_3(x_1)$", data_style="k+",
+                 data_label="Residuals: $y - h_1(x_1) - h_2(x_1)$")
+plt.xlabel("$x_1$")
+plt.ylabel("$y$  ", rotation=0)
+
+plt.subplot(3, 2, 6)
+plot_predictions([tree_reg1, tree_reg2, tree_reg3], X, y,
+                 axes=[-0.5, 0.5, -0.2, 0.8], style="r-",
+                 label="$h(x_1) = h_1(x_1) + h_2(x_1) + h_3(x_1)$")
+plt.xlabel("$x_1$")
+
+cp.store_fig("gradient-boosting-plot",
+             filepath = SAVE_PATH,
+             style = style,
+             close = True)
+

+

+
+
+

+
gradient-boosting-plot.png
+

+

+
+

+Let's try a gradient boosting regressor:
+

+
+

+
from sklearn.ensemble import GradientBoostingRegressor
+
+gbrt = GradientBoostingRegressor(max_depth=2, n_estimators=3,
+                                 learning_rate=1.0, random_state=42)
+gbrt.fit(X, y)
+

+

+
+
+GradientBoostingRegressor(learning_rate=1.0, max_depth=2, n_estimators=3,
+                          random_state=42)
+

+
+

+
gbrt_best = GradientBoostingRegressor(
+    max_depth=2, learning_rate=0.05, n_estimators=500,
+    n_iter_no_change=10, random_state=42)
+gbrt_best.fit(X, y)
+

+

+
+
+GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=500,
+                          n_iter_no_change=10, random_state=42)
+

+
+

+
gbrt_best.n_estimators_
+

+

+
+
+92
+

+
+

+
fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)
+
+plt.sca(axes[0])
+plot_predictions([gbrt], X, y, axes=[-0.5, 0.5, -0.1, 0.8], style="r-",
+                 label="Ensemble predictions")
+plt.title(f"learning_rate={gbrt.learning_rate}, "
+          f"n_estimators={gbrt.n_estimators_}")
+plt.xlabel("$x_1$")
+plt.ylabel("$y$", rotation=0)
+
+plt.sca(axes[1])
+plot_predictions([gbrt_best], X, y, axes=[-0.5, 0.5, -0.1, 0.8], style="r-")
+plt.title(f"learning_rate={gbrt_best.learning_rate}, "
+          f"n_estimators={gbrt_best.n_estimators_}")
+plt.xlabel("$x_1$")
+
+cp.store_fig("gbrt-learning-rate-plot",
+             filepath = SAVE_PATH,
+             style = style,
+             close = True)
+

+

+
+
+

+
gbrt-learning-rate-plot.png
+

+

+
+
+

+
import pandas as pd
+from sklearn.model_selection import train_test_split
+import tarfile
+import urllib.request
+
+def load_housing_data():
+    tarball_path = Path("datasets/housing.tgz")
+    if not tarball_path.is_file():
+        Path("datasets").mkdir(parents=True, exist_ok=True)
+        url = "https://github.com/ageron/data/raw/main/housing.tgz"
+        urllib.request.urlretrieve(url, tarball_path)
+        with tarfile.open(tarball_path) as housing_tarball:
+            housing_tarball.extractall(path="datasets")
+    return pd.read_csv(Path("datasets/housing/housing.csv"))
+
+housing = load_housing_data()
+
+train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)
+housing_labels = train_set["median_house_value"]
+housing = train_set.drop("median_house_value", axis=1)
+
+

+

+
+

+
from sklearn.pipeline import make_pipeline
+from sklearn.compose import make_column_transformer
+from sklearn.ensemble import HistGradientBoostingRegressor
+from sklearn.preprocessing import OrdinalEncoder 
+
+hgb_reg = make_pipeline(
+    make_column_transformer((OrdinalEncoder(), ["ocean_proximity"]),
+                            remainder="passthrough"),
+    HistGradientBoostingRegressor(categorical_features=[0], random_state=42)
+)
+hgb_reg.fit(housing, housing_labels)
+
+

+

+
+
+

+
from sklearn.model_selection import cross_val_score
+
+hgb_rmses = -cross_val_score(hgb_reg, housing, housing_labels,
+                             scoring="neg_root_mean_squared_error", cv=10)
+pd.Series(hgb_rmses).describe()
+

+

+

+

+
+

+
Stacking

+

+

+
from sklearn.ensemble import StackingClassifier
+
+stacking_clf = StackingClassifier(
+    estimators=[
+        ('lr', LogisticRegression(random_state=42)),
+        ('rf', RandomForestClassifier(random_state=42)),
+        ('svc', SVC(probability=True, random_state=42))
+    ],
+    final_estimator=RandomForestClassifier(random_state=43),
+    cv=5  # number of cross-validation folds
+)
+stacking_clf.fit(X_train, y_train)
+
+

+

+
+
+

+
stacking_clf.score(X_test, y_test)
+

+

+

+

+

+

+

+
Author: Daniel McGuiness

+

+
+
diff --git a/docs/Machine-Learning-Landscape.html b/docs/Machine-Learning-Landscape.html
index f684cf3..5f3cbf5 100644
--- a/docs/Machine-Learning-Landscape.html
+++ b/docs/Machine-Learning-Landscape.html
@@ -213,6 +213,38 @@
 org_html_manager.setup();  // activate after the parameters are set
 // @license-end
 
+
+
+
 
 
 

@@ -382,7 +414,7 @@ 
Work on the Data

 

 
 

-
country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,
+
country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,
                    x=gdppc_col, y=lifesat_col)
 
 min_life_sat = 4
@@ -415,7 +447,7 @@ 
Work on the Data

 

 
 
-

+

 
happy-money.png
 

 

@@ -426,12 +458,12 @@ 
Work on the Data

 

 
 

-
highlighted_countries = country_stats.loc[list(position_text.keys())]
+
highlighted_countries = country_stats.loc[list(position_text.keys())]
 highlighted_countries[[gdppc_col, lifesat_col]].sort_values(by=gdppc_col)
 

 

 
-
+
                GDP per capita (USD)  Life satisfaction
 Country                                               
 Turkey                 28384.987785                5.5
@@ -453,7 +485,7 @@ 
Fit the Model

 

 
 

-
country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,
+
country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,
                    x=gdppc_col, y=lifesat_col)
 
 X = np.linspace(min_gdp, max_gdp, 1000)
@@ -480,18 +512,18 @@ 
Fit the Model

 

 
 
-

+

 
model-tweak.png
 

 

 
 

-Now, generate our model, get the best $\theta_0$ and $\theta_0$ and
+Now, generate our model, get the best \(\theta_0\) and \(\theta_0\) and
 plot the best fit.
 

 
 

-
from sklearn import linear_model
+
from sklearn import linear_model
 
 X_sample = country_stats[[gdppc_col]].values
 y_sample = country_stats[[lifesat_col]].values
@@ -520,7 +552,7 @@ 
Fit the Model

 

 
 
-

+

 
best-plot.png
 

 

@@ -530,7 +562,7 @@ 
Fit the Model

 

 
 

-
cyprus_gdp_per_capita = gdp_per_capita[gdppc_col].loc["Cyprus"]
+
cyprus_gdp_per_capita = gdp_per_capita[gdppc_col].loc["Cyprus"]
 cyprus_predicted_life_satisfaction = lin1.predict([[cyprus_gdp_per_capita]])[0, 0]
 
 country_stats.plot(kind='scatter', figsize=(5, 3), grid=True,
@@ -557,7 +589,7 @@ 
Fit the Model

 

 
 
-

+

 
prediction.png
 

 

@@ -572,7 +604,7 @@ 
Testing the Model

 

 
 

-
missing_data = full_country_stats[(full_country_stats[gdppc_col] < min_gdp) |
+
missing_data = full_country_stats[(full_country_stats[gdppc_col] < min_gdp) |
                                   (full_country_stats[gdppc_col] > max_gdp)]
 
 position_text_missing_countries = {
@@ -616,7 +648,7 @@ 
Testing the Model

 

 
 
-

+

 
missing-data.png
 

 

@@ -636,7 +668,7 @@ 
Over-Fitting Data

 

 
 

-
from sklearn import preprocessing
+
from sklearn import preprocessing
 from sklearn import pipeline
 
 full_country_stats.plot(kind='scatter',
@@ -664,7 +696,7 @@ 
Over-Fitting Data

 

 
 
-

+

 
overfitting-data.png
 

 

@@ -679,7 +711,7 @@ 
Controlling the Parameters

 

 
 

-
w_countries = [c for c in full_country_stats.index if "W" in c.upper()]
+
w_countries = [c for c in full_country_stats.index if "W" in c.upper()]
 all_w_countries = [c for c in gdp_per_capita.index if "W" in c.upper()]
 
 country_stats.plot(kind='scatter', x=gdppc_col, y=lifesat_col, figsize=(8, 3))
@@ -705,7 +737,7 @@ 
Controlling the Parameters

 

 
 
-

+

 
ridge-data.png
 

 

diff --git a/docs/Training-Models.html b/docs/Training-Models.html
new file mode 100644
index 0000000..9f0f163
--- /dev/null
+++ b/docs/Training-Models.html
@@ -0,0 +1,1810 @@
+
+
+
+
+
+
+Code for Machine Learning and Data Science II Training Models
+
+
+
+
+
+
+
+
+
+
+
+
+
+

+  UP 
+ |
+  HOME 
+

+
Code for Machine Learning and Data Science II Training Models

+
+

+These are the code snippets used in Training Models
+part of Machine Learning and Data Science II.
+

+
+

+
Introduction

+

+

+The following is a custom package written to handle plotting
+and other functions required by the lecture. it is by no means
+necessary as cp.store_fig can easily be replaced by
+using plt.show().
+

+
+

+
import matplotlib.pyplot as plt
+import ChalcedonPy as cp
+
+# Initialise ChalcedonPy
+cp.init(save_path="Training-Models",
+        display_mode="slide") 
+

+

+

+

+
+

+
Linear Regression

+

+

+A linear model makes a prediction by simply computing a
+weighted sum of the input features, plus a constant called
+the bias term (also called the intercept term),
+

+

+
+

+
The Normal Equation

+

+

+To find the value of \(\theta\) that minimizes the MSE, there
+exists a closed-form solution—in other words, a
+mathematical equation that gives the result directly.
+

+
+

+This is called the Normal equation.
+

+
+

+First generate some data.
+

+
+

+
import numpy as np
+
+np.random.seed(42)  # to make this code example reproducible
+m = 100  # number of instances
+X = 2 * np.random.rand(m, 1)  # column vector
+y = 4 + 3 * X + np.random.randn(m, 1)  # column vector
+

+

+
+

+The following code plots the data
+

+
+

+
import matplotlib.pyplot as plt
+
+plt.figure(figsize=(11, 6))
+plt.plot(X, y, ".", label="Data Points")
+plt.xlabel("$x_1$")
+plt.ylabel("$y$", rotation=0)
+plt.axis([0, 2, 0, 15])
+
+plt.legend(loc="upper left")
+
+cp.store_fig("generated-data-plot", close = True)
+

+

+
+
+

+
generated-data-plot.png
+

+

+
+
+
+The @ operator performs matrix multiplication. If A and B are NumPy arrays,
+then A @ B is equivalent to np.matmul(A, B). Many other libraries, like
+TensorFlow, PyTorch, and JAX, support the @ operator as well. However,
+you cannot use @ on pure Python arrays (i.e., lists of lists).
+

+
+

+Now time to calculate \(\hat{\theta}\) using the normal equation:
+

+
+

+
from sklearn.preprocessing import add_dummy_feature
+
+X_b = add_dummy_feature(X)  # add x0 = 1 to each instance
+theta_best = np.linalg.inv(X_b.T @ X_b) @ X_b.T @ y
+

+

+
+
+
+
+++
++
++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+





FUNCTIONDEFINITIONMORE INFO
linalg.inv(...)Given a square matrix a, return the matrix ainv satisfying a @ ainv = ainv @ a = eye(a.shape[0])Link
sklearn.preprocessing.add_dummy_feature(...)Augment dataset with an additional dummy feature.Link

+
+

+Let’s see what the theta values are:
+

+
+

+
print(theta_best)
+

+

+
+
+[[4.21509616]
+ [2.77011339]]
+

+
+

+Not the best but not the worst either. Now we can make predictions using \(\hat{\theta}\):
+

+
+

+
X_new = np.array([[0], [2]])
+X_new_b = add_dummy_feature(X_new)  # add x0 = 1 to each instance
+y_predict = X_new_b @ theta_best
+print(y_predict)
+

+

+
+
+[[4.21509616]
+ [9.75532293]]
+

+
+

+Let’s plot this model’s predictions:
+

+
+

+
plt.figure(figsize=(11, 6))
+
+plt.plot(X, y, ".", label="Data Points")
+plt.plot(X_new, y_predict, "-", label="Predictions")
+
+plt.xlabel("$x_1$")
+plt.ylabel("$y$", rotation=0)
+
+plt.axis([0, 2, 0, 15])
+
+plt.legend(loc="upper left")
+
+cp.store_fig("linear-model-predictions-plot", close = None)
+

+

+
+
+

+
linear-model-predictions-plot.png
+

+

+
+

+Performing linear regression using Scikit-Learn is relatively straightforward:
+

+
+

+
from sklearn.linear_model import LinearRegression
+
+lin_reg = LinearRegression()
+lin_reg.fit(X, y)
+
+print("Estimated coefficients: ", lin_reg.coef_)
+print("The b0 value is: ", lin_reg.intercept_)
+

+

+
+
+Estimated coefficients:  [[2.77011339]]
+The b0 value is:  [4.21509616]
+

+
+
+
+
+++
++
++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+





FUNCTIONDEFINITIONMORE INFO
sklearn.linear_model.LinearRegression(...)Ordinary least squares Linear Regression. LinearRegression fits a linear model with coefficients w = (w1, …, wp) to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation.Link
.fit(...)Fit linear model.Link

+
+

+Same information can be retrieved from np.linalg.lstsq():
+

+
+

+
theta_best_svd, residuals, rank, s = np.linalg.lstsq(X_b, y,
+                                                     rcond=1e-6)
+

+

+
+
+
+
+++
++
++
+
+
+
+
+
+
+
+
+
+
+
+
+
+





FUNCTIONDEFINITIONMORE INFO
np.linalg.lstsq(...)Return the least-squares solution to a linear matrix equation.Link

+
+

+You can use np.linalg.pinv() to compute the pseudoinverse directly:
+

+
+

+
np.linalg.pinv(X_b) @ y
+

+

+
+
+
+
+++
++
++
+
+
+
+
+
+
+
+
+
+
+
+
+
+





FUNCTIONDEFINITIONMORE INFO
np.linalg.pinv(...)Compute the (Moore-Penrose) pseudo-inverse of a matrix.Link

+

+

+

+
+

+
Gradient Descent

+

+

+Gradient descent is a generic optimization algorithm
+capable of finding optimal solutions to a wide range of problems.
+

+
+

+The general idea of gradient descent is to tweak parameters
+iteratively in order to minimize a cost function.
+

+
+

+Let’s look at a quick implementation of Batch Gradient Descent algorithm:
+

+
+

+
eta = 0.1  # learning rate
+n_epochs = 1000
+m = len(X_b)  # number of instances
+
+np.random.seed(42)
+theta = np.random.randn(2, 1)  # randomly initialized model parameters
+# Each iteration over the training set is called an epoch.
+for epoch in range(n_epochs):
+    gradients = 2 / m * X_b.T @ (X_b @ theta - y)
+    theta = theta - eta * gradients
+
+print(theta)
+

+

+
+
+[[4.21509616]
+ [2.77011339]]
+

+
+

+What about with different learning rate ? The code below
+does just that.
+

+
+

+
import matplotlib as mpl
+def plot_gradient_descent(theta, eta):
+    m = len(X_b)
+    plt.plot(X, y, ".")
+    n_epochs, n_shown = 1000, 20
+    theta_path = []
+    for epoch in range(n_epochs):
+        if epoch < n_shown:
+            y_predict = X_new_b @ theta
+            color = mpl.colors.rgb2hex(plt.cm.OrRd(epoch / n_shown + 0.15))
+            plt.plot(X_new, y_predict, linestyle="solid", color=color)
+        gradients = 2 / m * X_b.T @ (X_b @ theta - y)
+        theta = theta - eta * gradients
+        theta_path.append(theta)
+    plt.xlabel("$x_1$")
+    plt.axis([0, 2, 0, 15])
+    plt.title(fr"$\eta = {eta}$")
+    return theta_path
+

+

+
+

+And the plotting function for the comparison for
+three different learning rates.
+

+
+

+
np.random.seed(42)
+theta = np.random.randn(2, 1)  # random initialization
+plt.figure(figsize=(11, 6))
+plt.subplot(131)
+plot_gradient_descent(theta, eta=0.02)
+plt.ylabel("$y$", rotation=0)
+plt.subplot(132)
+theta_path_bgd = plot_gradient_descent(theta, eta=0.1)
+plt.gca().axes.yaxis.set_ticklabels([])
+plt.subplot(133)
+plt.gca().axes.yaxis.set_ticklabels([])
+plot_gradient_descent(theta, eta=0.5)
+cp.store_fig("gradient-descent-plot", close = True)
+

+

+
+
+

+
gradient-descent-plot.png
+

+

+

+
+

+
Stochastic Gradient Descent

+

+

+stochastic gradient descent picks a random instance in the training
+set at every step and computes the gradients based
+only on that single instance. 
+

+
+

+
theta_path_sgd = []
+

+

+
+

+This code implements stochastic gradient descent using a simple learning schedule:
+

+
+

+
n_epochs = 50
+t0, t1 = 5, 50  # learning schedule hyperparameters
+
+def learning_schedule(t):
+    return t0 / (t + t1)
+
+np.random.seed(42)
+theta = np.random.randn(2, 1)  # random initialization
+n_shown = 20
+
+plt.figure(figsize=(11, 6))  
+
+for epoch in range(n_epochs):
+    for iteration in range(m):
+
+        if epoch == 0 and iteration < n_shown:
+            y_predict = X_new_b @ theta
+            color = mpl.colors.rgb2hex(plt.cm.OrRd(iteration / n_shown + 0.15))
+            plt.plot(X_new, y_predict, color=color)
+
+        random_index = np.random.randint(m)
+        xi = X_b[random_index : random_index + 1]
+        yi = y[random_index : random_index + 1]
+        gradients = 2 * xi.T @ (xi @ theta - yi)  # for SGD, do not divide by m
+        eta = learning_schedule(epoch * m + iteration)
+        theta = theta - eta * gradients
+        theta_path_sgd.append(theta) 
+
+plt.plot(X, y, ".")
+plt.xlabel("$x_1$")
+plt.ylabel("$y$", rotation=0)
+plt.axis([0, 2, 0, 15])
+cp.store_fig("stochastic-gradient-plot", close = True)
+

+

+
+
+

+
stochastic-gradient-plot.png
+

+

+
+

+Let's see the solution to our algorithm.
+

+
+

+
print(theta)
+

+

+
+
+[[4.21076011]
+ [2.74856079]]
+

+
+

+To perform linear regression using stochastic GD with Scikit-Learn,
+you can use the SGDRegressor class, which defaults to optimizing
+the MSE cost function. 
+

+
+

+
from sklearn.linear_model import SGDRegressor
+
+sgd_reg = SGDRegressor(max_iter=1000, tol=1e-5, penalty=None, eta0=0.01,
+                       n_iter_no_change=100, random_state=42)
+sgd_reg.fit(X, y.ravel())  # y.ravel() because fit() expects 1D targets
+

+

+
+
+
+
+++
++
++
+
+
+
+
+
+
+
+
+
+
+
+
+
+





FUNCTIONDEFINITIONMORE INFO
sklearn.linear_model.SGDRegressor(...)Linear model fitted by minimizing a regularized empirical loss with SGD.Link

+
+

+As can be seen our solution is quite close to the one returned by the normal solution.
+

+
+

+
print(sgd_reg.intercept_, sgd_reg.coef_)
+

+

+
+
+[4.21278812] [2.77270267]
+

+

+

+
+

+
Mini-batch Gradient Descent

+

+

+Each step, instead of computing the gradients based on the full
+set (as in batch GD) or based on just one instance (as in stochastic GD),
+mini-batch GD computes the gradients on small random sets of instances called mini-batches.
+

+
+

+Time to compare all three.
+

+
+

+
from math import ceil
+
+n_epochs = 50
+minibatch_size = 20
+n_batches_per_epoch = ceil(m / minibatch_size)
+
+np.random.seed(42)
+theta = np.random.randn(2, 1)  # random initialization
+
+t0, t1 = 200, 1000  # learning schedule hyperparameters
+
+def learning_schedule(t):
+    return t0 / (t + t1)
+

+

+
+

+
theta_path_mgd = []
+for epoch in range(n_epochs):
+    shuffled_indices = np.random.permutation(m)
+    X_b_shuffled = X_b[shuffled_indices]
+    y_shuffled = y[shuffled_indices]
+    for iteration in range(0, n_batches_per_epoch):
+        idx = iteration * minibatch_size
+        xi = X_b_shuffled[idx : idx + minibatch_size]
+        yi = y_shuffled[idx : idx + minibatch_size]
+        gradients = 2 / minibatch_size * xi.T @ (xi @ theta - yi)
+        eta = learning_schedule(iteration)
+        theta = theta - eta * gradients
+        theta_path_mgd.append(theta)
+
+theta_path_bgd = np.array(theta_path_bgd)
+theta_path_sgd = np.array(theta_path_sgd)
+theta_path_mgd = np.array(theta_path_mgd)
+

+

+
+

+
plt.figure(figsize=(11, 6))
+plt.plot(theta_path_sgd[:, 0], theta_path_sgd[:, 1], "-s", linewidth=1,
+         label="Stochastic")
+plt.plot(theta_path_mgd[:, 0], theta_path_mgd[:, 1], "-+", linewidth=2,
+         label="Mini-batch")
+plt.plot(theta_path_bgd[:, 0], theta_path_bgd[:, 1], "-o", linewidth=3,
+         label="Batch")
+plt.legend(loc="upper left")
+plt.xlabel(r"$\theta_0$")
+plt.ylabel(r"$\theta_1$   ", rotation=0)
+plt.axis([2.6, 4.6, 2.3, 3.4])
+
+cp.store_fig("mini-batch-gradient-plot", close = True)
+

+

+
+
+

+
mini-batch-gradient-plot.png
+

+

+
+

+They all end up near the minimum, but batch GD’s path actually stops at the minimum,
+while both stochastic GD and mini-batch GD continue to walk around.
+

+

+

+

+
+

+
Polynomial Regression

+

+

+What if your data is more complex than a straight line?
+

+
+

+Surprisingly, you can use a linear model to fit nonlinear data.
+A simple way to do this is to add powers of each feature as new features, then
+train a linear model on this extended set of features. This technique is called polynomial regression.
+

+
+

+First generate some random data.
+

+
+

+
np.random.seed(42)
+m = 100
+X = 6 * np.random.rand(m, 1) - 3
+y = 0.5 * X ** 2 + X + 2 + np.random.randn(m, 1)
+

+

+
+

+And now we plot this data.
+

+
+

+
plt.figure(figsize=(11, 6))
+
+plt.plot(X, y, ".", label="Data Points")
+plt.xlabel("$x_1$")
+plt.ylabel("$y$", rotation=0)
+plt.axis([-3, 3, 0, 10])
+plt.legend(loc="upper left")
+
+cp.store_fig("quadratic-data-plot", close = True)
+

+

+
+
+

+
quadratic-data-plot.png
+

+

+
+

+A single line wont fit. We need to use sklearn.preprocessing.PolynomialFeatures()
+for this:
+

+
+

+
from sklearn.preprocessing import PolynomialFeatures
+
+poly_features = PolynomialFeatures(degree=2, include_bias=False)
+X_poly = poly_features.fit_transform(X)
+
+print(X[0])
+print(X_poly[0])
+

+

+
+
+[-0.75275929]
+[-0.75275929  0.56664654]
+

+
+
+
+
+++
++
++
+
+
+
+
+
+
+
+
+
+
+
+
+
+





FUNCTIONDEFINITIONMORE INFO
sklearn.preprocessing.PolynomialFeatures(...)Generate polynomial and interaction features.Link

+
+

+We can now see our results.
+

+
+

+
lin_reg = LinearRegression()
+lin_reg.fit(X_poly, y)
+print(lin_reg.intercept_, lin_reg.coef_)
+
+X_new = np.linspace(-3, 3, 100).reshape(100, 1)
+X_new_poly = poly_features.transform(X_new)
+y_new = lin_reg.predict(X_new_poly)
+

+

+
+
+[1.78134581] [[0.93366893 0.56456263]]
+

+
+

+And plot our comparisons.
+

+
+

+
plt.figure(figsize=(11, 6))
+
+plt.plot(X, y, ".", label="Data Points")
+plt.plot(X_new, y_new, "-", label="Predictions")
+plt.xlabel("$x_1$")
+plt.ylabel("$y$", rotation=0)
+plt.legend(loc="upper left")
+plt.axis([-3, 3, 0, 10])
+
+cp.store_fig("quadratic-prediction-plot", close = True)
+

+

+
+
+

+
quadratic-prediction-plot.png
+

+

+

+
+

+
Learning Curves

+

+

+Let's see if increasing the degree number improves
+our model.
+

+
+

+
from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import make_pipeline
+
+plt.figure(figsize=(11, 6))
+for style, width, degree in (("-+", 2, 1), ("--", 2, 2), ("-", 1, 300)):
+    polybig_features = PolynomialFeatures(degree=degree, include_bias=False)
+    std_scaler = StandardScaler()
+    lin_reg = LinearRegression()
+    polynomial_regression = make_pipeline(polybig_features, std_scaler, lin_reg)
+    polynomial_regression.fit(X, y)
+    y_newbig = polynomial_regression.predict(X_new)
+    label = f"{degree} degree{'s' if degree > 1 else ''}"
+    plt.plot(X_new, y_newbig, style, label=label)
+

+

+
+

+
plt.plot(X, y, ".", label="Data Points")
+plt.legend(loc="upper left")
+plt.xlabel("$x_1$")
+plt.ylabel("$y$", rotation=0)
+plt.axis([-3, 3, 0, 10])
+cp.store_fig("quadratic-prediction-with-degrees-plot", close = True)
+plt.close()
+

+

+
+
+

+
quadratic-prediction-with-degrees-plot.png
+

+

+
+
+

+
from sklearn.model_selection import learning_curve
+
+train_sizes, train_scores, valid_scores = learning_curve(
+    LinearRegression(), X, y, train_sizes=np.linspace(0.01, 1.0, 40), cv=5,
+    scoring="neg_root_mean_squared_error")
+train_errors = -train_scores.mean(axis=1)
+valid_errors = -valid_scores.mean(axis=1)
+

+

+
+

+
plt.figure(figsize=(11, 6))
+
+plt.plot(train_sizes, train_errors, "-+", label="train")
+plt.plot(train_sizes, valid_errors, "-", label="valid")
+
+plt.xlabel("Training set size")
+plt.ylabel("RMSE")
+
+plt.legend(loc="upper right")
+plt.axis([0, 80, 0, 2.5])
+cp.store_fig("underfitting-learning-curves-plot", close = True)
+

+

+
+
+

+
underfitting-learning-curves-plot.png
+

+

+
+

+
from sklearn.pipeline import make_pipeline
+
+polynomial_regression = make_pipeline(
+    PolynomialFeatures(degree=10, include_bias=False),
+    LinearRegression())
+
+train_sizes, train_scores, valid_scores = learning_curve(
+    polynomial_regression, X, y, train_sizes=np.linspace(0.01, 1.0, 40), cv=5,
+    scoring="neg_root_mean_squared_error")
+

+

+
+

+
train_errors = -train_scores.mean(axis=1)
+valid_errors = -valid_scores.mean(axis=1)
+

+

+
+

+
plt.figure(figsize=(11, 6))
+
+plt.plot(train_sizes, train_errors, "-+", label="train")
+plt.plot(train_sizes, valid_errors, "-", label="valid")
+plt.legend(loc="upper right")
+plt.xlabel("Training set size")
+plt.ylabel("RMSE")
+plt.axis([0, 80, 0, 2.5])
+
+cp.store_fig("learning-curves-plot", close = True)
+

+

+
+
+

+
learning-curves-plot.png
+

+

+

+

+

+
+

+
Regularised Linear Models

+

+

+

+
Ridge Regression

+

+

+
np.random.seed(42)
+m = 20
+X = 3 * np.random.rand(m, 1)
+y = 1 + 0.5 * X + np.random.randn(m, 1) / 1.5
+X_new = np.linspace(0, 3, 100).reshape(100, 1)
+

+

+
+

+
plt.figure(figsize=(11, 6))
+
+plt.plot(X, y, ".", label = "Data Points")
+plt.xlabel("$x_1$")
+plt.ylabel("$y$  ", rotation=0)
+plt.legend(loc="upper right")
+plt.axis([0, 3, 0, 3.5])
+
+cp.store_fig("ridge-regression-plot-init", close = True)
+

+

+
+
+

+
ridge-regression-plot-init.png
+

+

+
+

+
from sklearn.linear_model import Ridge
+
+ridge_reg = Ridge(alpha=0.1, solver="cholesky")
+ridge_reg.fit(X, y)
+ridge_reg.predict([[1.5]])
+

+

+
+

+
def plot_model(model_class, polynomial, alphas, **model_kwargs):
+    plt.plot(X, y, ".")
+    for alpha, style in zip(alphas, (":", "--", "-")):
+        if alpha > 0:
+            model = model_class(alpha, **model_kwargs)
+        else:
+            model = LinearRegression()
+        if polynomial:
+            model = make_pipeline(
+                PolynomialFeatures(degree=10, include_bias=False),
+                StandardScaler(),
+                model)
+        model.fit(X, y)
+        y_new_regul = model.predict(X_new)
+        plt.plot(X_new, y_new_regul, style, linewidth=2,
+                 label=fr"$\alpha = {alpha}$")
+    plt.legend(loc="upper left")
+    plt.xlabel("$x_1$")
+    plt.axis([0, 3, 0, 3.5])
+
+plt.figure(figsize=(11, 6))
+
+plt.subplot(121)
+plot_model(Ridge, polynomial=False, alphas=(0, 10, 100), random_state=42)
+plt.ylabel("$y$  ", rotation=0)
+plt.subplot(122)
+plot_model(Ridge, polynomial=True, alphas=(0, 10**-5, 1), random_state=42)
+
+cp.store_fig("ridge-regression-plot-result", close = True)
+

+

+
+
+

+
ridge-regression-plot-result.png
+

+

+
+

+
sgd_reg = SGDRegressor(penalty="l2", alpha=0.1 / m, tol=None,
+                       max_iter=1000, eta0=0.01, random_state=42)
+sgd_reg.fit(X, y.ravel())  # y.ravel() because fit() expects 1D targets
+print(sgd_reg.predict([[1.5]]))
+

+

+
+
+[1.55302613]
+

+
+

+
ridge_reg = Ridge(alpha=0.1, solver="sag", random_state=42)
+ridge_reg.fit(X, y)
+print(ridge_reg.predict([[1.5]]))
+

+

+
+
+[[1.55326019]]
+

+
+

+
alpha = 0.1
+A = np.array([[0., 0.], [0., 1.]])
+X_b = np.c_[np.ones(m), X]
+print(np.linalg.inv(X_b.T @ X_b + alpha * A) @ X_b.T @ y)
+
+print(ridge_reg.intercept_, ridge_reg.coef_  )
+

+

+
+
+[[0.97898394]
+ [0.3828496 ]]
+[0.97896386] [[0.38286422]]
+

+

+

+
+

+
Lasso Regression

+

+

+
from sklearn.linear_model import Lasso
+
+lasso_reg = Lasso(alpha=0.1)
+lasso_reg.fit(X, y)
+lasso_reg.predict([[1.5]])
+

+

+
+

+
plt.figure(figsize=(11, 6))
+
+plt.subplot(121)
+
+plot_model(Lasso, polynomial=False, alphas=(0, 0.1, 1), random_state=42)
+plt.ylabel("$y$  ", rotation=0)
+plt.subplot(122)
+plot_model(Lasso, polynomial=True, alphas=(0, 1e-2, 1), random_state=42)
+
+cp.store_fig("lasso-regression-plot-init", close = True)
+

+

+
+
+

+
lasso-regression-plot-init.png
+

+

+
+

+
t1a, t1b, t2a, t2b = -1, 3, -1.5, 1.5
+
+t1s = np.linspace(t1a, t1b, 500)
+t2s = np.linspace(t2a, t2b, 500)
+t1, t2 = np.meshgrid(t1s, t2s)
+T = np.c_[t1.ravel(), t2.ravel()]
+Xr = np.array([[1, 1], [1, -1], [1, 0.5]])
+yr = 2 * Xr[:, :1] + 0.5 * Xr[:, 1:]
+
+J = (1 / len(Xr) * ((T @ Xr.T - yr.T) ** 2).sum(axis=1)).reshape(t1.shape)
+
+N1 = np.linalg.norm(T, ord=1, axis=1).reshape(t1.shape)
+N2 = np.linalg.norm(T, ord=2, axis=1).reshape(t1.shape)
+
+t_min_idx = np.unravel_index(J.argmin(), J.shape)
+t1_min, t2_min = t1[t_min_idx], t2[t_min_idx]
+
+t_init = np.array([[0.25], [-1]])
+
+def bgd_path(theta, X, y, l1, l2, core=1, eta=0.05, n_iterations=200):
+    path = [theta]
+    for iteration in range(n_iterations):
+        gradients = (core * 2 / len(X) * X.T @ (X @ theta - y)
+                     + l1 * np.sign(theta) + l2 * theta)
+        theta = theta - eta * gradients
+        path.append(theta)
+    return np.array(path)
+
+fig, axes = plt.subplots(2, 2, sharex=True, sharey=True, figsize=(10, 8))
+
+for i, N, l1, l2, title in ((0, N1, 2.0, 0, "Lasso"), (1, N2, 0, 2.0, "Ridge")):
+    JR = J + l1 * N1 + l2 * 0.5 * N2 ** 2
+
+    tr_min_idx = np.unravel_index(JR.argmin(), JR.shape)
+    t1r_min, t2r_min = t1[tr_min_idx], t2[tr_min_idx]
+
+    levels = np.exp(np.linspace(0, 1, 20)) - 1
+    levelsJ = levels * (J.max() - J.min()) + J.min()
+    levelsJR = levels * (JR.max() - JR.min()) + JR.min()
+    levelsN = np.linspace(0, N.max(), 10)
+
+    path_J = bgd_path(t_init, Xr, yr, l1=0, l2=0)
+    path_JR = bgd_path(t_init, Xr, yr, l1, l2)
+    path_N = bgd_path(theta=np.array([[2.0], [0.5]]), X=Xr, y=yr,
+                      l1=np.sign(l1) / 3, l2=np.sign(l2), core=0)
+    ax = axes[i, 0]
+    ax.grid()
+    ax.axhline(y=0, color="k")
+    ax.axvline(x=0, color="k")
+    ax.contourf(t1, t2, N / 2.0, levels=levelsN)
+    ax.plot(path_N[:, 0], path_N[:, 1], "y--")
+    ax.plot(0, 0, "ys")
+    ax.plot(t1_min, t2_min, "ys")
+    ax.set_title(fr"$\ell_{i + 1}$ penalty")
+    ax.axis([t1a, t1b, t2a, t2b])
+    if i == 1:
+        ax.set_xlabel(r"$\theta_1$")
+    ax.set_ylabel(r"$\theta_2$", rotation=0)
+
+    ax = axes[i, 1]
+    ax.grid()
+    ax.axhline(y=0, color="k")
+    ax.axvline(x=0, color="k")
+    ax.contourf(t1, t2, JR, levels=levelsJR, alpha=0.9)
+    ax.plot(path_JR[:, 0], path_JR[:, 1], "w-o")
+    ax.plot(path_N[:, 0], path_N[:, 1], "y--")
+    ax.plot(0, 0, "ys")
+    ax.plot(t1_min, t2_min, "ys")
+    ax.plot(t1r_min, t2r_min, "rs")
+    ax.set_title(title)
+    ax.axis([t1a, t1b, t2a, t2b])
+    if i == 1:
+        ax.set_xlabel(r"$\theta_1$")
+
+cp.store_fig("lasso-regression-plot-lasso-vs-ridge", close = True)
+

+

+
+
+

+
lasso-regression-plot-lasso-vs-ridge.png
+

+

+

+

+
+

+
Elastic Net

+

+

+
from sklearn.linear_model import ElasticNet
+
+elastic_net = ElasticNet(alpha=0.1, l1_ratio=0.5)
+elastic_net.fit(X, y)
+print(elastic_net.predict([[1.5]]))
+

+

+
+
+[1.54333232]
+

+

+

+
+

+
Early Stopping

+

+

+
from copy import deepcopy
+from sklearn.metrics import root_mean_squared_error
+from sklearn.preprocessing import StandardScaler
+
+np.random.seed(42)
+m = 100
+X = 6 * np.random.rand(m, 1) - 3
+y = 0.5 * X ** 2 + X + 2 + np.random.randn(m, 1)
+X_train, y_train = X[: m // 2], y[: m // 2, 0]
+X_valid, y_valid = X[m // 2 :], y[m // 2 :, 0]
+
+preprocessing = make_pipeline(PolynomialFeatures(degree=90, include_bias=False),
+                              StandardScaler())
+X_train_prep = preprocessing.fit_transform(X_train)
+X_valid_prep = preprocessing.transform(X_valid)
+sgd_reg = SGDRegressor(penalty=None, eta0=0.002, random_state=42)
+n_epochs = 500
+best_valid_rmse = float('inf')
+train_errors, val_errors = [], [] 
+
+for epoch in range(n_epochs):
+    sgd_reg.partial_fit(X_train_prep, y_train)
+    y_valid_predict = sgd_reg.predict(X_valid_prep)
+    val_error = root_mean_squared_error(y_valid, y_valid_predict)
+    if val_error < best_valid_rmse:
+        best_valid_rmse = val_error
+        best_model = deepcopy(sgd_reg)
+
+    y_train_predict = sgd_reg.predict(X_train_prep)
+    train_error = root_mean_squared_error(y_train, y_train_predict)
+    val_errors.append(val_error)
+    train_errors.append(train_error)
+
+best_epoch = np.argmin(val_errors)
+plt.figure(figsize=(11, 6))
+plt.annotate('Best model',
+             xy=(best_epoch, best_valid_rmse),
+             xytext=(best_epoch, best_valid_rmse + 0.5),
+             ha="center",
+             arrowprops=dict(facecolor='black', shrink=0.05))
+plt.plot([0, n_epochs], [best_valid_rmse, best_valid_rmse], "k:", linewidth=2)
+plt.plot(val_errors, "-", linewidth=3, label="Validation set")
+plt.plot(best_epoch, best_valid_rmse, "bo")
+plt.plot(train_errors, "--", linewidth=2, label="Training set")
+plt.legend(loc="upper right")
+plt.xlabel("Epoch")
+plt.ylabel("RMSE")
+plt.axis([0, n_epochs, 0, 3.5])
+cp.store_fig("early-stopping-plot-quadratic-data", close = True)
+

+

+
+
+

+
early-stopping-plot-quadratic-data.png
+

+

+

+

+

+
+

+
Logistic Regression

+

+

+

+
Estimating Probabilities

+

+

+
lim = 6
+t = np.linspace(-lim, lim, 100)
+sig = 1 / (1 + np.exp(-t))
+

+

+
+

+
plt.figure(figsize=(11, 6))
+plt.plot([-lim, lim], [0, 0], "k-")
+plt.plot([-lim, lim], [0.5, 0.5], "k:")
+plt.plot([-lim, lim], [1, 1], "r:")
+plt.plot([0, 0], [-1.1, 1.1], "-")
+plt.plot(t, sig, "-", linewidth=2, label=r"$\sigma(t) = \dfrac{1}{1 + e^{-t}}$")
+plt.xlabel("t")
+plt.legend(loc="upper left")
+plt.axis([-lim, lim, -0.1, 1.1])
+plt.gca().set_yticks([0, 0.25, 0.5, 0.75, 1])
+
+cp.store_fig("logistic-function", close = True)
+

+

+
+
+

+
logistic-function.png
+

+

+

+

+
+

+
Desicion Boundaries

+

+

+
from sklearn.datasets import load_iris
+
+iris = load_iris(as_frame=True)
+print(list(iris))
+

+

+
+
+['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module']
+

+
+

+
print(iris.DESCR)
+

+

+
+
+.. _iris_dataset:
+Iris plants dataset
+--------------------
+**Data Set Characteristics:**
+:Number of Instances: 150 (50 in each of three classes)
+:Number of Attributes: 4 numeric, predictive attributes and the class
+:Attribute Information:
+    - sepal length in cm
+    - sepal width in cm
+    - petal length in cm
+    - petal width in cm
+    - class:
+            - Iris-Setosa
+            - Iris-Versicolour
+            - Iris-Virginica
+:Summary Statistics:
+============== ==== ==== ======= ===== ====================
+                Min  Max   Mean    SD   Class Correlation
+============== ==== ==== ======= ===== ====================
+sepal length:   4.3  7.9   5.84   0.83    0.7826
+sepal width:    2.0  4.4   3.05   0.43   -0.4194
+petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)
+petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)
+============== ==== ==== ======= ===== ====================
+:Missing Attribute Values: None
+:Class Distribution: 33.3% for each of 3 classes.
+:Creator: R.A. Fisher
+:Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
+:Date: July, 1988
+The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken
+from Fisher's paper. Note that it's the same as in R, but not as in the UCI
+Machine Learning Repository, which has two wrong data points.
+This is perhaps the best known database to be found in the
+pattern recognition literature.  Fisher's paper is a classic in the field and
+is referenced frequently to this day.  (See Duda & Hart, for example.)  The
+data set contains 3 classes of 50 instances each, where each class refers to a
+type of iris plant.  One class is linearly separable from the other 2; the
+latter are NOT linearly separable from each other.
+|details-start|
+**References**
+|details-split|
+- Fisher, R.A. "The use of multiple measurements in taxonomic problems"
+  Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
+  Mathematical Statistics" (John Wiley, NY, 1950).
+- Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.
+  (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.
+- Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
+  Structure and Classification Rule for Recognition in Partially Exposed
+  Environments".  IEEE Transactions on Pattern Analysis and Machine
+  Intelligence, Vol. PAMI-2, No. 1, 67-71.
+- Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions
+  on Information Theory, May 1972, 431-433.
+- See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II
+  conceptual clustering system finds 3 classes in the data.
+- Many, many more ...
+|details-end|
+

+
+

+
iris.data.head(3)
+

+

+
+
+   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)
+0                5.1               3.5                1.4               0.2
+1                4.9               3.0                1.4               0.2
+2                4.7               3.2                1.3               0.2
+

+
+

+
iris.target.head(3)  # note that the instances are not shuffled
+

+

+
+
+

+
iris.target_names
+

+

+
+

+
from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+
+X = iris.data[["petal width (cm)"]].values
+y = iris.target_names[iris.target] == 'virginica'
+X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
+
+log_reg = LogisticRegression(random_state=42)
+log_reg.fit(X_train, y_train)
+

+

+
+

+
X_new = np.linspace(0, 3, 1000).reshape(-1, 1) 
+y_proba = log_reg.predict_proba(X_new)
+decision_boundary = X_new[y_proba[:, 1] >= 0.5][0, 0]
+

+

+
+

+
plt.figure(figsize=(10, 6))
+plt.plot(X_new, y_proba[:, 0], "b--", linewidth=2,
+         label="Not Iris virginica proba")
+plt.plot(X_new, y_proba[:, 1], "g-", linewidth=2, label="Iris virginica proba")
+plt.plot([decision_boundary, decision_boundary], [0, 1], "k:", linewidth=2,
+         label="Decision boundary")
+plt.arrow(x=decision_boundary, y=0.08, dx=-0.3, dy=0,
+          head_width=0.05, head_length=0.1, fc="b", ec="b")
+plt.arrow(x=decision_boundary, y=0.92, dx=0.3, dy=0,
+          head_width=0.05, head_length=0.1, fc="g", ec="g")
+plt.plot(X_train[y_train == 0], y_train[y_train == 0], "bs")
+plt.plot(X_train[y_train == 1], y_train[y_train == 1], "g^")
+plt.xlabel("Petal width (cm)"), plt.ylabel("Probability")
+plt.legend(loc="center left"), plt.axis([0, 3, -0.02, 1.02])
+plt.grid(), cp.store_fig("logistic_regression_plot", close = True)
+

+

+
+

+
decision_boundary
+

+

+
+

+
log_reg.predict([[1.7], [1.5]])
+

+

+
+

+
X = iris.data[["petal length (cm)", "petal width (cm)"]].values
+y = iris.target_names[iris.target] == 'virginica'
+X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
+
+log_reg = LogisticRegression(C=2, random_state=42)
+log_reg.fit(X_train, y_train)
+
+# for the contour plot
+x0, x1 = np.meshgrid(np.linspace(2.9, 7, 500).reshape(-1, 1),
+                     np.linspace(0.8, 2.7, 200).reshape(-1, 1))
+X_new = np.c_[x0.ravel(), x1.ravel()]  # one instance per point on the figure
+y_proba = log_reg.predict_proba(X_new)
+zz = y_proba[:, 1].reshape(x0.shape)
+
+# for the decision boundary
+left_right = np.array([2.9, 7])
+boundary = -((log_reg.coef_[0, 0] * left_right + log_reg.intercept_[0])
+             / log_reg.coef_[0, 1])
+

+

+
+

+
plt.figure(figsize=(10, 6))
+plt.plot(X_train[y_train == 0, 0], X_train[y_train == 0, 1], "bs")
+plt.plot(X_train[y_train == 1, 0], X_train[y_train == 1, 1], "g^")
+contour = plt.contour(x0, x1, zz, cmap=plt.cm.brg)
+plt.clabel(contour, inline=1)
+plt.plot(left_right, boundary, "k--", linewidth=3)
+plt.text(3.5, 1.27, "Not Iris virginica", color="b", ha="center")
+plt.text(6.5, 2.3, "Iris virginica", color="g", ha="center")
+plt.xlabel("Petal length")
+plt.ylabel("Petal width")
+plt.axis([2.9, 7, 0.8, 2.7])
+cp.store_fig("logistic-regression-contour-plot", close = True)
+

+

+
+
+

+
logistic-regression-contour-plot.png
+

+

+

+

+
+

+
Softmax Regression

+

+

+Softmax regression (or multinomial logistic regression) is a generalization
+of logistic regression to the case where we want to handle multiple classes.
+

+
+

+
X = iris.data[["petal length (cm)", "petal width (cm)"]].values
+y = iris["target"]
+X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
+
+softmax_reg = LogisticRegression(C=30, random_state=42)
+softmax_reg.fit(X_train, y_train)
+

+

+
+

+
softmax_reg.predict([[5, 2]])
+softmax_reg.predict_proba([[5, 2]]).round(2)
+

+

+
+

+
from matplotlib.colors import ListedColormap
+
+custom_cmap = ListedColormap(["#fafab0", "#9898ff", "#a0faa0"])
+
+x0, x1 = np.meshgrid(np.linspace(0, 8, 500).reshape(-1, 1),
+                     np.linspace(0, 3.5, 200).reshape(-1, 1))
+X_new = np.c_[x0.ravel(), x1.ravel()]
+
+y_proba = softmax_reg.predict_proba(X_new)
+y_predict = softmax_reg.predict(X_new)
+
+zz1 = y_proba[:, 1].reshape(x0.shape)
+zz = y_predict.reshape(x0.shape)
+

+

+
+

+
plt.figure(figsize=(10, 6))
+plt.plot(X[y == 2, 0], X[y == 2, 1], "g^", label="Iris virginica")
+plt.plot(X[y == 1, 0], X[y == 1, 1], "bs", label="Iris versicolor")
+plt.plot(X[y == 0, 0], X[y == 0, 1], "yo", label="Iris setosa")
+
+plt.contourf(x0, x1, zz, cmap=custom_cmap)
+contour = plt.contour(x0, x1, zz1, cmap="hot")
+plt.clabel(contour, inline=1)
+plt.xlabel("Petal length")
+plt.ylabel("Petal width")
+plt.legend(loc="center left")
+plt.axis([0.5, 7, 0, 3.5])
+
+cp.store_fig("softmax-regression-contour-plot", close = True)
+

+

+
+
+

+
softmax-regression-contour-plot.png
+

+

+

+

+

+

+

+
Author: Daniel McGuiness

+

+
+
diff --git a/docs/config/test.js b/docs/config/test.js
index c337e4f..dad7451 100644
--- a/docs/config/test.js
+++ b/docs/config/test.js
@@ -597,7 +597,7 @@ var org_html_manager = {
     t.CONSOLE.style.overflow = "hidden";
     t.CONSOLE.style.verticalAlign = "middle";
     t.CONSOLE.style.zIndex = "9";
-    t.CONSOLE.style.border = "1px solid #cccccc";
+    t.CONSOLE.style.border = "0px solid #cccccc";
     t.CONSOLE.id = "org-info-js_console-container";
 
     t.BODY.insertBefore(t.CONSOLE, t.BODY.firstChild);
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