{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Unsupervised Learning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* In **supervised learning**, we know the right answer beforehand when we train our model\n", "* In **reinforcement learning**, we define a measure of reward for particular actions by the agent. \n", "* In **unsupervised learning**, however, we are dealing with unlabeled data or data of unknown structure. \n", "* Using unsupervised learning techniques, we are able to explore the structure of our data to extract meaningful information **without the guidance of a known outcome variable or reward function**." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction to scikit-learn\n", "\n", "Scikit-learn Cheat sheet at https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 5.1 3.5 1.4 0.2]\n", " [ 4.9 3. 1.4 0.2]\n", " [ 4.7 3.2 1.3 0.2]\n", " [ 4.6 3.1 1.5 0.2]\n", " [ 5. 3.6 1.4 0.2]\n", " [ 5.4 3.9 1.7 0.4]\n", " [ 4.6 3.4 1.4 0.3]\n", " [ 5. 3.4 1.5 0.2]\n", " [ 4.4 2.9 1.4 0.2]\n", " [ 4.9 3.1 1.5 0.1]\n", " [ 5.4 3.7 1.5 0.2]\n", " [ 4.8 3.4 1.6 0.2]\n", " [ 4.8 3. 1.4 0.1]\n", " [ 4.3 3. 1.1 0.1]\n", " [ 5.8 4. 1.2 0.2]\n", " [ 5.7 4.4 1.5 0.4]\n", " [ 5.4 3.9 1.3 0.4]\n", " [ 5.1 3.5 1.4 0.3]\n", " [ 5.7 3.8 1.7 0.3]\n", " [ 5.1 3.8 1.5 0.3]\n", " [ 5.4 3.4 1.7 0.2]\n", " [ 5.1 3.7 1.5 0.4]\n", " [ 4.6 3.6 1. 0.2]\n", " [ 5.1 3.3 1.7 0.5]\n", " [ 4.8 3.4 1.9 0.2]\n", " [ 5. 3. 1.6 0.2]\n", " [ 5. 3.4 1.6 0.4]\n", " [ 5.2 3.5 1.5 0.2]\n", " [ 5.2 3.4 1.4 0.2]\n", " [ 4.7 3.2 1.6 0.2]\n", " [ 4.8 3.1 1.6 0.2]\n", " [ 5.4 3.4 1.5 0.4]\n", " [ 5.2 4.1 1.5 0.1]\n", " [ 5.5 4.2 1.4 0.2]\n", " [ 4.9 3.1 1.5 0.1]\n", " [ 5. 3.2 1.2 0.2]\n", " [ 5.5 3.5 1.3 0.2]\n", " [ 4.9 3.1 1.5 0.1]\n", " [ 4.4 3. 1.3 0.2]\n", " [ 5.1 3.4 1.5 0.2]\n", " [ 5. 3.5 1.3 0.3]\n", " [ 4.5 2.3 1.3 0.3]\n", " [ 4.4 3.2 1.3 0.2]\n", " [ 5. 3.5 1.6 0.6]\n", " [ 5.1 3.8 1.9 0.4]\n", " [ 4.8 3. 1.4 0.3]\n", " [ 5.1 3.8 1.6 0.2]\n", " [ 4.6 3.2 1.4 0.2]\n", " [ 5.3 3.7 1.5 0.2]\n", " [ 5. 3.3 1.4 0.2]\n", " [ 7. 3.2 4.7 1.4]\n", " [ 6.4 3.2 4.5 1.5]\n", " [ 6.9 3.1 4.9 1.5]\n", " [ 5.5 2.3 4. 1.3]\n", " [ 6.5 2.8 4.6 1.5]\n", " [ 5.7 2.8 4.5 1.3]\n", " [ 6.3 3.3 4.7 1.6]\n", " [ 4.9 2.4 3.3 1. ]\n", " [ 6.6 2.9 4.6 1.3]\n", " [ 5.2 2.7 3.9 1.4]\n", " [ 5. 2. 3.5 1. ]\n", " [ 5.9 3. 4.2 1.5]\n", " [ 6. 2.2 4. 1. ]\n", " [ 6.1 2.9 4.7 1.4]\n", " [ 5.6 2.9 3.6 1.3]\n", " [ 6.7 3.1 4.4 1.4]\n", " [ 5.6 3. 4.5 1.5]\n", " [ 5.8 2.7 4.1 1. ]\n", " [ 6.2 2.2 4.5 1.5]\n", " [ 5.6 2.5 3.9 1.1]\n", " [ 5.9 3.2 4.8 1.8]\n", " [ 6.1 2.8 4. 1.3]\n", " [ 6.3 2.5 4.9 1.5]\n", " [ 6.1 2.8 4.7 1.2]\n", " [ 6.4 2.9 4.3 1.3]\n", " [ 6.6 3. 4.4 1.4]\n", " [ 6.8 2.8 4.8 1.4]\n", " [ 6.7 3. 5. 1.7]\n", " [ 6. 2.9 4.5 1.5]\n", " [ 5.7 2.6 3.5 1. ]\n", " [ 5.5 2.4 3.8 1.1]\n", " [ 5.5 2.4 3.7 1. ]\n", " [ 5.8 2.7 3.9 1.2]\n", " [ 6. 2.7 5.1 1.6]\n", " [ 5.4 3. 4.5 1.5]\n", " [ 6. 3.4 4.5 1.6]\n", " [ 6.7 3.1 4.7 1.5]\n", " [ 6.3 2.3 4.4 1.3]\n", " [ 5.6 3. 4.1 1.3]\n", " [ 5.5 2.5 4. 1.3]\n", " [ 5.5 2.6 4.4 1.2]\n", " [ 6.1 3. 4.6 1.4]\n", " [ 5.8 2.6 4. 1.2]\n", " [ 5. 2.3 3.3 1. ]\n", " [ 5.6 2.7 4.2 1.3]\n", " [ 5.7 3. 4.2 1.2]\n", " [ 5.7 2.9 4.2 1.3]\n", " [ 6.2 2.9 4.3 1.3]\n", " [ 5.1 2.5 3. 1.1]\n", " [ 5.7 2.8 4.1 1.3]\n", " [ 6.3 3.3 6. 2.5]\n", " [ 5.8 2.7 5.1 1.9]\n", " [ 7.1 3. 5.9 2.1]\n", " [ 6.3 2.9 5.6 1.8]\n", " [ 6.5 3. 5.8 2.2]\n", " [ 7.6 3. 6.6 2.1]\n", " [ 4.9 2.5 4.5 1.7]\n", " [ 7.3 2.9 6.3 1.8]\n", " [ 6.7 2.5 5.8 1.8]\n", " [ 7.2 3.6 6.1 2.5]\n", " [ 6.5 3.2 5.1 2. ]\n", " [ 6.4 2.7 5.3 1.9]\n", " [ 6.8 3. 5.5 2.1]\n", " [ 5.7 2.5 5. 2. ]\n", " [ 5.8 2.8 5.1 2.4]\n", " [ 6.4 3.2 5.3 2.3]\n", " [ 6.5 3. 5.5 1.8]\n", " [ 7.7 3.8 6.7 2.2]\n", " [ 7.7 2.6 6.9 2.3]\n", " [ 6. 2.2 5. 1.5]\n", " [ 6.9 3.2 5.7 2.3]\n", " [ 5.6 2.8 4.9 2. ]\n", " [ 7.7 2.8 6.7 2. ]\n", " [ 6.3 2.7 4.9 1.8]\n", " [ 6.7 3.3 5.7 2.1]\n", " [ 7.2 3.2 6. 1.8]\n", " [ 6.2 2.8 4.8 1.8]\n", " [ 6.1 3. 4.9 1.8]\n", " [ 6.4 2.8 5.6 2.1]\n", " [ 7.2 3. 5.8 1.6]\n", " [ 7.4 2.8 6.1 1.9]\n", " [ 7.9 3.8 6.4 2. ]\n", " [ 6.4 2.8 5.6 2.2]\n", " [ 6.3 2.8 5.1 1.5]\n", " [ 6.1 2.6 5.6 1.4]\n", " [ 7.7 3. 6.1 2.3]\n", " [ 6.3 3.4 5.6 2.4]\n", " [ 6.4 3.1 5.5 1.8]\n", " [ 6. 3. 4.8 1.8]\n", " [ 6.9 3.1 5.4 2.1]\n", " [ 6.7 3.1 5.6 2.4]\n", " [ 6.9 3.1 5.1 2.3]\n", " [ 5.8 2.7 5.1 1.9]\n", " [ 6.8 3.2 5.9 2.3]\n", " [ 6.7 3.3 5.7 2.5]\n", " [ 6.7 3. 5.2 2.3]\n", " [ 6.3 2.5 5. 1.9]\n", " [ 6.5 3. 5.2 2. ]\n", " [ 6.2 3.4 5.4 2.3]\n", " [ 5.9 3. 5.1 1.8]]\n", "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", " 2 2]\n" ] } ], "source": [ "# Load datasets\n", "from sklearn import datasets\n", "iris = datasets.load_iris()\n", "digits = datasets.load_digits()\n", "print(iris.data)\n", "print(iris.target)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The data is always a 2D array, shape (n_samples, n_features)\n", "* Pandas.io provides tools to read data from common formats including CSV, Excel, JSON and SQL. DataFrames may also be constructed from lists of tuples or dicts. Pandas handles heterogeneous data smoothly and provides tools for manipulation and conversion into a numeric array suitable for scikit-learn. \n", "* Numpy/routines.io for standard loading of columnar data into numpy arrays" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['DictionaryLearning',\n", " 'FactorAnalysis',\n", " 'FastICA',\n", " 'IncrementalPCA',\n", " 'KernelPCA',\n", " 'LatentDirichletAllocation',\n", " 'MiniBatchDictionaryLearning',\n", " 'MiniBatchSparsePCA',\n", " 'NMF',\n", " 'PCA',\n", " 'RandomizedPCA',\n", " 'SparseCoder',\n", " 'SparsePCA',\n", " 'TruncatedSVD',\n", " '__all__',\n", " '__builtins__',\n", " '__cached__',\n", " '__doc__',\n", " '__file__',\n", " '__loader__',\n", " '__name__',\n", " '__package__',\n", " '__path__',\n", " '__spec__',\n", " '_online_lda',\n", " 'base',\n", " 'cdnmf_fast',\n", " 'dict_learning',\n", " 'dict_learning_online',\n", " 'factor_analysis',\n", " 'fastica',\n", " 'fastica_',\n", " 'incremental_pca',\n", " 'kernel_pca',\n", " 'nmf',\n", " 'non_negative_factorization',\n", " 'online_lda',\n", " 'pca',\n", " 'randomized_svd',\n", " 'sparse_encode',\n", " 'sparse_pca',\n", " 'truncated_svd']" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn import decomposition\n", "dir(decomposition)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0333333333333\n", "0.00333333333333\n" ] }, { "data": { "text/plain": [ "0.66666666666666663" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn import preprocessing\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.preprocessing import Imputer\n", "from sklearn.metrics import mean_absolute_error, mean_squared_error\n", "\n", "X, y = iris.data[:, :2], iris.target\n", "# Split the data into training and test sets\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33)\n", "# Use the standard and normalizer options to scale the data\n", "scaler = preprocessing.StandardScaler().fit(X_train)\n", "standardized_X = scaler.transform(X_train)\n", "scaler = preprocessing.Normalizer().fit(X_train)\n", "normalized_X = scaler.transform(X_train)\n", "# Deal with missing data, axis 0 is columns\n", "imp = Imputer(missing_values=0, strategy='mean', axis=0) \n", "imp.fit_transform(X_train)\n", "# Error using MSE and MAE\n", "y_true = [3, -0.6, 2]\n", "y_pred = [3, -0.5, 2]\n", "print(mean_absolute_error(y_true, y_pred))\n", "print(mean_squared_error(y_true, y_pred))\n", "# Accuracy score\n", "y_true = [3, 4, 2]\n", "y_pred = [3, 5, 2]\n", "accuracy_score(y_true, y_pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dimensionality Reduction with scikit-learn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Often we are working with data of high dimensionality—each observation comes with a high number of measurements, that can present a challenge for limited storage space and the computational performance of machine learning algorithms. \n", "* Unsupervised dimensionality reduction is a commonly used approach in feature preprocessing to remove noise from data, which can also degrade the predictive performance of certain algorithms, and compress the data onto a smaller dimensional subspace while retaining most of the relevant information. \n", "* Sometimes, dimensionality reduction can also be useful for visualizing data—for example, a high-dimensional feature set can be projected onto one, two,or three-dimensional feature spaces in order to visualize it via 3D or 2D scatterplots or histograms. \n", "\n", "* There are two main categories of dimensionality reduction techniques: **feature selection** and **feature extraction**. Using feature selection, we select a subset of the original features. In feature extraction, we derive information from the feature set to construct a new feature subspace.\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Transformers are classes that implement both fit() and transform()\n", "* Estimators are classes that implement both fit() and predict()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "explained variance ratio (first two components): [ 0.92461621 0.05301557]\n" ] }, { "data": { "image/png": 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ozp498WFnz9mzQyxePBsRaGnRLl3K6KTSPH8Z7OO1eJQ4hgXjyDbzde7Mx+8n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\nQr7StLziV86Zm9UNPj/zfQFczPBmdUX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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Let us do the same above in 2D\n", "import matplotlib.pyplot as plt\n", "from sklearn import datasets\n", "from sklearn.decomposition import PCA\n", "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", "\n", "# Load data\n", "iris = datasets.load_iris()\n", "X = iris.data\n", "y = iris.target\n", "target_names = iris.target_names\n", "\n", "# Apply PCA\n", "pca = PCA(n_components=2)\n", "X_r = pca.fit_transform(X)\n", "\n", "# Percentage of variance explained for each components - How do you select the appropriate number of components\n", "print('explained variance ratio (first two components): %s'\n", " % str(pca.explained_variance_ratio_))\n", "\n", "# Plot the figure in 2D\n", "plt.figure()\n", "colors = ['navy', 'turquoise', 'darkorange']\n", "lw = 2\n", "\n", "for color, i, target_name in zip(colors, [0, 1, 2], target_names):\n", " plt.scatter(X_r[y == i, 0], X_r[y == i, 1], color=color, alpha=.8, lw=lw,\n", " label=target_name)\n", "plt.legend(loc='best', shadow=False, scatterpoints=1)\n", "plt.title('PCA of IRIS dataset')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction to TensorFlow\n", "\n", "### API guide is at https://www.tensorflow.org/api_guides/" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tensor(\"A:0\", shape=(2,), dtype=float32)\n", "Tensor(\"mul:0\", shape=(2,), dtype=float32)\n", "(2,)\n", "[ 2. 6.]\n", "[ 2. 6.]\n", "[ 1. 9.]\n", "[ 9. 81.]\n", "b'Hello, TensorFlow!'\n", "[ 2. 6.]\n" ] } ], "source": [ "import tensorflow as tf\n", "sess = tf.InteractiveSession()\n", "\n", "# Some tensor we want to print the value of\n", "a = tf.constant([1.0, 3.0],name = \"A\")\n", "b = a*3\n", "hello = tf.constant('Hello, TensorFlow!')\n", "\n", "print(a)\n", "print(b)\n", "#Explicitly call the shape function\n", "print(a.get_shape())\n", "\n", "#Execute the statements above\n", "print(sess.run(a+a))\n", "print(sess.run(a*2))\n", "print(sess.run(a**2))\n", "print(sess.run(b**2))\n", "print(sess.run(hello))\n", "# ANother way to do the same above\n", "print((a*2).eval())\n", "\n", "sess.close() # Because it is an interactive session we have to close it" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## PCA example" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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U9fHWz/dhJdAX0TsB/EBVf5b1QBJ6G4CnVXVOVZsAHgDwWxmPKRFV/ZKqvklV\nr8FKSuBHWY8phZ+JyK8CQOvP5zMeTyEUMrCLyFoRebnzdwBvx8pX0EJR1f8F8GxrsxJgJUf9wwyH\nlMbNKGgapuUkgKtFZEREBCv/FoW6kO0QkVe1/tyIlQt2Rf53+TaAD7b+/kEADwY8lloKuUBJRF6L\n85UXwwC+rqqfznBIiYnIFgBfBLAGwE8AfEhVT2c7qnhaudxnAbxWVc9kPZ6kROQuADdhJXUxC+CP\nVPWlbEcVn4j8C4BXAmgC+BNV/V7GQ4pERO4F8NtY6YT4MwB3ApgG8A0AG7Fy8r1RVd0XWHPF5328\nAOBvAKwHMA/giKru6NkYihjYiYjIXyFTMURE5I+BnYjIMgzsRESWYWAnIrIMAzsRkWUY2ImILMPA\nTkRkGQZ2IiLL/D+VQKt445bCVgAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Load data\n", "iris = datasets.load_iris()\n", "X = iris.data\n", "y = iris.target\n", "target_names = iris.target_names\n", "\n", "# Reset the graph, used mostly when working in Jupyter notebook environments\n", "tf.reset_default_graph()\n", "\n", "X_input = tf.placeholder(shape=[None,None], dtype=tf.float64)\n", "\n", "# Call the SVD tensorflow function\n", "s,u,v = tf.svd(X_input)\n", "# Compute the 2-D projection\n", "X_proj = tf.matmul(X,v[:,0:2])\n", "\n", "init_vars = tf.global_variables_initializer()\n", "\n", "with tf.Session() as sess:\n", " init_vars.run()\n", " # the returned variables are numpy arrays\n", " s_c, u_c, v_c, x_proj_c = sess.run([s,u,v,X_proj], feed_dict={X_input : X })\n", " \n", " #print(u_c,s_c,v_c)\n", " #print(x_proj_c)\n", " plt.figure()\n", " plt.scatter(x_proj_c[:,0],x_proj_c[:,1])\n", " plt.show()\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Autoencoder for Dimensionality Reduction" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reconstruction loss at 0 is 25.2114\n", "Reconstruction loss at 50 is 1.3328\n", "Reconstruction loss at 100 is 0.0345587\n", "Reconstruction loss at 150 is 0.0288743\n", "Reconstruction loss at 200 is 0.0268748\n", "Reconstruction loss at 250 is 0.0254899\n", "Reconstruction loss at 300 is 0.0245015\n", "Reconstruction loss at 350 is 0.0237531\n", "Reconstruction loss at 400 is 0.0231364\n", "Reconstruction loss at 450 is 0.0225895\n", "Reconstruction loss at 500 is 0.0220826\n", "Reconstruction loss at 550 is 0.0216034\n", "Reconstruction loss at 600 is 0.021148\n", "Reconstruction loss at 650 is 0.0207156\n", "Reconstruction loss at 700 is 0.0203063\n", "Reconstruction loss at 750 is 0.01992\n", "Reconstruction loss at 800 is 0.0195565\n", "Reconstruction loss at 850 is 0.0192151\n", "Reconstruction loss at 900 is 0.0188947\n", "Reconstruction loss at 950 is 0.0185943\n", "Reconstruction loss at 1000 is 0.0183125\n", "Reconstruction loss at 1050 is 0.018048\n", "Reconstruction loss at 1100 is 0.0177992\n", "Reconstruction loss at 1150 is 0.0175648\n", "Reconstruction loss at 1200 is 0.0173433\n", "Reconstruction loss at 1250 is 0.0171334\n", "Reconstruction loss at 1300 is 0.0169339\n", "Reconstruction loss at 1350 is 0.0167437\n", "Reconstruction loss at 1400 is 0.0165616\n", "Reconstruction loss at 1450 is 0.0163868\n", "Reconstruction loss at 1500 is 0.0162184\n", "Reconstruction loss at 1550 is 0.0160558\n", "Reconstruction loss at 1600 is 0.0158983\n", "Reconstruction loss at 1650 is 0.0157454\n", "Reconstruction loss at 1700 is 0.0155968\n", "Reconstruction loss at 1750 is 0.0154519\n", "Reconstruction loss at 1800 is 0.0153106\n", "Reconstruction loss at 1850 is 0.0151725\n", "Reconstruction loss at 1900 is 0.0150375\n", "Reconstruction loss at 1950 is 0.0149053\n", "Reconstruction loss at 2000 is 0.014776\n", "Reconstruction loss at 2050 is 0.0146492\n", "Reconstruction loss at 2100 is 0.0145251\n", "Reconstruction loss at 2150 is 0.0144033\n", "Reconstruction loss at 2200 is 0.0142841\n", "Reconstruction loss at 2250 is 0.0141671\n", "Reconstruction loss at 2300 is 0.0140525\n", "Reconstruction loss at 2350 is 0.0139401\n", "Reconstruction loss at 2400 is 0.0138299\n", "Reconstruction loss at 2450 is 0.0137219\n", "Reconstruction loss at 2500 is 0.0136435\n", "Reconstruction loss at 2550 is 0.0135124\n", "Reconstruction loss at 2600 is 0.0134107\n", "Reconstruction loss at 2650 is 0.0133111\n", "Reconstruction loss at 2700 is 0.0132135\n", "Reconstruction loss at 2750 is 0.013119\n", "Reconstruction loss at 2800 is 0.0130241\n", "Reconstruction loss at 2850 is 0.0129323\n", "Reconstruction loss at 2900 is 0.0128423\n", "Reconstruction loss at 2950 is 0.0127543\n", "Reconstruction loss at 3000 is 0.0126679\n", "Reconstruction loss at 3050 is 0.0125833\n", "Reconstruction loss at 3100 is 0.0125004\n", "Reconstruction loss at 3150 is 0.0124192\n", "Reconstruction loss at 3200 is 0.0123398\n", "Reconstruction loss at 3250 is 0.0122689\n", "Reconstruction loss at 3300 is 0.0121857\n", "Reconstruction loss at 3350 is 0.012111\n", "Reconstruction loss at 3400 is 0.0120379\n", "Reconstruction loss at 3450 is 0.0119664\n", "Reconstruction loss at 3500 is 0.0118972\n", "Reconstruction loss at 3550 is 0.0118278\n", "Reconstruction loss at 3600 is 0.0117608\n", "Reconstruction loss at 3650 is 0.0116952\n", "Reconstruction loss at 3700 is 0.0116505\n", "Reconstruction loss at 3750 is 0.0115687\n", "Reconstruction loss at 3800 is 0.0115072\n", "Reconstruction loss at 3850 is 0.0114475\n", "Reconstruction loss at 3900 is 0.0113891\n", "Reconstruction loss at 3950 is 0.0113681\n", "[[ 7.48546743 0.01751965]\n", " [ 6.82744837 0.16693634]\n", " [ 6.82480145 0.06441253]\n", " [ 6.55144453 0.34456939]\n", " [ 7.46499872 0.02484006]\n", " [ 8.06461525 0.24184352]\n", " [ 6.86196232 0.18442756]\n", " [ 7.25040102 0.18051177]\n", " [ 6.19617891 0.31906027]\n", " [ 6.86955023 0.250063 ]\n", " [ 7.94935656 0.01645416]\n", " [ 6.99486589 0.3508243 ]\n", " [ 6.711061 0.17872638]\n", " [ 6.28281498 -0.03989643]\n", " [ 8.76317978 -0.49909621]\n", " [ 8.90783596 -0.16847605]\n", " [ 8.21777248 -0.22681433]\n", " [ 7.49323225 0.03230351]\n", " [ 8.29456425 0.16659147]\n", " [ 7.71940565 0.09170669]\n", " [ 7.60831404 0.30854422]\n", " [ 7.63901663 0.1257444 ]\n", " [ 7.1836648 -0.33752126]\n", " [ 7.2175827 0.45187217]\n", " [ 6.87999821 0.70231742]\n", " [ 6.85949278 0.37469119]\n", " [ 7.22764015 0.3272441 ]\n", " [ 7.55580091 0.10810989]\n", " [ 7.50593519 0.01019925]\n", " [ 6.70993376 0.41590589]\n", " [ 6.73040152 0.40858561]\n", " [ 7.70042086 0.10378319]\n", " [ 8.07696438 -0.02219623]\n", " [ 8.53704166 -0.22355276]\n", " [ 6.86955023 0.250063 ]\n", " [ 7.18895912 -0.13247389]\n", " [ 7.95824862 -0.20594114]\n", " [ 6.86955023 0.250063 ]\n", " [ 6.32262325 0.18264216]\n", " [ 7.35902357 0.15393776]\n", " [ 7.42289782 -0.05828685]\n", " [ 5.82192707 0.30562812]\n", " [ 6.49893284 0.1441347 ]\n", " [ 7.33132219 0.33755833]\n", " [ 7.57401323 0.5751484 ]\n", " [ 6.7265892 0.20829421]\n", " [ 7.67335272 0.19408733]\n", " [ 6.67788839 0.20815116]\n", " [ 7.840734 0.0430283 ]\n", " [ 7.20053434 0.08260125]\n", " [ 8.11446285 3.61420584]\n", " [ 7.54706907 3.55410552]\n", " [ 7.84887075 3.90914655]\n", " [ 5.95198774 3.35116529]\n", " [ 7.26478338 3.72171068]\n", " [ 6.4185605 3.78757095]\n", " [ 7.45778608 3.81053853]\n", " [ 5.6331377 2.62685347]\n", " [ 7.446033 3.64631462]\n", " [ 6.02479172 3.25149298]\n", " [ 5.31256294 2.91162324]\n", " [ 6.94251251 3.37399006]\n", " [ 6.38365507 3.19319725]\n", " [ 6.87239361 3.91113377]\n", " [ 6.74269629 2.74041104]\n", " [ 7.81530809 3.36168909]\n", " [ 6.50177622 3.80520558]\n", " [ 6.56889391 3.26724076]\n", " [ 6.44827604 3.79979086]\n", " [ 6.25968122 3.13935208]\n", " [ 6.91237879 4.08282089]\n", " [ 7.04449892 3.09545231]\n", " [ 6.66820574 4.18411303]\n", " [ 6.76871061 3.9008193 ]\n", " [ 7.34365463 3.34796977]\n", " [ 7.61853075 3.40751696]\n", " [ 7.50630999 3.86153364]\n", " [ 7.52071047 4.12828064]\n", " [ 6.84811354 3.71816301]\n", " [ 6.60185194 2.61008263]\n", " [ 6.10119295 3.0680151 ]\n", " [ 6.13171816 2.93606663]\n", " [ 6.66100073 3.06248021]\n", " [ 6.44983244 4.47444057]\n", " [ 6.28453016 3.85835361]\n", " [ 7.29665136 3.63667822]\n", " [ 7.70820475 3.72796583]\n", " [ 6.66781425 3.60723066]\n", " [ 6.63940477 3.30697942]\n", " [ 6.12829733 3.31265783]\n", " [ 6.05553102 3.74727821]\n", " [ 6.99883747 3.77471542]\n", " [ 6.53455687 3.19889808]\n", " [ 5.65360546 2.6195333 ]\n", " [ 6.33665133 3.48190522]\n", " [ 6.70197392 3.38278627]\n", " [ 6.62158346 3.41682363]\n", " [ 7.12640953 3.40111804]\n", " [ 6.0611701 2.2177422 ]\n", " [ 6.57171774 3.31891298]\n", " [ 7.02990341 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import tensorflow as tf \n", "\n", "# Load data\n", "iris = datasets.load_iris()\n", "X_data_train = iris.data[0:50]\n", "X_data_test = iris.data[50:]\n", "X_data_total = iris.data\n", "Y_data_train = iris.target[0:50]\n", "Y_data_test = iris.target[50:]\n", "target_names = iris.target_names\n", "\n", "n_inputs = 4 # 3D inputs \n", "n_hidden = 2 # 2D codings \n", "n_outputs = n_inputs \n", "learning_rate = 0.01\n", "\n", "X = tf.placeholder( tf.float32, shape =[ None, n_inputs]) \n", "hidden = tf.layers.dense( X, n_hidden) \n", "outputs = tf.layers.dense( hidden, n_outputs) \n", "reconstruction_loss = tf.reduce_mean( tf.square( outputs - X)) # MSE\n", "\n", "optimizer = tf.train.AdamOptimizer( learning_rate) \n", "training_op = optimizer.minimize( reconstruction_loss) \n", "\n", "init = tf.global_variables_initializer()\n", "\n", "\n", "X_train, X_test = X_data_train, X_data_test # load the dataset \n", "n_iterations = 4000 \n", "codings = hidden # the output of the hidden layer provides the codings \n", "with tf.Session() as sess:\n", " init.run() \n", " for iteration in range( n_iterations): \n", " #training_op.run( feed_dict ={ X: X_train}) # no labels (unsupervised) \n", " t, rc = sess.run([training_op,reconstruction_loss], feed_dict = {X : X_train})\n", " \n", " if((iteration%50) == 0):\n", " print(\"Reconstruction loss at \",iteration,\" is \",rc)\n", " \n", " codings_val = codings.eval( feed_dict ={ X: X_data_total})\n", "\n", "print(codings_val)\n", "plt.figure()\n", "plt.scatter(codings_val[:,0],codings_val[:,1])\n", "plt.show()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## K-Means TensorFlow example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Clustering is an exploratory data analysis technique that allows us to organize a pile of information into meaningful subgroups (clusters) without having any prior knowledge of their group memberships. \n", "* Each cluster that may arise during the analysis defines a group of objects that share a certain degree of similarity but are more dissimilar to objects in other clusters, which is why clustering is also sometimes called \"unsupervised classification.\" \n", "* Clustering is a great technique for structuring information and deriving meaningful relationships among data\n", "* For example, it allows marketers to discover customer groups based on their interests in order to develop distinct marketing programs.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## First example\n", "\n", "* Height and Weight data of size 25000\n", "* Data from http://socr.ucla.edu/docs/resources/SOCR_Data/SOCR_Data_Dinov_020108_HeightsWeights.html\n", "* Goal is to find discrete T shirt sizes from this distribution, since there are no visible clusters\n", "* Cluster each height, weight combination into one of four classes corresponding to sizes S,M,L,XL" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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VThBKkdKU8v6MiBYQ0Zmapu3UNO02IhpPRKf/T977cyK65X9epLUEsYZ1RPQd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M47k8Pq5s2jLU5VJq2oAD7+OPfxRKMUe5g0ieNyuxklMWoUcNll9vfZFX8FGPJnJZ507bXMBd6c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\n2/lvap8uV/qz3G6R6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wS2YgVRx47Y6XM64alxOqFsN2sWrtW/f6hUalYW0Z9/Eh0+jF29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\nkzhpYknxhhmf56pVEy9EYLWiGf0svvEG0a5dkY8TjT/+2GxIW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3FFIerTB9cPJ/AQ7WfK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11FStztWtm/5+2WXW+x61aAHs2AF06FB+35uUFK2SFrxhKaDPc8st2oZQJSWR7X2zdKn1Pk1A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\ntcvr1d9TUjT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0UrUThFKkNKW8PyOiBUR0lqZpOzVNu42IxhPRGf+T9/6ciG75nxdpLUGsYR0R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+2mO3Osl3afL75VSXd7+/ZbHuv3J2eFdzg4Brud1xs4EHj5ZWDOHLqliVCe+Hy0/LjdbD//DPz6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\nb79V3LiOF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WoiknG4eURkgtDacv3hzw6h1SVNRH6pmGGWiAFi/B+hGW//f/bOO86pKm3A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\n/53mvzfnQsQFRIWIyWqiX+V+9KjQI0PsTiuL2iyi34Z+rLm0BpV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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import tensorflow as tf\n", "import pandas as pd\n", "from matplotlib import pyplot as plt\n", "from matplotlib.colors import ListedColormap\n", "import time\n", "\n", "# Based on work from https://github.com/sentimos/kmeans/blob/master/KMeans.ipynb\n", "\n", "def ScatterPlot(X, Y, assignments=None, centers=None):\n", " if assignments is None:\n", " assignments = [0] * len(X)\n", " fig = plt.figure(figsize=(14,8))\n", " cmap = ListedColormap(['red', 'green', 'blue', 'magenta']) # Colors are assigned based on the position of the \n", " # cluster in the list 'clusters'\n", " plt.scatter(X, Y, c=assignments, cmap=cmap)\n", " if centers is not None:\n", " plt.scatter(centers[:, 0], centers[:, 1], c=range(len(centers)), \n", " marker='+', s=800) \n", " plt.xlabel('Height (in)')\n", " plt.ylabel('Weight (lbs)')\n", " plt.show()\n", "\n", "\n", "def input_fn():\n", " return tf.constant(data.as_matrix(),\n", " tf.float32, data.shape), None\n", "\n", "# Data from http://socr.ucla.edu/docs/resources/SOCR_Data/SOCR_Data_Dinov_020108_HeightsWeights.html\n", "data = pd.read_csv('untitled.txt',header=None,sep='\\t',index_col=0)\n", "data.columns = ['Height','Weight']\n", "\n", "ScatterPlot(data.Height, data.Weight)\n", "\n", "tf.logging.set_verbosity(tf.logging.ERROR)\n", "kmeans = tf.contrib.learn.KMeansClustering(\n", " num_clusters=4, relative_tolerance=0.0001)\n", "\n", "kmeans.fit(input_fn=input_fn)\n", "\n", "clusters = kmeans.clusters() # Returns cluster centers as 2D coordinates\n", "assignments = list(kmeans.predict_cluster_idx(input_fn=input_fn))\n", "\n", "ScatterPlot(data.Height, data.Weight, assignments, clusters)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, 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