{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "

Unsupervied Learning

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In this notebook we will conduct unsupervied learning for Iris datasets

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Data from Fisher, 1936

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I used, modified code and instruction in Udacity ML nanodegree

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Things we will cover: data exploration, data preprocessing, PCA, k-means, t-SNE

" ] }, { "cell_type": "code", "execution_count": 131, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "naruto_question.jpg sasuke.jpg sasuke_preprocessing.jpg\r\n" ] } ], "source": [ "ls images" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

What is Unsupervised Learning?

\n", "\n", "\"Naruto\"

Modified image from Naruto uzumaki Pinterest.

\n", "

It is a way of extracting useful knowlege from the data \"without any label\" or a way of transforming the data into meaningful format.
\n", " The emphasis is \"without any label\". This contrasts with Supervised Learning where we will predict the label on the testing sets from the training sets with the labels.

" ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [], "source": [ "# Import necessay libraries to use in the following sections\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "sns.set();\n", "%matplotlib inline\n", "import sklearn" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# we dont' have a header(feature names), we set header:= None\n", "iris_data = pd.read_csv(\"iris.data.csv\", header=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Data Exploration

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In this section, we will split the data into trainining and test set; then we will explore the dataset.

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The dataset contains 150 samples, each with five attributes. We will drop the class name variable and conduct unsupervised learning.

" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Num of row is: 150, num of cols is 5\n" ] }, { "data": { "text/html": [ "
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01234
05.13.51.40.2Iris-setosa
14.93.01.40.2Iris-setosa
24.73.21.30.2Iris-setosa
34.63.11.50.2Iris-setosa
45.03.61.40.2Iris-setosa
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" ], "text/plain": [ " 0 1 2 3 4\n", "0 5.1 3.5 1.4 0.2 Iris-setosa\n", "1 4.9 3.0 1.4 0.2 Iris-setosa\n", "2 4.7 3.2 1.3 0.2 Iris-setosa\n", "3 4.6 3.1 1.5 0.2 Iris-setosa\n", "4 5.0 3.6 1.4 0.2 Iris-setosa" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"Num of row is: {0}, num of cols is {1}\".format(iris_data.shape[0], iris_data.shape[1]))\n", "iris_data.head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# we change the features of the data to explicit names\n", "\n", "iris_data.columns = [\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\", \"class\"]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sepal_lengthsepal_widthpetal_lengthpetal_widthclass
05.13.51.40.2Iris-setosa
14.93.01.40.2Iris-setosa
24.73.21.30.2Iris-setosa
34.63.11.50.2Iris-setosa
45.03.61.40.2Iris-setosa
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" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width class\n", "0 5.1 3.5 1.4 0.2 Iris-setosa\n", "1 4.9 3.0 1.4 0.2 Iris-setosa\n", "2 4.7 3.2 1.3 0.2 Iris-setosa\n", "3 4.6 3.1 1.5 0.2 Iris-setosa\n", "4 5.0 3.6 1.4 0.2 Iris-setosa" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iris_data.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "X_data = iris_data[[\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"]]\n", "y_data = iris_data[\"class\"]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Iris-setosa\n", "1 Iris-setosa\n", "2 Iris-setosa\n", "3 Iris-setosa\n", "4 Iris-setosa\n", "Name: class, dtype: object" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_data.head()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 0 0 0 0 0 0 0 0 0]\n", "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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": [ "# encoding class labels\n", "from sklearn.preprocessing import LabelEncoder\n", "le = LabelEncoder()\n", "y_data_en = le.fit_transform(y_data.values)\n", "print(y_data_en[:10])\n", "print(y_data_en)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# print the encoding labels\n", "le.classes_" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# By default, train and test set will be splitted into 0.75% and 0.25% of original dataset\n", "# By default, dataset is shuffled before the split\n", "# set random_state in order for later replication(we can replicate the result this way since\n", "# same split will occur if we use the same random_state number)\n", "# we will use whole datasets as training data and use split to just shuffle the data\n", "\n", "from sklearn.model_selection import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X_data, y_data_en, \n", " test_size = 0, random_state=7)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X_train and X_test shape : (150, 4) vs (0, 4)\n", "y_train and y_test shape : (150,) vs (0,)\n" ] } ], "source": [ "print(\"X_train and X_test shape : {0} vs {1}\".format(X_train.shape, X_test.shape) )\n", "print(\"y_train and y_test shape : {0} vs {1}\".format(y_train.shape, y_test.shape))" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2 1 0 1 2 0 1 1 0 1 1 1 0 2 0 1 2 2 0 0]\n" ] } ], "source": [ "# check the random order of data (check whether data was shuffled or not)\n", "print(y_train[:20])" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sepal_lengthsepal_widthpetal_lengthpetal_width
count150.000000150.000000150.000000150.000000
mean5.8433333.0540003.7586671.198667
std0.8280660.4335941.7644200.763161
min4.3000002.0000001.0000000.100000
25%5.1000002.8000001.6000000.300000
50%5.8000003.0000004.3500001.300000
75%6.4000003.3000005.1000001.800000
max7.9000004.4000006.9000002.500000
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" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width\n", "count 150.000000 150.000000 150.000000 150.000000\n", "mean 5.843333 3.054000 3.758667 1.198667\n", "std 0.828066 0.433594 1.764420 0.763161\n", "min 4.300000 2.000000 1.000000 0.100000\n", "25% 5.100000 2.800000 1.600000 0.300000\n", "50% 5.800000 3.000000 4.350000 1.300000\n", "75% 6.400000 3.300000 5.100000 1.800000\n", "max 7.900000 4.400000 6.900000 2.500000" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# inspect the numerical reprensentation of each feature\n", "X_train.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Selecting samples

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we will select three samples to explore them later. We will choose them so that they \n", " is distinct (each feature value are different) from each other

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we will choose three examples as follows:
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    \n", "
  1. sample_1 = septal_length is maximum in the data
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  3. sample_2 = sepal length is the closest to the mean value
  4. \n", "
  5. sample_3 = petal length is the closest to the mean value
  6. \n", "
\n", "

" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

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sepal_lengthsepal_widthpetal_lengthpetal_width
1317.93.86.42.0
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" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width\n", "131 7.9 3.8 6.4 2.0" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Choose a sample whose septal_length is maximum\n", "X_train.loc[X_train[\"sepal_length\"] >= 7.8]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "sample_1 = X_train.loc[X_train[\"sepal_length\"] >= 7.8]" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "sepal_length 5.8\n", "sepal_width 2.7\n", "petal_length 3.9\n", "petal_width 1.2\n", "Name: 82, dtype: float64" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Choose a sample whose sepal length is the closest to the mean value\n", "X_train.loc[(abs(X_train[\"sepal_length\"] - X_train[\"sepal_length\"].mean())).argmin()]" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "sample_2 = X_train.loc[82]" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "sepal_length 5.5\n", "sepal_width 2.4\n", "petal_length 3.8\n", "petal_width 1.1\n", "Name: 80, dtype: float64" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Choose a sample whose petal length is the closest to the mean value\n", "X_train.loc[(abs(X_train[\"petal_length\"] - X_train[\"petal_length\"].mean())).argmin()]" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "sample_3 = X_train.loc[80]" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "# create a holder for the three samples chosen\n", "samples = [sample_1, sample_2, sample_3]" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Chosen samples of iris dataset\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
sepal_lengthsepal_widthpetal_lengthpetal_width
07.93.86.42.0
15.82.73.91.2
25.52.43.81.1
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" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width\n", "0 7.9 3.8 6.4 2.0\n", "1 5.8 2.7 3.9 1.2\n", "2 5.5 2.4 3.8 1.1" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "indices = [131, 82, 80]\n", "samples = pd.DataFrame(X_train.loc[indices], columns=X_train.keys()).reset_index(drop=True)\n", "print(\"Chosen samples of iris dataset\")\n", "samples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Feature dependence

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One thing we can find from data is that some feature is predictive of others. In other words, is it possible to predict the sepal length using the other 3 features? (This is one example. Target variable does not have to be a sepal length. We can choose any feature to be inspected.)

\n", "

We will choose \"sepal length\" as a target class and use the other featuers for supervised learning models.

" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.836542685606\n" ] } ], "source": [ "# Copy the X_train data and split them into a target(a dependent variable) \n", "# and independet variables\n", "pseudo_target = X_train['sepal_length']\n", "pseudo_data = X_train.drop(['sepal_length'], axis=1)\n", "\n", "# Split the data into training and testing sets: 0.75 goes to training set.\n", "X_pseudo_train, X_pseudo_test, y_pseudo_train, y_pseudo_test\\\n", " = train_test_split(pseudo_data, pseudo_target, random_state=7)\n", " \n", "# Use a decision tree regressorn to fit and infer the target values \n", "from sklearn.tree import DecisionTreeRegressor\n", "regressor = DecisionTreeRegressor(random_state=7)\n", "regressor.fit(X_pseudo_train, y_pseudo_train)\n", "y_pseduo_pred = regressor.predict(X_pseudo_test)\n", "from sklearn.metrics import r2_score\n", "score = r2_score(y_pseudo_test, y_pseduo_pred)\n", "print(score)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

The coefficient of determination, R2 is 0.83

\n", "

So about 83% of the variance of target variable is explained by the three variables.\n", "It is clear that there is a dependency of this target variable(petal length) on the three variables. We could drop the \"petal length\" feature for reducing the dimentionality.(thus\n", "reducing the run-time cost)

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Visuzlize Feature Distributions

\n", "\n", "

In order to get sense of data, we will plot the feature distribution.\n", "In this way, we can understand not only the each feature's distribution but also the\n", "correlation between each feature.

\n", "\n", "\n", "

\"sakura\"

\n", "

Image from Fandom

" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ]], dtype=object)" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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rz4E3gT+ahDZOqQPnWvApKjtX54kOzQx12/oFJMXree9446jx0cL84FMUalv7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8nR2rssZdsi3MNuDxCOSkxXH3zsKwXLb6B92+5FQyX8ezcXn6mE4gXHLT4lDI5STFa7hvzxIM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\nTZg3805GUpTfvyUhhBAT4wbvEO6ntp8e1fumxJyastpWXt9TwbpFs1g4O2n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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pd.plotting.scatter_matrix(X_train, alpha=0.9, figsize=(14, 8), diagonal=\"kde\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "

What we can infer from the data plot above

\n", "\n", "

Each feature distribution:


\n", "

Sepal length and petal length are almost following normal distribution (Sepal length\n", "does not trace the exact bell curve though).\n", "Petal length and petal width trace bimodal distribution(meaning that there are two groups or two most frequent samples)

\n", "\n", "\n", "

Correlation:

\n", "

Positive correlation can be seen between 'sepal_length' and 'petal_length', between 'petal_length' and 'petal_width'. 'Septal_length' and 'petal_width' are somewhat correlated positively but it is not clear.

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can confirm the correlation we inferred above by using the heatmap." ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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GUzzKxkSTmpae/drnP/Y3UqPa+dx+Q0fufXIUoydMpUHdWpQvF8cZcXG0adEU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\nESkHLFXVv+UqUx54D1isqsP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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.heatmap(X_train.corr(), annot=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Our inference is confirmed.

\n", "\n", "

Positive correlation ( in decreasing order ) for 3 distributions:\n", "

    \n", "
  1. 0.96 for \"petal_length\" and \"petal_width\"
  2. \n", "
  3. 0.87 for \"sepal_length\" and \"petal_length\"
  4. \n", "
  5. 0.82 for \"petal_width\" and \"sepal_length\"
  6. \n", "

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Data Preprocessig

\n", "

We will transform the data into an appropriate distribution.
\n", "We will also detect outliers and deal with them.

\n", "\n", "\"sasuke\"\n", "

Modified image from Sasuke's Ninja Way

" ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sasuke.jpg sasuke_preprocessing.jpg\r\n" ] } ], "source": [ "ls images/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Some machine-leanrning algorithms are sensetive to the data distribution. It is often preferable to pre-scale the datasets

\n", "

We will scale dataset (StandardScaler in this case) so that each feature will have zero mean and unit variance.(This does not necessarily mean that each feature will trace Gaussian Distribution)

" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean of value for each feature before scaling\n", "sepal_length 5.843333\n", "sepal_width 3.054000\n", "petal_length 3.758667\n", "petal_width 1.198667\n", "dtype: float64\n", "Variance of value for each feature before scaling\n", "sepal_length 0.685694\n", "sepal_width 0.188004\n", "petal_length 3.113179\n", "petal_width 0.582414\n", "dtype: float64\n", "Mean of value for each feature after scaling\n", "[ -4.45569507e-16 -6.48370246e-16 2.75335310e-16 -1.34707060e-16]\n", "Vaiance of value for each feature after scaling\n", "[ 1. 1. 1. 1.]\n" ] } ], "source": [ "# Using StandardScaler to make each distribution have zero mean and unit variance\n", "from sklearn.preprocessing import StandardScaler\n", "scaler = StandardScaler()\n", "scaler.fit(X_train)\n", "X_train_scaled = scaler.transform(X_train)\n", "samples_scaled = scaler.transform(samples)\n", "print(\"Mean of value for each feature before scaling\\n{0}\".format(X_train.mean(axis=0)))\n", "print(\"Variance of value for each feature before scaling\\n{0}\".format(X_train.var(axis=0)))\n", "print(\"Mean of value for each feature after scaling\\n{0}\".format(X_train_scaled.mean(axis=0)))\n", "print(\"Vaiance of value for each feature after scaling\\n{0}\".format(X_train_scaled.var(axis=0)))" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "X_train_scaled_df = pd.DataFrame({\"sepal_length\": X_train_scaled[:,0], \n", " \"sepal_width\": X_train_scaled[:, 1],\n", " \"petal_length\": X_train_scaled[:, 2], \n", " \"petal_width\": X_train_scaled[:, 3]})" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ]], dtype=object)" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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jq5OLHpMkCU03KGg6mm7UvKBsPnlTnPrid1/tcQseHV03FmXS/vqDu0wGU4QS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\nz8WQh2LJRzHkIV0+TNPkzwd7+dEfTuALRmmpL+f269ZyycamvHS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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pd.plotting.scatter_matrix(X_train_scaled_df, figsize=(14, 8), diagonal=\"kde\")" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
petal_lengthpetal_widthsepal_lengthsepal_width
01.5020131.0535372.4920191.726266
10.0803700.001753-0.052506-0.819166
20.023504-0.129720-0.416010-1.513375
\n", "
" ], "text/plain": [ " petal_length petal_width sepal_length sepal_width\n", "0 1.502013 1.053537 2.492019 1.726266\n", "1 0.080370 0.001753 -0.052506 -0.819166\n", "2 0.023504 -0.129720 -0.416010 -1.513375" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "samples_scaled_df = pd.DataFrame({\"sepal_length\": samples_scaled[:,0], \n", " \"sepal_width\": samples_scaled[:, 1],\n", " \"petal_length\": samples_scaled[:, 2], \n", " \"petal_width\": samples_scaled[:, 3]})\n", "samples_scaled_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Outlier detection

\n", "

We will detect outliers and deal with them

\n", "

We will use Turkey's Method for identifying outliers for identifying outliers

" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data points considered outliers for the feature petal_length\n", "Data points considered outliers for the feature petal_width\n", "Data points considered outliers for the feature sepal_length\n", "Data points considered outliers for the feature sepal_width\n" ] } ], "source": [ "outliers = []\n", "\n", "for feature in X_train_scaled_df.keys():\n", " q1 = np.percentile(X_train_scaled_df[feature], 25, axis=0)\n", " q3 = np.percentile(X_train_scaled_df[feature], 75, axis=0)\n", " step = 1.5 * float(q3-q1)\n", " print(\"Data points considered outliers for the feature {0}\".format(feature))\n", " \n", " X_train_scaled_df[~((X_train_scaled_df[feature] >= q1 - step)&(X_train_scaled_df[feature] <= q3+step))]" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(data=X_train_scaled_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Fowllowing Turkey's Method for identifying outliers, \n", "we do not have outliers in this datasets

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Feature transformation

\n", "

We will see the underlying structure of the data

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

PCA for reducing dimesionality

" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "from sklearn.decomposition import PCA\n", "pca = PCA(random_state=7)\n", "pca.fit(X_train_scaled)\n", "pca_X_train = pca.transform(X_train_scaled)\n", "pca_samples = pca.transform(samples_scaled)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "def pca_components(data, pca):\n", " '''\n", " Visualize the each components of pca fitted on data.\n", " It shows the feature weights and explained variance.\n", " '''\n", " \n", " # Extract index of components (count the number of components)\n", " dimensions = [\"Principal Component {}\".format(i) for i in range(1, len(pca.components_)+1)]\n", " # Create a dataframe: row is each dimension(component) and column is keys(features)\n", " components = pd.DataFrame(np.round(pca.components_,4), columns=data.keys())\n", " components.index = dimensions\n", " \n", " # Variance explained by each dimension(component)\n", " ratios = pca.explained_variance_ratio_.reshape(len(pca.components_), 1)\n", " # Create a dataframe for variance explained for each dimension\n", " # row is each dimension and column is variance explained\n", " variance_ratios = pd.DataFrame(np.round(ratios, 4), columns=[\"Explained Variance\"])\n", " \n", " fig, ax = plt.subplots(figsize=(15, 10))\n", " \n", " # Visualize the feature weights\n", " components.plot(ax = ax, kind = 'bar')\n", " ax.set_ylabel(\"Feature Weights\")\n", " ax.set_xticklabels(dimensions, rotation=0)\n", " \n", " # Display the explained variance for each dimension above the plot\n", " for i, var in enumerate(pca.explained_variance_ratio_):\n", " ax.text(i-0.40, ax.get_ylim()[1] + 0.05, \n", " \"Explained Vairance\\n {:.4f}\".format(var))\n", " " ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "image/png": 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f/vSyfOhDe+WJJx7P5ptvlXHjxidJpky5NN/97rcyduzYHHbYUZk+fXqOOOJT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\nQIRAAACAgQiBAAAAAxECAQAABvL/Xd8TyqJEEF4AAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pca_components(X_train_scaled_df, pca)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Interpretation of Principal Components above

\n", "

Important to Note:

Sign (+/-) of each weight will have meaning only if they are compared to other features.\n", " Sign can be flipped and sign itself does not indicate anything.
\n", " We will consider the feature weight larger than 0.5 in absolute value to have strong correlation.

\n", "\n", "
    \n", "
  1. \n", " First component: This component positively correlates with \"petal length\", \"sepal length\" and \"sepal_width\".\n", " This means two things: \n", "
      \n", "
    1. The three variables vary together
    2. \n", "
    3. First component can be viewed as the measure of qunatity of the \"petal length\", \"sepal length\" and \"sepal width\"
    4. \n", "
    \n", "

  2. \n", " \n", "
  3. \n", " Second component: This component has positive correlation with \"petal width\"\n", " This component can be viewed as the measure of quantity(length) of \"petal width\".\n", "
  4. \n", " \n", "
  5. \n", " Third component: This component strongly correlates with \"petal length\" and \"sepal width\" to some extent (If one feature increasing, the other one is decreasing). \n", " This component can be viwed as the measure of the quantity of \"petal length\" and \"sepal width\".\n", "
  6. \n", " \n", "
  7. \n", " Fourth component: This component positively correlates with \"sepal length\".\n", " This component can be viewed as the measure of the quantity of sepal length(how long sepal length is).\n", "
  8. \n", " \n", "
" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cumulative variance for each component [ 0.72770452 0.95800975 0.99484807 1. ]\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(range(4), np.cumsum(pca.explained_variance_ratio_))\n", "plt.title(\"Cumulative variance explained by PCA\")\n", "plt.xlabel(\"number of component\")\n", "plt.ylabel(\"Variance explained\")\n", "print(\"Cumulative variance for each component\", np.cumsum(pca.explained_variance_ratio_))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Dimensionality Reduction

\n", "

PCA allows us to reduce the dimensionality of the data -- \n", "this will result in the less computation cost. As a side effect, total variance in the data will also decrease.
We will use 2 components since it captures 95% of total vairance.

" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "# Apply PCA with two components\n", "pca = PCA(n_components=2, random_state=2)\n", "\n", "# Fit and transform the scaled data\n", "reduced_data = pca.fit_transform(X_train_scaled)\n", "# Fit and transform the sample data\n", "reduced_samples = pca.transform(samples_scaled)\n", "\n", "# Create a DataFrame for the reduced data\n", "reduced_data_df = pd.DataFrame(reduced_data, columns = [\"PCA 1\", \"PCA 2\"])" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PCA 1PCA 2
02.31612.6262
10.2360-0.7759
20.1215-1.5636
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" ], "text/plain": [ " PCA 1 PCA 2\n", "0 2.3161 2.6262\n", "1 0.2360 -0.7759\n", "2 0.1215 -1.5636" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(np.round(reduced_samples, 4), columns=[\"PCA 1\", \"PCA 2\"])" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "def biplot(original_data, reduced_data, pca):\n", " '''\n", " Create a biplot that shows a scatterplot of the reduced data \n", " and the projections of the original features.\n", " \n", " '''\n", " fig, ax = plt.subplots(figsize=(14, 8))\n", " # scatterplot of the reduced data\n", " ax.scatter(x=reduced_data.loc[:, \"PCA 1\"], y=reduced_data.loc[:, \"PCA 2\"], \n", " facecolors='b', edgecolors='b', s=70, alpha=0.5)\n", " \n", " feature_vectors = pca.components_.T\n", "\n", " # Scale factors to make the arrows easier to see\n", " arrow_size, text_pos = 3.0, 3.8\n", " \n", " # projections of the original features\n", " for i, v in enumerate(feature_vectors):\n", " ax.arrow(0, 0, arrow_size*v[0], arrow_size*v[1], \n", " head_width=0.2, head_length=0.2, linewidth=2, color='red')\n", " ax.text(v[0]*text_pos, v[1]*text_pos, original_data.columns[i], color='black', \n", " ha='center', va='center', fontsize=18)\n", " \n", " ax.set_xlabel(\"Principal Component 1\", fontsize=14)\n", " ax.set_ylabel(\"Principal Component 2\", fontsize=14)\n", " ax.set_title(\"PC plane with original feature projections.\", fontsize=16)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "image/png": 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jJCIiIpJKSoBERCIqXn0zNp3z0lwfIxEREZFUUQIkIhIRKhkZm+534jE+RiIi\nIiKpogRIRCRO5V/+GpvO+myxj5GIiIhIKigBEhGJU3vYxNh00X57+BiJiIiIpIISIBGRRqovuRwA\nEw5jVq/2ORoRERFJJiVAIiKN1EQSIIB+xxzpYyQiIiKSbEqAREQaCwTYOHYCANll/4LaWp8DEhER\nkWQJ+h2AJJfrQlmZYdGiLKqrDQUFLiUl9ZSWuhjjd3Qi3UfVfQ9SPHQzAHr/7mLWTZ3mc0QiIiKS\nDGoB6kFcF2bNCjJnTjbLlweorDQsXx5gzpxsZs8O4rp+RyjSjeTnU7/V1gD0+tt09AckIiLSMygB\n6kHKygyffRYg2KhdLxiExYsDlJWpCUikPdY891JsutcD9/kYiYiIiCSLEqAeZNGirCbJT1Qw6C0X\nkcSFNx8Sm+599eWtrCkiIiLdhRKgHqS6uvUWnpoatQCJtFfF3Fdj09mvvdrKmiIiItIdKAHqQQoK\nWh+jkJ+vMQwi7RXaZdfYdKFKYouIiHR7SoB6kJKSekKh5peFQt5yEWm/qnvuj01nffG5j5GIiIhI\nZykB6kFKS11GjAg3SYJCIRgxIkxpqVqARDpi49HHxqaLxuzjYyQiIiLSWUqAehBjYOLEEBMm1DFk\nSJjCQpchQ8KMH1/HxIkh3QdIpKOMoebXF3mTNdWYNRU+ByQiIiIdpRuh9jDGeC1BpaUt9IUTkQ6p\nvuo68u+ZCkC/E45hzfPzfI5IREREOkItQCIiicjKona/AwDI/ud7UFfnc0AiIiLSEUqAREQSVPXQ\n9Nh072uv8DESERER6SglQCIiCXJ79yE8sBiAXn/5M7gqLCIiItLdKAESEWmHihfnx6bzpj/iXyAi\nIiLSIb4WQbDWZgMPAUOBXGCK4zjP+hmTiEhrwlttHZvuc8mFbDjpFB+jERERkfbyuwXoBGC14zh7\nA2OBe3yOR0SkTWuefTE2nf3OWz5GIiIiIu3ldwL0D+CauMeq3Swiaa9u9z1j04UTx/kYiYiIiLSX\ncdNgEK+1tg/wLPCA4zh/a23dUKjeDQazuiYwEZGWPPggnHGGN71kCWyzjb/xiIiISDzT4gK/EyBr\n7ZbALOBex3Eeamv98vK1CQVcXNyH8vK1nQ1P0oSOZ8/T7Y+p61I8uB8A4QEDWL34S58D8le3P57S\nhI5pz6Lj2fPomLauuLhPiwmQ30UQBgPzgF87jvOqn7GIiLSLMdScfhb5D95PYPVqzNoq3D59/Y5K\nRCQtuC6UlRkWLcqiutpQUOBSUlJPaamLafG0VKRr+D0G6EqgCLjGWrsg8tPL55hERBJSPfmm2HTf\nU070MRIRkfThujBrVpA5c7JeNvLlAAAgAElEQVRZvjxAZaVh+fIAc+ZkM3t2ULdQE9/52gLkOM6F\nwIV+xiCdp6s8krGys6ndfU9y3nuHnDdeg1AIgr7+WxUR8V1ZmeGzzwJN/h0Gg7B4cYBhwwwjRyoL\nEv/43QIk3Zyu8kimq5o+MzZdMGWSb3GIiKSLRYuyWrwWFAx6y0X8pARIOqWtqzxlZWoCkp7N7VdI\nuKA3APn33o2yfhHJdNXVrX/319To3ED8pQRIOkVXeURgzatvxKZz//64j5GIiPivoKD1C0H5+bpQ\nJP5SAiSdoqs8IlA/bLvYdN/zz/YxEhER/5WU1BNq4db2oZC3XMRPSoB85rqwcKFhxowg99+fzYwZ\nQRYuNN2mF42u8oh41vzjmdh08MN/+hiJiIi/SktdRowIN0mCQiEYMSJMaanODcRfKlfko2gBgfgx\nNJWVhmXLAixZEmbixFDaV1ErKaln2bKmY4BAV3kks9Ttu39sumjcgZSvrPIxGhER/xgDEyeG2HZb\nQ1lZFjU1hvx8VYiV9KEEyEc9oUxkaanLkiVhFi9u+Dp0lUcy0dqbbqXPlb8DILD8W8JDtvA5IhER\nfxjjnSOUlrbQF07ER+oC56OeUEAgepVnwoQ6hgwJU1joMmRImPHj67pFC5ZIMm047azYdOGEg32M\nRERERFqiFiAf9ZQCArrKIxJhDOtP+BW9ZjxK1jdfQ3U1FBT4HZWIiIjEUQuQj1RAQKTnWXfLH2LT\nfc85zcdIREREpDlKgHykMpEiPVBODnU//gkAuS++APX6OxYREUknSoB8pDKRIj1T5cynY9P5t93s\nYyQiIiLSmBIgH6mAgEjP5PYfgBupcFJwx60+RyMiIiLxVATBZyogINIzVbz2Dv333g2A3GeeZuMR\nP/M5IhEREQG1AImIpES9HR6b7nvGyf4FIiIiIg0oARIRSZHKx/4emw4u/MTHSERERCRKCZCISIrU\nHnRobLrooH19jERERESilACJiKTQumuuj02bFSt8jERERERACZCISEqtP++C2HThkeN8jERERERA\nCZCISGoFAmz42dEABP/3Oaxf73NAIiIimU0JkIhIiq2dOi023efCc3yMRERERJQAiYikWl4eoUhZ\n7LzZT0M47HNAIiIimUsJkIhIF1jz1POx6V5/vNPHSERERDKbEiARkS7gDhoUm+5942QfIxEREcls\nSoBERLrID6++FZvOefEFHyMRERHJXEqARES6SH1JaWy630nH+hiJiIhI5lICJCLShSr/Mj02nbX4\nPz5GIiIikpmCfgcg3ZfrQlmZYdGiLKqrDQUFLiUl9ZSWuhiT+u1FuqPaw46ITfffd3fKV1b5GI2I\niEjmUQIkHeK6MGtWkM8+CxCMfIoqKw3LlgVYsiTMxImhVpOYzm4v0p1V/+5KCm69CQCzahXuwIE+\nRyQiIpI51AVOOqSszDRIXqKCQVi8OEBZWevZS2e3F+nOan7zu9h0v19M9DESEelq5eXlVFdXt3u7\niRPHscsuO7d7u/PPP5tBg/q2e7tUCIfDLFv2VezxzJmPMWhQX95++00fo5JMpARIOmTRoqwmyUtU\nMOgtT+X2It1aIMDGcYcBkP1pGWzc6HNAItIVXn11HnvuuQurV6/yO5Qut3ZtFWPHHsDMmY/5HYqI\nEiDpmOrq1ltoampaX97Z7UW6u6p7H4hN97n0Ih8jEZGu8tFHH1JZucbvMHxRUVHBJ5987HcYIoAS\nIOmgggK31eX5+a0v7+z2It1efj71Ww0FIG/mY97AOBEREUk5JUDSISUl9YRCzS8Lhbzlqdy+Oa4L\nCxcaZswIcv/92cyYEWThQqPzSklba557MTbd6/5pPkYiIi3ZZZedufjiX/PYY39l1KhShg7djPHj\nD+Ktt95osN4HH7zPUUcdwTbbDGGbbYZw9NFH8PHHH8aWn3/+2dx++y0AjBpVwsSJ42LLnntuNkcc\nMZZtt/0RW2wxgFGjSpg8+Ro2pqh77PLl33LeeWcyYsQ2bLllMQccMJonn3yiwTrnn382e+65C598\n8hFHHDGWrbcezI47bsuVV17K+vXrG6z7v/99zkknHct2223J8OFDufLKS5k+/REGDerLsmVf8fbb\nbzJqVAkAt99+S2x+VHn5Ss4553S2225Lhg3bgl/96ji++ebrlLx2EVACJB1UWuoyYkS4SRITCsGI\nEWFKS1vPOjq7fWPRqnJz5mSzfHmAykrD8uUB5szJZvbsoJIgSUvhzYfEpntfe6WPkYhIa15//TUu\nv/y3HHbYEVx22dWsWlXOMcccyTvvvAXAggXzmThxHGvXVnL55Vdx8cWX8u2333DEEWN57713ADjp\npFMYFxn7d8MNN3PRRZcAMGPGo5x22kn069ePa66ZzKRJU/jRj7Zk2rS7uPvuO5L+Wr7//jsOPfQA\n3nhjAaeffjaTJk2hf/8BnHvuGdxzz10N1l21qpxf/OJItttuB6ZM+T277bY7Dz54P7dGqlgCfPPN\n1xx22MF88MH7nHvu+Zx77gW88MLzTJlyXWyd7be33HDDzQCMG3cY06b9mQEDNlW/vOii86io+IFr\nrpnMsccex7x5cznxRN0sWlJHZbClQ4yBiRNDbLutoawsi5oaQ35+4vfx6ez2jbVVVW7YMMPIkcqC\nJP1UvDifokMPACB7/ivUHXCgzxGJSGPffPM1jzzyN8aNmwDA0Ucfyx57/B833HAdc+a8zKWXXsRP\nfrILzzwzl6wsr4jPaaedyQEH7MWVV/6O+fPfYtddf8qOO+7ECy88x9ixE9hqq60BuO++PzJq1G48\n+ujjmMiX3ymnnMGoUSU8//yzXHrpFUl9LTfeOJmNGzfwxhvvM3jwZpFYz+Lss0/j97+fwjHHHEdx\ncTEAa9as4aabbuX0088G4MQTT2b06F156qm/c911NwBei05lZSULFrzLDjvY2Puz556jYs85aNAg\nxo6dwDXXXMGOO+7E0Uc3TG722Wd//vrXx2OPq6urefzxGSxd+iVDh26T1NcvAmoBkk4wxmvJOeGE\nEGeeWccJJ4QYOTLx5KWz28dTVTnprkL/t+kkofDYn/kYiYi0ZPvtd4glPwADBw7k6KOP4eOPP+ST\nTz7iq6+WMnbsBNasWcPq1atZvXo1GzZs4OCDx/Lpp2UsX/5ti/tesOBdHn/8yVjyA17LS79+hR0q\nl92acDjM3Llz2H33vQgGs2Oxrl69mvHjD2fjxo28/vr8BtscfnjD/0s77bQz5eUrAXBdl7lzn2fM\nmINiyQ/A5psP4aijjkk4riOP/HmDxz/+8f8BsHLlyna9PpFEqQVIegRVlZPurOreB+h77hkAZP3v\nc+q3297niEQk3g47DG8yb9iwbXFdl7ff9rrBTZ58NZMnX93s9t9++w1DhmzR7LLs7Gz+9a9PmDXr\nST7//L98+eUSVq0qB2DLLbdK0ivwrF69mqqqSubOfZ65c59vMdZ4AxvdqDknJ5f6em+cbkXFD1RU\nVLDNNts22c/22yf+f2zgwOIGj3v16gVAXV1twvsQaQ8lQNIjFBS4VFa2nOSoqpyks40//wVEEqCi\nA/dm1dLvfY5IROLl5GQ3mRdNAurrvcGsl19+Nbvssmuz22+//Q4t7nvKlEncffcdlJSMZNSoXTn6\n6GPZddefcsUVlzRJRjorHPZiPuywiZx00inNrrP11kMbPA4EWu4sVFfnvfbc3Nwmy3Jz8xKOq7Xn\nEEkFJUDSI5SU1LNsWdMxQNDxqnIiXcYYai74Dfl334GpqcGsqcAtLPI7KhGJWLr0yybzliz5gqys\nLEaP3geAgoIC9t13/wbrfPLJR1RUVJCX16vZ/X799TLuvvsOjj76WKZN+3ODZStXrkhS9JsMGDCQ\n/Px86urqmsT6zTdfU1a2kPz8goT3V1xcTEFBb7744n9Nli1Z8kWn4xVJFaXc0iMku6qcSFervuKa\n2HS/43/hYyQi0tgnn3zMhx/+M/Z45cqVPPnk3xk9eh9+/OP/Y/DgzXjggftZt25dbJ21a6s444yT\nufDCcwlGrs5FCySEw2EA1qypAMDahl3sXnnlJZYs+YJQS/eL6KBgMMiYMQfzyisv8emnixosu/ba\nKzn55OP44YfVCe8vEAhwyCFjmT//Zb76amls/po1Fcya9WSDdRu/dhE/qQVIeoRkV5UT6XJZWdQe\ncCA5818h+4P3oa4Ospt2uxGRrpebm8svf3kUZ511Lnl5vXj44QcIh8NMmnQj2dnZ3HTTbZxxxq84\n8MC9Of74X5GXl8uMGY/y9dfLuO++B2MJULT087RpdzNmzEHsv/8YfvSjLZk69Q9s2LCBIUO24JNP\nPmLmzMfIy8trkFAly9VXT+Ktt17niCPGcuqpZ/CjH23Jyy+/yLx5L3LSSacyfPiIdu3vssuu4pVX\n5jF27BjOOONscnJyefTRv1BZuQYgVtyhqKg/gUCAl16ay5ZbbsX48Ycl/bWJJEoJkPQY0apypaXJ\nvWIm0lUqH/wrxcO8ewP1vvoy1v0++fcAEZH222WXXTnyyKO4445bqaqqYvfd9+Dqqyez0047A3DY\nYUfw97/PZurU27njjlsJBAIMHz6C6dNncvDBY2P7OfLIn/P8888yc+YM3nnnTQ49dBx/+9uTXHvt\nFTzwwJ9wXZehQ7dhypTfEwrVcdVVl7Fw4SeMHPmTpL2WbbYZxty587n11huZMeMRqqur2XrroVx/\n/U2cccY5Hdrf7NkvMGnS1dx11x/Iy8vj6KN/STAYZNq0u8jJ8cYH5efnc+WV1zJt2l1ceeWlKm8t\nvjJuN7tDZHn52oQCLi7uQ3n52lSHI11Ex7Pn0TFtXv+dtycr0ve/fEUl3aX5Usez59Ex9eyyy85s\nueVWzJ79gt+hdEqqjmd5eTkDBw5sUMYb4IorLuGRR/7CsmUryVZrdkrob7R1xcV9WvwC1RggEZE0\nsmbuq7HpvEcf8jESEZG2nX76Sey9924NxvbU1NQwb96L7LxzqZIfSUvqAicikkbCcff96PO7i9lw\n8mk+RiMi6aa+vp5Vq1YltG7fvn1j99RJlaOPPpbf/OZ8jjvuKA49dDwbN27gH/94guXLv+W226am\n9LlFOkoJkIhImql49iWKDj8EgOy336Rur719jkhE0sW3337DqFElCa179933ceyxx6c0nhNO+BV5\neXk88MB9XH/9tQQCAUaO/AlPPfUce+45OqXPLdJRGgMk3YKOZ8+jY9q64kF9Y9PlK6t8jCQxOp49\nj45petqwYQPvv/9uQusOHz6CwYM3A3Q8eyId09a1NgZILUAiImlo7Z330OfiXwMQWPolYVVMEhEg\nLy+vyU1MRaR9VARBRCQNbTjuxNh00SH7+ReIiIhID6MWIBGRdGQMNWecTf4DfyJQUYFZW4Xbp2/b\n24mIv1wXs24tge+/J7Ai+rMCNzeXDaed6Xd0IoISIBGRtFU9+SbyH/gTAH1PPp7Kp57zOSKRDOa6\nmDUVTRKbwMrvCXz/PVlx88z6mmZ3ERq1K6Ek3tRURDpGCZCklOtCWZlh0aIsqqsNBQUuJSX1lJa6\n3eX+jiL+CQap3WMvct59m5w3X4dQCIL6ty2SVK6LWbWKwIrvyVrxnZfUxCc4339HYOUKAitXYDZu\n7PjT9OqFG8hKYuAi0lH6Js0gjZOR/HyXvLwwGzcGUpKcuC7MmhXks88CsXO2ykrDsmUBliwJM3Fi\nSEmQSBuqps9k4HZbAlBww3VUT77R54hEepbs116h8Nifp/Q5XGOouu8v1JeUpvR5RCQxSoAyRONk\nxHXhvfeyWLnSMHiwy/Dh4aQnJ2VlpkHyExUMwuLFAYYNM4wc2b3KsIt0NbdvP8J9+hJYW0X+fX+k\netIUdOVAJHly3ng9Zft2AQNUX3M9teMmpOx5RKR9VAUuQzRORlasgFWrDMEglJcbVqzwTqiiyUlZ\nWedPsBYtymqxt04w6C0XkbZVvLzpBC33ib/5GIlIz1Pzm0up3Xs/wEtYkiWa/Kw/7kTWn3dBEvcs\nIp2lBChDNE5GVq40BCJHPxAglgBB8pKT6urWk6iaGl3FFklEeNi2sem+F5zjYyQiPY/btx+Vjz/J\nhqOOwZC8JMgAtXvtzbpb71SrrUiaUQKUIRonI7W1DR/X1TV8nIzkpKCg9a+R/Hx1fxNJ1Jonn41N\nBz9438dIRHqgnBzWTvsz1RddQrJSldCwbal6aDrk5CRpjyKSLEqAMkTjZCQnp+Hj7OyGj5ORnJSU\n1BMKNb8sFPKWi0hi6vbZLzZdNP4g/wIR6amMoebKa1l721TcQOdOj8L9Cql67O+4Rf2TFJyIJJMS\noAzROBkZNMglHPamw2EYPHhTwtOZ5MR1YeFCw4wZQd59N8jq1bB8ecN1QiEYMSJMaalagETaY+3N\nt8emA99+42MkIj3Xhl+dyoZfntDh7d1gkKqHZ1C/7fZJjEpEkkkJUIYoLXUZMSIcS4IGD4aBA11C\nISgudmMJUGeSk2iluTlzslm+PEBVlWHQIG/+ihWGfv1chgwJM358nUpgi3TAhlPPiE0XqhVIJLnC\nYfJvvp7iQX3p9dhf27159Ftz3a13Ujd6n+TGJiJJpTLYGcIYmDgxxLbbGsrKsqipMRxwQIhevcJs\n2BCgpsa7L1Bn7gPUXNlrY2CLLSAUctl995DKXot0hjGsP/EUek1/mKzl30J1NRQU+B2VSPe2bh39\nTj2BnAXzmyx66sA/ss9Hd1Fc8b9WdxGt+FZz7gVsOOFXKQmzI+Lv/2cMuG5QNyMXQQlQRjHGawkq\nLW08MCeclP0nUvZ65MgWBgWJSELW3XwbvaY/DEDfs06hasbffY5IpHsKfLmEooP3I1C5psF8NzeX\nv170Lp/W7whA2Q5Hcsrsoxi6/L1YotOYATYeOo7qayanPO5ENb7/X0EBVFcHdDNyEZQASQfEX1Gq\nrjYUFHgtR+vWqey1SMrl5FD3k/8j+5OPyZ33ItTXQ5buqSWZqaXvo9ZaOLJfe5XCY45sMr/up3tQ\nOeMJ3H6FfH1/NlR682vyB3L/0XM57oWTKfn8mWaToLqdS6m698HY32JH4ko23YxcpGVKgKRdGl9R\nAqisNCxbFmD1ahg0qOXbHajstUhyVD7+FAOHbwNA/q03UnPFtT5HJNL1Wvs+atLC4br0mnY3va+/\npsl+as46j+pJUxpcSCgocKms3PRlFsruxfTD/sbhr13C6E/ua7B9/eDNqJrxBPTu3f64kqhx0rVo\nUYDc3IZFjqLUK0MyXVoUQbDW/tRau8DvOKRtrV1Rqq9vWvEtSmWvRZLH7T8ANzsbgII7b29jbZGe\nqa0WjrIyAxs20Of0X1E8uF+T5Kdq2p8pX1lF9Q03N2lFbe42Dm4gi2cOuINn9r5507y8PKqmzyQ8\nZIv2xZVkjYsQVVYaVq40/Pe/AT77rPlTPfXKkEzmewJkrf0d8CCQ53cs0rbWxvkMGQLBoGnypaGy\n1yLJV/HaO7Hp3FlP+hiJiD9a+z7qv/5bdv3FThRvNYi8Z2c1WFbxyhuUr6xi49HHtrjvxpVTo0L1\nhhUnXEjlnx7Czc2latqfCf34/xKOK9rykmzNJV05OS6BAJSXG1asaJrsqFeGZLJ06AL3BfAzYLrf\ngUjbqqtbv2K0ww5h9tgjFKs019nKciLSvPodbGy671mnUn7kUT5GI9L1mvs+2vrbd/n14/s3mR8a\nsSNrnnwOt7g4oX03Vzk1/vus1hzFD3vsSXjzIQnFFS8VLS/NJV2DBrlUVkIg4N2KYtiwTcvUK0My\nne8JkOM4T1lrhya6flFRPsFgYldPiov7dDQsacHmmxO7Iua68N138P33UFsLOTkwciQccACMGZP8\n59bx7Hl0TDvp+edhwgQAipf9F3bZxddwdDx7nnQ+prHvI9dll4/+zGFzzm660qmnwn33EczJYWAH\nnmPMmFa+z4pts7Pjvyebs9lmUFyc3E4vxjStiD9sGKxfDytWbBqbW1CQS12d969izJhcXZjsAdL5\nbzSd+Z4AtVdFRU1C6xUX96G8fG2Ko8k8W29t+PTT7Fhf5lWrDIFIR8pwGL74op4HHkj+IE8dz55H\nxzQJdtuH2PXsUaMoX1nlWyg6nj1Puh/ToVuE2HHqb9nj04ebLHty/7sovOIURv4YqNwIbOyyuOK/\nJxsLhWD06DrKy5Pb/cx1g1RXNx3VMHQo5OfDxo2GoqIcwuH1sVasVauSGoL4IN3/Rv3WWnLYagJk\nre0F7I03Puc9x3FWNlqeBxznOM5DSYhTuoHSUpclS8K8/nrT5GfgQJchQ1ReU6QrrbtuCr0nXw1A\nYMX3hAdv5nNEIqllVq+m8KjDGfPvRU2W3XvsK3y+2WhGjAizj08VzqLfk4sXNxyTk8rxsCUl9Sxb\n1rTwgjEwYACMH1/LgQfmUF6uqm8i0EoCZK0dDrwIDMAreR+01t7kOM71cav1Ax4AlABliGi/aMfJ\nZtWqMHV1huxsl0GDXAYP9parvKZI11l/7vmxBKjfxHFUvPuxzxGJpEbWojL6jxndZP6G4h8x/ez5\nrMzdkvx8l/Eldb6OO21r/FAq4mpP0pUO9ygS8VtrLUB/BF4DzgTCwBnA7dZaC5zoOE44WUE4jrMU\n2D1Z+5PUMgaKioj8Q23+SpbKa4p0EWPYcNQx5D35BMEv/ud1+u/Vy++oRJIm9+l/0Pfs05rM3zjh\nCKqm/Rl69WIiAHVdHVqLjPG+I0tLu+ZCYKJJl1/3KBJJN60lQLsB5zqOE/2P8idr7SLgBWC6tfaE\nlEcnaavxTeIaU3lNka6z9s57yHvyCQD6XHAOax94xN+ARDqrvp6CG64j/967myxad9V1rL/gNy3f\ndTtDJZJ0tXWPInVfl0zRWgL0A7AV8Hl0huM4b1trj8BLgh4CrkxteJKuWupvDCqvKdLlcnMJjdiR\n4OL/kPfM06y9/yFiA/REuhFTVUnfk35JzjtvNVlW+fiT1I452Ieoul6quqktWpRFVpZXvXXlSkNt\nrSEnZ1M3dnVfl0zRWgI0HXjYWns98IzjOOUAjuMssNYeBTwJ7NQFMUoa8mOQp4i0bM2TzzFwp20B\nyL/7DmouusTniLoPjYnouGS9d1n/+5yiMaMx69c3mB/u05c1Ly+gfth2SY48faWym9q6dV4LUHwR\no40bDZWVUFHhUlio727JDK0lQJOBWuAy4H/AgugCx3FesNbuj4ofZCw/BnmKSMvib/BYcNP1SoAS\npDERHZeM9y5n3lz6nXBMk/m1e+9H1aOP4fbOvHucpLKbWkUFDZKfqEDAm//DDx0MWhrQRZX012IC\n5DhOPTAl8tPc8veBnay1agXKUF09yFNEWvfD/Lfpf8BeAOS88Dy14yb4HFH605iIjuvwexcOkz/1\ndgpuaXp6UXPBb6i+4hrISuyG5z3RokVZzXYvh85XWW3r5Fsn552niyrdQ6c7iTuO8+9kBCIiIp1T\nv3NJbLrfycf5GEn3kcjJpjSv3e9dTQ19TzqW4s0KmyQ/lQ8+SvnKKqqvnpTRyQ9AdXXrZ8edqbJa\nWAjFxS7hRnV8w2FvflFRh3ctEW1dGCgrU/aTDjRKVkSkB6l8aEZsOuvfn/oYSfeQypPNni7R9y7w\n9TL677w9xUM3I/fFFxqs88Nr71C+soraw49MWZzdTUFB6y2Onamy2ru3y/DhYXbYIUyfPi55edCn\nj8sOO4QZPjzc5nNL23RRpXtobQyQiIh0M7UTDo9N999/T8pXVvkYTfpTSf+Oa+u922H56xQPOrTJ\n/LqRP6Hyiadx+w9IZXjdViqrrEb3PXiwy+DBDT/bquDafs2N9fn88wAFBS1vo4sq6SGhBMhauw/w\njuM4oUbzc4FDHcd5JhXBiYhI+1VfdhUFv78RALNqFe7AgT5HlL7StaR/dxhE3ex757rs9cl9TJz/\nmybrrz/5NNbddBstXh4XILVVVlXBNXlaGuvz3/8aevUyDB8ebnY7XVRJD63+F7LWBgADvAZsYa1d\n2WiVUmAmoNuOi4ikiZqLL40lQIVHHU7Fgnd8jih9peMJYXcZRB3/3uWaWo5+6Wx2+c/fmqy39s57\n2HDciRphn6BUVllVBdfkaWmsz+abuzhOgKIio1a2NNZiAmStPQu4D3DxkqBvW1h1XgriEhGRjgoE\n2Dj+cHLnPEvwP5/Cxo2Qm+t3VGkpHU8Iu0tlOmPgZ3t+S68bx1Ow7L9Nlle88AqhUbv5EFn3l8oq\nq6rgmhwtjfUZPNi7p9J33zVMgNTKll5aK4N9v7V2MV6hhPnAUUB8hXgXWAcsSmmEIiLSblXT/kzx\nnGcB6PPbC1h7z/0+R5S+0u2EMJVlkJMl+MlHFB2yf5P5oW2GUfnMXMKbbe5DVCJdp6UiIMbA8OFh\n1q+HIUPCaXFRRZpqtQuc4zhvAFhrtwGWOY6jtFVEpDvIz6d+66FkfbWUvL8/zto//kldkLqJdK5M\nlzvzMfpecE6T+Rt+dhRr77pPLY2SMVorAmIMbLddmBNOSI+LKtJUoiMRvwfOtNbuCmTjdYmLcRzn\npGQHJiIinbPmuZcYUGoB6HX/NNaf/WufI5JEpF1lulCIgmuvIP/Bpq2I6ybfxPqzz1NyLRknXQuo\nSGISTYAeAH4OvAiopqqISDcQ3w2p97VXKgHqJtLmxKqigsKxB5P90YdNFq158lnq9tmva+JoRneo\nkpcqmfza00k6FlCRxCWaAI0DjnUc57lUBiOpk+7/MNM9PpHuquKl12JjNbLnv0zdAQf5HJG0xe8T\nq6zPFlO07+7gumTHzQ8PHEjF3PmEtx6a0udvS1dXyfPr+6m5591553q++CILx0nvCoGZIB0LqEji\nEk2A1gNLUhmIpE66l1RNJD4R6ZjQT3aJTRce+3PdGLUb8OvEKmfOc/Q75fgm8zceeDBVDzxKq3d3\n7EJdWSXPr+/Plp73gw8CrFkTYKedGt5jJt0qBGaKdCugIolLNAG6Hphqrb0QLxGqjV/oOE7zd3uS\ntJDuJVUTie/AA/2JTSw6FhkAACAASURBVKQnqLrvQfqeczoAWZ//l/rtd/A5ImlLl51YhcPk33oj\nBXfc1nTZdddRfs7FEAikNoZ26soqeX59f7b0vKtXGyorDStWNL3HTLpUCBTpDhL9r3YtsC9eyetq\noK7Rj6SxRL4s/JTu8Yl0dxt//ovYdNGY0T5GImlj3Tr6HXMkxZsVNkl+Kh/5m9dSOGlS2iU/0LVV\n8vz6fmrpeWtrDYEArFjR/Gv0s0KgSHeSaAvQCSmNQlIqnUuqQvrHJ9IT1Fz4W/Lv+gNmwwbMmgrc\nwiK/QxIfBJZ+SdHB+xJYs6bBfDc7m4r5b1Nvh/sUWeK6skqeX99PLT1vTo7Lxo2Gurrml3d5hUCR\nbiqhBMhxnNcBrLVbADsA7wF9HcdZkcLYJEnSrqRqI+ken0hPUH351eTf9QcA+v3yKNbMfdXniKQr\nZS+YT+EvJjaZX7fb7lQ+9nfcfoU+RNWxAgNdWSXPr++nlp530CCXykrIzm76vK29dteFjz6CN94I\nqtCQCAkmQNba3sDDeKWww3hJ0J3W2mJgouM4K1MXonRW2pRUbUG6xyfSI2RlsfHAg8l9ZR7ZH30A\ndXWQnd32dtKlklpxzHXpde8f6T356iaLas46l+pJN0KWf12MO1pgoCur5Pn1/dTS8w4eDKtXuxQV\nNRx63dprj77Py5ZBba3XpTGdCiGJ+CHRzr1/AAYC2+BVhAP4LeACd6cgLkmi0lKXESPChBqNi0yX\nWvXpHp9IT1H150di072v/J1/gUizoieqc+Zks3x5gMpKw/LlAebMyWb27CBuov8KN2ygzxknUzy4\nX5Pkp+qe+ylfWUX1Dbf4mvxA2wUGysqaPyuPVsmbMKGOIUPCFBa6DBkSZvz4uqSfzPv1/dTS89bX\nwxFHhDjllMRfe/R9bny9o633WaQnS3QM0OHAOMdxvrLWu6u44zj/s9aeC7yWquAkOdK9Vn26xyfS\nY/TuTf3gzcha8T29Hv0L6269A/2BpY/OVhwLfLecwsMOJWvZ0ibLKl5+ndDInyQ54s7pTDW3rqqS\n59f3UyLPm2i1t66smifSXSSaAPWiUenriFxA357dQFd8WXSm64Zq6Yt0jTUvvMKAXXYGIO/hB9lw\n6hk+RyRRHT1RDf7zfYomNL3BbWj4CNY89TxucXGyQ02K7lIAx6/vJ2OgpMTFdetj36te1bn2JV+p\nfJ91E3PprhJNgJ4BbrbWnhR57Fprtwf+CDyfksikW0n3m62KiCe85Vax6T6X/1YJUBpp14mq65I3\n/RH6XHJhk/XW//IE1t02FXJykh1iUqkATuuS9b2aqvdZ3/vSnSU6Buh8vPv9rAYKgIXAZ5HHTf/7\nSsbpaF9uEel6Fc/Ni01nv/m6j5FIvIKC1k9E8/NdqKuj928voHhwvybJz9pb/kD5ikrW3XVvUpMf\n14WFCw0zZgS5//5sZswIsnChSXxMUgtKSuqbjHGJUgGc5H2vpup91vf+/7N35/FRVecfxz93ZhJC\nQlgEEqSCsugFMQER69pqRa2CShCo1qp13/qzrVZt3felrbZqa61V61LEFcWFYqtFcbcuSLDirSVo\nUCQJGAIkJJnl/v64SchklkyS2ef7fr3yYnLvZObMwr33Oec5z5FMFmsZ7M3AHNM0xwET2v7Osizr\n00Q2TjKDbcOSJR4++cSF12uQl2dTWmp3rFKtHGOR9OLbZ9+O24PnHO0seikpF63iWL8tGzjjrhkU\nX1gZsm/Ts0vw7ndAx+/xTEtKZC9/Mqu5ZaJ4zd1pf5+/+CJ4e1/fZ80tkkwWawocpmkaQAvwCW3z\nfkzTHAtgWVZVQlonKdfdibT95Pj+++6Ohdmamw0aGgzq620mTHBKdcYjl1u5xiLxs+X2uyj++U8A\ncK2pIjBmbIpbJOECgpG1K7jg4X1C7uvfcSSbFr9EYKdRQdvjHbD0tTBDNCqAE1285u60v89ffgnL\nlgXi9j5nyhwukXBiXQfoSOBeYMcuuwycUtipraWZZLlyIR7LibT95FhQYAetTO1yQV2dwZAhBqWl\ndp9zuZVrLBJfzT88sSMAGvL9g9n43+oUt0g6BwTNDz3NzL/9OOQ+LTOOZvPd90H//mEfI94BS6J7\n+VUAJ7J4zt0xDJg6FUaNit/7rDlckslinQP0a+AtYAowttPPmLZ/c0bc1mlIc7YNTz3l4qmnPKxY\n4aay0kVNjXOg65zf235yLCmxCQSvy4bLBTU1RlxyuT/8EOUai8STYdB09nkAuDZtwtiiNLiU8/sZ\ncN2VTD+0OCT42Xr51dTVNLD5wUciBj8QW8DSE+r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5c4/joYfuj6lds2fPCfp9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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "biplot(X_train_scaled_df, reduced_data_df, pca)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Observation

\n", "

We can see that right points in the middle of principal component 2 axis are iris with big(long) \"petal length\", \n", " \"sepal width\" and \"sepal length\".
As we saw from the visualization of the feature weights, \n", " \"petal length\", \"sepal width\" and \"sepal length\" are mostly strongly accociated with\n", " the first component, and \"petal width\" with the second component.\n", "

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Clustering

\n", "\n", "

In this section, we will use K-means to cluter the datasets.
\n", "We will use Silhouette coefficients to choose the number of cluster

" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cluster is 2, score is 0.613037 \n", "cluster is 3, score is 0.508155 \n", "cluster is 4, score is 0.445313 \n", "cluster is 5, score is 0.413809 \n", "cluster is 6, score is 0.427851 \n", "cluster is 7, score is 0.426982 \n", "cluster is 8, score is 0.445488 \n", "cluster is 9, score is 0.442099 \n" ] } ], "source": [ "from sklearn.cluster import KMeans\n", "from sklearn.metrics import silhouette_score\n", "clusters = [i for i in range(2, 10)]\n", "for cluster in clusters:\n", " kmeans = KMeans(n_clusters=cluster)\n", " clusterer = kmeans.fit(reduced_data)\n", " \n", " preds = clusterer.predict(reduced_data)\n", " centers = clusterer.cluster_centers_\n", " \n", " sample_pred = clusterer.predict(reduced_samples)\n", " \n", " score = silhouette_score(reduced_data, preds)\n", " print(\"cluster is %d, score is %f \" % (cluster,score))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Two clusters results in the best score even though we know that we actually have three classes\n", "in the datasets

" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1.11009664 -0.14611241]\n", " [-2.22019328 0.29222482]]\n" ] } ], "source": [ "# Fit a kmeans with two clusters\n", "kmeans = KMeans(n_clusters=2, random_state=0)\n", "clusterer = kmeans.fit(reduced_data)\n", "\n", "preds = clusterer.predict(reduced_data)\n", "\n", "centers = clusterer.cluster_centers_\n", "\n", "samples_preds = clusterer.predict(reduced_samples)\n", "print(centers)" ] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, "outputs": [], "source": [ "import matplotlib.cm as cm\n", "\n", "def cluster_results(reduced_data, preds, centers, pca_samples):\n", "\t'''\n", "\tVisualizes the PCA-reduced cluster data in two dimensions\n", "\tAdds cues for cluster centers and student-selected sample data\n", "\t'''\n", "\n", "\tpredictions = pd.DataFrame(preds, columns = ['Cluster'])\n", "\tplot_data = pd.concat([predictions, reduced_data], axis = 1)\n", "\n", "\t# Generate the cluster plot\n", "\tfig, ax = plt.subplots(figsize = (14,8))\n", "\n", "\t# Color map\n", "\tcmap = cm.get_cmap('gist_rainbow')\n", "\n", "\t# Color the points based on assigned cluster\n", "\tfor i, cluster in plot_data.groupby('Cluster'): \n", "\t cluster.plot(ax = ax, kind = 'scatter', x = 'PCA 1', y = 'PCA 2', \\\n", "\t color = cmap((i)*1.0/(len(centers)-1)), label = 'Cluster %i'%(i), s=30);\n", "\n", "\t# Plot centers with indicators\n", "\tfor i, c in enumerate(centers):\n", "\t ax.scatter(x = c[0], y = c[1], color = 'white', edgecolors = 'black', \\\n", "\t alpha = 1, linewidth = 2, marker = 'o', s=200);\n", "\t ax.scatter(x = c[0], y = c[1], marker='$%d$'%(i), alpha = 1, s=100);\n", "\n", "\t# Plot transformed sample points \n", "\tax.scatter(x = pca_samples[:,0], y = pca_samples[:,1], \\\n", "\t s = 150, linewidth = 4, color = 'black', marker = 'x');\n", "\n", "\t# Set plot title\n", "\tax.set_title(\"Cluster Learning on PCA-Reduced Data - Centroids Marked by Number\\nTransformed Sample Data Marked by Black Cross\");\n" ] }, { "cell_type": "code", "execution_count": 124, "metadata": {}, "outputs": [ { "data": { "image/png": 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5XRnXW778Lo46aiYtLS39VDIZrNSlTdJqm1pNvCLSKehpr4zQNqXvE0CJiIhI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\nlLiust6uQAAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Display the results of the clustering from implementation\n", "cluster_results(reduced_data_df, preds, centers, reduced_samples)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

We will use t-SNE to see the class distribution in two dimension

\n", "

t-SNE will try to find a two dimensional representation of data which will preserve the distance in the original dimension( that is to say, points that are close will remain close and points that are far away remain far away).

\n", "

Important to Note: We are able to understand the class distribution since we have ground the truth labels.

" ] }, { "cell_type": "code", "execution_count": 125, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,\"['0: Iris-setosa', '1: Iris-versicolor', '2: Iris-virginica']\")" ] }, "execution_count": 125, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AOIkwAwAAAMBJhBkAAAAATiLMAAAAAHASYQYAAACAkwgzAAAAAJxU6PUBjTHv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\nvUybj2+do5o3X1PvY6eo7Mh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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "colors = [\"#476A2A\", \"#7851B8\", \"#BD3430\"]\n", "from sklearn.manifold import TSNE\n", "tsne = TSNE(random_state=42)\n", "\n", "X_train_scaled_tsne = tsne.fit_transform(X_train_scaled)\n", "plt.figure(figsize=(14, 8))\n", "plt.xlim(X_train_scaled_tsne[:, 0].min(), X_train_scaled_tsne[:, 0].max()+1)\n", "plt.ylim(X_train_scaled_tsne[:, 1].min(), X_train_scaled_tsne[:, 1].max()+1)\n", "for i in range(X_train_scaled_tsne.shape[0]):\n", " plt.text(X_train_scaled_tsne[i, 0], X_train_scaled_tsne[i, 1],\n", " str(y_train[i]), \n", " color = colors[y_train[i]],\n", " fontdict={'weight': 'bold', 'size': 9})\n", " \n", "class_index = [\"{0}: {1}\".format(class_num, le.inverse_transform(class_num)) for class_num in [0, 1, 2]]\n", "plt.title(class_index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Observation

\n", "

We can see that class label setosa is well seprated from the other two classes, \n", " versicolor and virginica.K-means might have not been able to seperate the two latter classes.

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

What's Next?

\n", "

We could use the finings and explore other techniques on this datasets\n", "such as other clustering methods or conduct supervised learning with data whose features are reduced by PCA. What if we perform clustering on the original non-reduced dataset?

\n", "

\n", " \"rock

\n", "

Image from NARUTO SPINOFF

" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "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": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 }