{ "cells": [ { "cell_type": "markdown", "metadata": { "button": false, "new_sheet": false, "run_control": { "read_only": false } }, "source": [ " \n", "\n", "\n", " Engineering AI (ENGI XXXXXXXXXXLab...

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attached is the Spyder file for the assignment, to be done in anaconda


{ "cells": [ { "cell_type": "markdown", "metadata": { "button": false, "new_sheet": false, "run_control": { "read_only": false } }, "source": [ "

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Engineering AI (ENGI37926) - Lab 5

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Lab Overview

\n", "This is part of the lab series for Engineering Artificial Intelligence, prepared in Fall, 2021. In this lab, we learn how to use Python libary to implement Decision Tree and Randow Forest. \n", "\n", "

Table of contents

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  1. Import Needed Packages
  2. \n", "

  3. Decision Tree
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  5. Random Forest

  6. \n", "

  7. Practice
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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "


1. Import Needed packages

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For this lab, the following packages are needed


\n", "numpy: Python library for working with arrays \n", "matplotlib: Python inline ploting library \n", "pandas: Python Data Analysis Library to work with dataframes \n", "sklearn: scikit-learn, a commonly used machine learning library. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "


2. Decision Tree

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For this lab, we are going to use our own randomly generated data.

\n", "\n", "In this section, we are going to create random clusters of points with labels. Scikit-learn has a make_blobs class that are able to create a cluster of points called blob. The benefit is that all points created this way will have an inherently label identifying which cluster it belongs to. \n", "\n", "In order for make_blobs to work properly, let's look into the following paramters: \n", "\n", "n_samples: int or array type, default = 100. Represents the total number of points of the dataset. If int, it is the total number of points equally divided among clusters. If array-like, each element of the sequence indicates the number of samples per cluster. \n", "\n", "centers: int or array of shape, default=None. The number of centers to generate, or the fixed center locations. If n_samples is an int and centers is None, 3 centers are generated. If n_samples is array-like, centers must be either None or an array of length equal to the length of n_samples.\n", "\n", "cluster_std: float or sequence of floats, default=1.0. The standard deviation of the clusters. \n", "\n", "random_state: int, default=None. Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. \n", "\n", "\n", "

Scikit-learn make_blobs reference:
click here

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Scikit-learn randomized data generation:
click here

\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.datasets import make_blobs\n", "\n", "n_samples = [2000,2000,2000]\n", "random_state = 200\n", "\n", "cluster_std = 2.5\n", "centers = None\n", "\n", "X, Y_true = make_blobs(n_samples=n_samples, centers=centers, random_state=random_state, cluster_std=cluster_std)\n", "plt.scatter(X[:, 0], X[:, 1], marker='.', c=Y_true)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Based on the code above and the related parameters, please answer the following questions based on your observation. \n", "\n", "__(0.5') Question 1__: What is the impact of doubling the value of paramter cluster_std? Why? \n", "Answer: doubling the value of parameter cluster_std would make the clusters come together closer to the middle. The reason for this is because as the number gets higher, the more it tightens the standard deviation of the clusters around the mean \n", "\n", "__(0.5') Question 2__: What are the total number of data points? How to change it? \n", "Answer: 2000, to change the data points you can change the values of n_samples as they are the number of samples per cluster \n", "\n", "__(0.5') Question 3__: What are the features of this dataset generated by make_blobs? \n", "Answer: n_samples, random_state, cluster_std, centers \n", "\n", "__(0.5') Question 4__: What do the colors of these points stand for? \n", "Answer: They stand for the colour of their true class " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__(1')__ Now split the dataset into test (80%) and training samples (20%). " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'x' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_14428/900440855.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mx_train\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'x' is not defined" ] } ], "source": [ "import numpy as np\n", "from sklearn.model_selection import train_test_split\n", "\n", "\n", "x_train, x_test, y_train, y_test = train_test_split(x, y)\n", "x_train\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "DecisionTreeClassifier is a class capable of performing multi-class classification on a dataset. As with other classifiers, DecisionTreeClassifier takes as input two arrays: an array X of shape (n_samples, n_features) holding the training samples, and an array Y of integer values, shape (n_samples,), holding the class labels for the training samples: \n", "

Scikit-learn decision tree classifier:
click here

" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'train_X' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m
Answered 1 days AfterNov 04, 2021

Answer To: { "cells": [ { "cell_type": "markdown", "metadata": { "button": false, "new_sheet": false,...

Sandeep Kumar answered on Nov 06 2021
111 Votes
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"\n",
"\n",
"\n",
"

Engineering AI (ENGI37926) - Lab 5

\n",
"\n",
"

Lab Overview

\n",
"This is part of the lab series for Engineering Artificial Intelligence, prepared in Fall, 2021. In this lab, we learn how to use Python libary to implement Decision Tree and Randow Forest. \n",
"\n",
"

Table of contents

\n",
"\n",
"
\n",
"
    \n",
    "
  1. Import Needed Packages
  2. \n",
    "
  3. Decision Tree
  4. \n",
    "
  5. Random Forest
  6. \n",
    "
  7. Practice
  8. \n",
    "
\n",
"
\n",
"
\n",
"
"
]
},
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"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"

1. Import Needed packages

\n",
"

For this lab, the following packages are needed


\n",
"numpy: Python library for working with arrays
\n",
"matplotlib: Python inline ploting library
\n",
"pandas: Python Data Analysis Library to work with dataframes
\n",
"sklearn: scikit-learn, a commonly used machine learning library. "
]
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{
"cell_type": "code",
"execution_count": 1,
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"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"

2. Decision Tree

\n",
"

For this lab, we are going to use our own randomly generated data.

\n",
"\n",
"In this section, we are going to create random clusters of points with labels. Scikit-learn has a make_blobs class that are able to create a cluster of points called blob. The benefit is that all points created this way will have an inherently label identifying which cluster it belongs to.
\n",
"\n",
"In order for make_blobs to work properly, let's look into the following paramters:
\n",
"\n",
"n_samples: int or array type, default = 100. Represents the total number of points of the dataset. If int, it is the total number of points equally divided among clusters. If array-like, each element of the sequence indicates the number of samples per cluster.
\n",
"\n",
"centers: int or array of shape, default=None. The number of centers to generate, or the fixed center locations. If n_samples is an int and centers is None, 3 centers are generated. If n_samples is array-like, centers must be either None or an array of length equal to the length of n_samples.\n",
"\n",
"cluster_std: float or sequence of floats, default=1.0. The standard deviation of the clusters. \n",
"\n",
"random_state: int, default=None. Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls.
\n",
"\n",
"\n",
"

Scikit-learn make_blobs reference: click here

\n",
"\n",
"

Scikit-learn randomized data generation: click here

\n"
]
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{
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""
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"metadata": {},
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",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn.datasets import make_blobs\n",
"\n",
"n_samples = [2000,2000,2000]\n",
"random_state = 200\n",
"\n",
"cluster_std = 2.5\n",
"centers = None\n",
"\n",
"X, Y_true = make_blobs(n_samples=n_samples, centers=centers, random_state=random_state, cluster_std=cluster_std)\n",
"plt.scatter(X[:, 0], X[:, 1], marker='.', c=Y_true)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Based on the code above and the related parameters, please answer the following questions based on your observation. \n",
"\n",
"__(0.5') Question 1__: What is the impact of doubling the value of paramter cluster_std? Why?
\n",
"Answer: doubling the value of parameter cluster_std would make the clusters come together closer to the middle. The reason for this is because as the number gets higher, the more it tightens the standard deviation of the clusters around the mean
\n",
"\n",
"__(0.5') Question 2__: What are the total number of data points? How to change it?
\n",
"Answer: 2000, to change the data points you can change the values of n_samples as they are the number of samples per cluster
\n",
"\n",
"__(0.5') Question 3__: What are the features of this dataset generated by make_blobs?
\n",
"Answer: n_samples, random_state, cluster_std, centers
\n",
"\n",
"__(0.5') Question 4__: What do the colors of these points stand for?
\n",
"Answer: They stand for the colour of their true class
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__(1')__ Now split the dataset into test (80%) and training samples (20%). "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.03418587, -3.9475651 ],\n",
" [ 3.15178717, -2.14025321],\n",
" [ 9.12730507, -5.50582914],\n",
" ...,\n",
" [ 0.24897543, -3.33425686],\n",
" [ 3.50366982, -2.7729243 ],\n",
" [ 9.57942331, -4.89508368]])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"\n",
"x_train, x_test, y_train, y_test = train_test_split(X, Y_true)\n",
"x_train\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"DecisionTreeClassifier is a class capable of performing multi-class classification on a dataset. As with other classifiers, DecisionTreeClassifier takes as input two arrays: an array X of shape (n_samples, n_features) holding the training samples, and an array Y of integer values, shape (n_samples,), holding the class labels for the training samples:
\n",
"

Scikit-learn decision tree classifier: click here

"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import tree\n",
"clf = tree.DecisionTreeClassifier(max_depth=6)\n",
"clf = clf.fit(x_train, y_train)\n",
"predict_y = clf.predict(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4500, 2)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4500,)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1500, 2)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_test.shape"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1500,)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predict_y.shape"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6000,)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Y_true.shape"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png":...
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