Start working on defeat learners assignment.
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"""
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A simple wrapper for linear regression. (c) 2015 Tucker Balch
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Note, this is NOT a correct DTLearner; Replace with your own implementation.
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Copyright 2018, Georgia Institute of Technology (Georgia Tech)
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Atlanta, Georgia 30332
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All Rights Reserved
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Template code for CS 4646/7646
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Georgia Tech asserts copyright ownership of this template and all derivative
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works, including solutions to the projects assigned in this course. Students
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||||
and other users of this template code are advised not to share it with others
|
||||
or to make it available on publicly viewable websites including repositories
|
||||
such as github and gitlab. This copyright statement should not be removed
|
||||
or edited.
|
||||
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||||
We do grant permission to share solutions privately with non-students such
|
||||
as potential employers. However, sharing with other current or future
|
||||
students of CS 7646 is prohibited and subject to being investigated as a
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GT honor code violation.
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-----do not edit anything above this line---
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Student Name: Tucker Balch (replace with your name)
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GT User ID: tb34 (replace with your User ID)
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GT ID: 900897987 (replace with your GT ID)
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"""
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import numpy as np
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import warnings
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class DTLearner(object):
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def __init__(self, leaf_size=1, verbose = False):
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warnings.warn("\n\n WARNING! THIS IS NOT A CORRECT DTLearner IMPLEMENTATION! REPLACE WITH YOUR OWN CODE\n")
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pass # move along, these aren't the drones you're looking for
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def author(self):
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return 'tb34' # replace tb34 with your Georgia Tech username
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def addEvidence(self,dataX,dataY):
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"""
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@summary: Add training data to learner
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@param dataX: X values of data to add
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@param dataY: the Y training values
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"""
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# slap on 1s column so linear regression finds a constant term
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newdataX = np.ones([dataX.shape[0],dataX.shape[1]+1])
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newdataX[:,0:dataX.shape[1]]=dataX
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# build and save the model
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self.model_coefs, residuals, rank, s = np.linalg.lstsq(newdataX, dataY, rcond=None)
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def query(self,points):
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"""
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@summary: Estimate a set of test points given the model we built.
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@param points: should be a numpy array with each row corresponding to a specific query.
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@returns the estimated values according to the saved model.
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"""
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return (self.model_coefs[:-1] * points).sum(axis = 1) + self.model_coefs[-1]
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if __name__=="__main__":
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print("the secret clue is 'zzyzx'")
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@ -0,0 +1,57 @@
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"""
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A simple wrapper for linear regression. (c) 2015 Tucker Balch
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Copyright 2018, Georgia Institute of Technology (Georgia Tech)
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Atlanta, Georgia 30332
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All Rights Reserved
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Template code for CS 4646/7646
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Georgia Tech asserts copyright ownership of this template and all derivative
|
||||
works, including solutions to the projects assigned in this course. Students
|
||||
and other users of this template code are advised not to share it with others
|
||||
or to make it available on publicly viewable websites including repositories
|
||||
such as github and gitlab. This copyright statement should not be removed
|
||||
or edited.
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||||
|
||||
We do grant permission to share solutions privately with non-students such
|
||||
as potential employers. However, sharing with other current or future
|
||||
students of CS 7646 is prohibited and subject to being investigated as a
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GT honor code violation.
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||||
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-----do not edit anything above this line---
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"""
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import numpy as np
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class LinRegLearner(object):
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def __init__(self, verbose = False):
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pass # move along, these aren't the drones you're looking for
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def author(self):
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return 'tb34' # replace tb34 with your Georgia Tech username
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def addEvidence(self,dataX,dataY):
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"""
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@summary: Add training data to learner
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@param dataX: X values of data to add
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@param dataY: the Y training values
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"""
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# slap on 1s column so linear regression finds a constant term
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newdataX = np.ones([dataX.shape[0],dataX.shape[1]+1])
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newdataX[:,0:dataX.shape[1]]=dataX
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# build and save the model
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self.model_coefs, residuals, rank, s = np.linalg.lstsq(newdataX, dataY, rcond=None)
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def query(self,points):
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"""
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@summary: Estimate a set of test points given the model we built.
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@param points: should be a numpy array with each row corresponding to a specific query.
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@returns the estimated values according to the saved model.
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"""
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return (self.model_coefs[:-1] * points).sum(axis = 1) + self.model_coefs[-1]
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if __name__=="__main__":
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print("the secret clue is 'zzyzx'")
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@ -0,0 +1,52 @@
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"""
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template for generating data to fool learners (c) 2016 Tucker Balch
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Copyright 2018, Georgia Institute of Technology (Georgia Tech)
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Atlanta, Georgia 30332
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All Rights Reserved
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Template code for CS 4646/7646
|
||||
|
||||
Georgia Tech asserts copyright ownership of this template and all derivative
|
||||
works, including solutions to the projects assigned in this course. Students
|
||||
and other users of this template code are advised not to share it with others
|
||||
or to make it available on publicly viewable websites including repositories
|
||||
such as github and gitlab. This copyright statement should not be removed
|
||||
or edited.
|
||||
|
||||
We do grant permission to share solutions privately with non-students such
|
||||
as potential employers. However, sharing with other current or future
|
||||
students of CS 7646 is prohibited and subject to being investigated as a
|
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GT honor code violation.
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||||
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-----do not edit anything above this line---
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Student Name: Tucker Balch (replace with your name)
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GT User ID: tb34 (replace with your User ID)
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GT ID: 900897987 (replace with your GT ID)
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"""
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import numpy as np
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import math
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# this function should return a dataset (X and Y) that will work
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# better for linear regression than decision trees
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def best4LinReg(seed=1489683273):
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np.random.seed(seed)
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X = np.zeros((100,2))
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Y = np.random.random(size = (100,))*200-100
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# Here's is an example of creating a Y from randomly generated
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# X with multiple columns
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# Y = X[:,0] + np.sin(X[:,1]) + X[:,2]**2 + X[:,3]**3
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return X, Y
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def best4DT(seed=1489683273):
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np.random.seed(seed)
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X = np.zeros((100,2))
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Y = np.random.random(size = (100,))*200-100
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return X, Y
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def author():
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return 'tb34' #Change this to your user ID
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if __name__=="__main__":
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print("they call me Tim.")
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@ -0,0 +1,230 @@
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"""MC3-H1: Best4{LR,DT} - grading script.
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Usage:
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- Switch to a student feedback directory first (will write "points.txt" and "comments.txt" in pwd).
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- Run this script with both ml4t/ and student solution in PYTHONPATH, e.g.:
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PYTHONPATH=ml4t:MC3-P1/jdoe7 python ml4t/mc3_p1_grading/grade_learners.py
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Copyright 2018, Georgia Institute of Technology (Georgia Tech)
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Atlanta, Georgia 30332
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All Rights Reserved
|
||||
|
||||
Template code for CS 4646/7646
|
||||
|
||||
Georgia Tech asserts copyright ownership of this template and all derivative
|
||||
works, including solutions to the projects assigned in this course. Students
|
||||
and other users of this template code are advised not to share it with others
|
||||
or to make it available on publicly viewable websites including repositories
|
||||
such as github and gitlab. This copyright statement should not be removed
|
||||
or edited.
|
||||
|
||||
We do grant permission to share solutions privately with non-students such
|
||||
as potential employers. However, sharing with other current or future
|
||||
students of CS 7646 is prohibited and subject to being investigated as a
|
||||
GT honor code violation.
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||||
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-----do not edit anything above this line---
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"""
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import pytest
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from grading.grading import grader, GradeResult, time_limit, run_with_timeout, IncorrectOutput
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# These two lines will be commented out in the final grading script.
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from LinRegLearner import LinRegLearner
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from DTLearner import DTLearner
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import os
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import sys
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import traceback as tb
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import numpy as np
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import pandas as pd
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from collections import namedtuple
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import math
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import time
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import functools
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seconds_per_test_case = 5
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max_points = 100.0
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html_pre_block = True # surround comments with HTML <pre> tag (for T-Square comments field)
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# Test cases
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Best4TestCase = namedtuple('Best4TestCase', ['description', 'group','max_tests','needed_wins','row_limits','col_limits','seed'])
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best4_test_cases = [
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Best4TestCase(
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description="Test Case 1: Best4LinReg",
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group="best4lr",
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max_tests=15,
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needed_wins=10,
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row_limits=(10,1000),
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col_limits=(2,10),
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seed=1489683274
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),
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Best4TestCase(
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description="Test Case 2: Best4DT",
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group="best4dt",
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max_tests=15,
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needed_wins=10,
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row_limits=(10,1000),
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col_limits=(2,10),
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seed=1489683274
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),
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Best4TestCase(
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description='Test for author() method',
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group='author',
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max_tests=None,
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needed_wins=None,
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row_limits=None,
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col_limits=None,
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seed=None,
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),
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]
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# Test functon(s)
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@pytest.mark.parametrize("description,group,max_tests,needed_wins,row_limits,col_limits,seed", best4_test_cases)
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def test_learners(description, group, max_tests, needed_wins, row_limits, col_limits, seed, grader):
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"""Test data generation methods beat given learner.
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Requires test description, test case group, and a grader fixture.
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"""
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points_earned = 0.0 # initialize points for this test case
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incorrect = True
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msgs = []
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try:
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dataX, dataY = None,None
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same_dataX, same_dataY = None,None
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diff_dataX, diff_dataY = None,None
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betterLearner, worseLearner = None, None
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if group=='author':
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try:
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from gen_data import author
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auth_string = run_with_timeout(author,seconds_per_test_case,(),{})
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if auth_string == 'tb34':
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incorrect = True
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msgs.append(" Incorrect author name (tb34)")
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points_earned = -10
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elif auth_string == '':
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incorrect = True
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msgs.append(" Empty author name")
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points_earned = -10
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else:
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incorrect = False
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except Exception as e:
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incorrect = True
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msgs.append(" Exception occured when calling author() method: {}".format(e))
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points_earned = -10
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else:
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if group=="best4dt":
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from gen_data import best4DT
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dataX, dataY = run_with_timeout(best4DT,seconds_per_test_case,(),{'seed':seed})
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same_dataX,same_dataY = run_with_timeout(best4DT,seconds_per_test_case,(),{'seed':seed})
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diff_dataX,diff_dataY = run_with_timeout(best4DT,seconds_per_test_case,(),{'seed':seed+1})
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betterLearner = DTLearner
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worseLearner = LinRegLearner
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elif group=='best4lr':
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from gen_data import best4LinReg
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dataX, dataY = run_with_timeout(best4LinReg,seconds_per_test_case,(),{'seed':seed})
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same_dataX, same_dataY = run_with_timeout(best4LinReg,seconds_per_test_case,(),{'seed':seed})
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diff_dataX, diff_dataY = run_with_timeout(best4LinReg,seconds_per_test_case,(),{'seed':seed+1})
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betterLearner = LinRegLearner
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worseLearner = DTLearner
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num_samples = dataX.shape[0]
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cutoff = int(num_samples*0.6)
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worse_better_err = []
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for run in range(max_tests):
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permutation = np.random.permutation(num_samples)
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train_X,train_Y = dataX[permutation[:cutoff]], dataY[permutation[:cutoff]]
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test_X,test_Y = dataX[permutation[cutoff:]], dataY[permutation[cutoff:]]
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better = betterLearner()
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worse = worseLearner()
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better.addEvidence(train_X,train_Y)
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worse.addEvidence(train_X,train_Y)
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better_pred = better.query(test_X)
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worse_pred = worse.query(test_X)
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better_err = np.linalg.norm(test_Y-better_pred)
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worse_err = np.linalg.norm(test_Y-worse_pred)
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worse_better_err.append( (worse_err,better_err) )
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worse_better_err.sort(key=functools.cmp_to_key(lambda a,b: int((b[0]-b[1])-(a[0]-a[1]))))
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better_wins_count = 0
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for worse_err,better_err in worse_better_err:
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if better_err < 0.9*worse_err:
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better_wins_count = better_wins_count+1
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points_earned += 5.0
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if better_wins_count >= needed_wins:
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break
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incorrect = False
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if (dataX.shape[0] < row_limits[0]) or (dataX.shape[0]>row_limits[1]):
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incorrect = True
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msgs.append(" Invalid number of rows. Should be between {}, found {}".format(row_limits,dataX.shape[0]))
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points_earned = max(0,points_earned-20)
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if (dataX.shape[1] < col_limits[0]) or (dataX.shape[1]>col_limits[1]):
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incorrect = True
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msgs.append(" Invalid number of columns. Should be between {}, found {}".format(col_limits,dataX.shape[1]))
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points_earned = max(0,points_earned-20)
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if better_wins_count < needed_wins:
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incorrect = True
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msgs.append(" Better learner did not exceed worse learner. Expected {}, found {}".format(needed_wins,better_wins_count))
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if not(np.array_equal(same_dataY,dataY)) or not(np.array_equal(same_dataX,dataX)):
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incorrect = True
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msgs.append(" Did not produce the same data with the same seed.\n"+\
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" First dataX:\n{}\n".format(dataX)+\
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" Second dataX:\n{}\n".format(same_dataX)+\
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" First dataY:\n{}\n".format(dataY)+\
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" Second dataY:\n{}\n".format(same_dataY))
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points_earned = max(0,points_earned-20)
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if np.array_equal(diff_dataY,dataY) and np.array_equal(diff_dataX,dataX):
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incorrect = True
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msgs.append(" Did not produce different data with different seeds.\n"+\
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" First dataX:\n{}\n".format(dataX)+\
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" Second dataX:\n{}\n".format(diff_dataX)+\
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" First dataY:\n{}\n".format(dataY)+\
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" Second dataY:\n{}\n".format(diff_dataY))
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points_earned = max(0,points_earned-20)
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if incorrect:
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if group=='author':
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raise IncorrectOutput("Test failed on one or more criteria.\n {}".format('\n'.join(msgs)))
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else:
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inputs_str = " Residuals: {}".format(worse_better_err)
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raise IncorrectOutput("Test failed on one or more output criteria.\n Inputs:\n{}\n Failures:\n{}".format(inputs_str, "\n".join(msgs)))
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else:
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if group != 'author':
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avg_ratio = 0.0
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worse_better_err.sort(key=functools.cmp_to_key(lambda a,b: int(np.sign((b[0]-b[1])-(a[0]-a[1])))))
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for we,be in worse_better_err[:10]:
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avg_ratio += (float(we) - float(be))
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avg_ratio = avg_ratio/10.0
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if group=="best4dt":
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grader.add_performance(np.array([avg_ratio,0]))
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else:
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grader.add_performance(np.array([0,avg_ratio]))
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except Exception as e:
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# Test result: failed
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msg = "Description: {} (group: {})\n".format(description, group)
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# Generate a filtered stacktrace, only showing erroneous lines in student file(s)
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tb_list = tb.extract_tb(sys.exc_info()[2])
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for i in range(len(tb_list)):
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row = tb_list[i]
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tb_list[i] = (os.path.basename(row[0]), row[1], row[2], row[3]) # show only filename instead of long absolute path
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tb_list = [row for row in tb_list if (row[0] == 'gen_data.py')]
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if tb_list:
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msg += "Traceback:\n"
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msg += ''.join(tb.format_list(tb_list)) # contains newlines
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elif 'grading_traceback' in dir(e):
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msg += "Traceback:\n"
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msg += ''.join(tb.format_list(e.grading_traceback))
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msg += "{}: {}".format(e.__class__.__name__, str(e))
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# Report failure result to grader, with stacktrace
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grader.add_result(GradeResult(outcome='failed', points=points_earned, msg=msg))
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raise
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else:
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# Test result: passed (no exceptions)
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grader.add_result(GradeResult(outcome='passed', points=points_earned, msg=None))
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if __name__ == "__main__":
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pytest.main(["-s", __file__])
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@ -0,0 +1,100 @@
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"""
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Test best4 data generator. (c) 2016 Tucker Balch
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||||
Copyright 2018, Georgia Institute of Technology (Georgia Tech)
|
||||
Atlanta, Georgia 30332
|
||||
All Rights Reserved
|
||||
|
||||
Template code for CS 4646/7646
|
||||
|
||||
Georgia Tech asserts copyright ownership of this template and all derivative
|
||||
works, including solutions to the projects assigned in this course. Students
|
||||
and other users of this template code are advised not to share it with others
|
||||
or to make it available on publicly viewable websites including repositories
|
||||
such as github and gitlab. This copyright statement should not be removed
|
||||
or edited.
|
||||
|
||||
We do grant permission to share solutions privately with non-students such
|
||||
as potential employers. However, sharing with other current or future
|
||||
students of CS 7646 is prohibited and subject to being investigated as a
|
||||
GT honor code violation.
|
||||
|
||||
-----do not edit anything above this line---
|
||||
"""
|
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import numpy as np
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import math
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import LinRegLearner as lrl
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import DTLearner as dt
|
||||
from gen_data import best4LinReg, best4DT
|
||||
|
||||
# compare two learners' rmse out of sample
|
||||
def compare_os_rmse(learner1, learner2, X, Y):
|
||||
|
||||
# compute how much of the data is training and testing
|
||||
train_rows = int(math.floor(0.6* X.shape[0]))
|
||||
test_rows = X.shape[0] - train_rows
|
||||
|
||||
# separate out training and testing data
|
||||
train = np.random.choice(X.shape[0], size=train_rows, replace=False)
|
||||
test = np.setdiff1d(np.array(range(X.shape[0])), train)
|
||||
trainX = X[train, :]
|
||||
trainY = Y[train]
|
||||
testX = X[test, :]
|
||||
testY = Y[test]
|
||||
|
||||
# train the learners
|
||||
learner1.addEvidence(trainX, trainY) # train it
|
||||
learner2.addEvidence(trainX, trainY) # train it
|
||||
|
||||
# evaluate learner1 out of sample
|
||||
predY = learner1.query(testX) # get the predictions
|
||||
rmse1 = math.sqrt(((testY - predY) ** 2).sum()/testY.shape[0])
|
||||
|
||||
# evaluate learner2 out of sample
|
||||
predY = learner2.query(testX) # get the predictions
|
||||
rmse2 = math.sqrt(((testY - predY) ** 2).sum()/testY.shape[0])
|
||||
|
||||
return rmse1, rmse2
|
||||
|
||||
def test_code():
|
||||
|
||||
# create two learners and get data
|
||||
lrlearner = lrl.LinRegLearner(verbose = False)
|
||||
dtlearner = dt.DTLearner(verbose = False, leaf_size = 1)
|
||||
X, Y = best4LinReg()
|
||||
|
||||
# compare the two learners
|
||||
rmseLR, rmseDT = compare_os_rmse(lrlearner, dtlearner, X, Y)
|
||||
|
||||
# share results
|
||||
print()
|
||||
print("best4LinReg() results")
|
||||
print(f"RMSE LR : {rmseLR}")
|
||||
print(f"RMSE DT : {rmseDT}")
|
||||
if rmseLR < 0.9 * rmseDT:
|
||||
print("LR < 0.9 DT: pass")
|
||||
else:
|
||||
print("LR >= 0.9 DT: fail")
|
||||
print
|
||||
|
||||
# get data that is best for a random tree
|
||||
lrlearner = lrl.LinRegLearner(verbose = False)
|
||||
dtlearner = dt.DTLearner(verbose = False, leaf_size = 1)
|
||||
X, Y = best4DT()
|
||||
|
||||
# compare the two learners
|
||||
rmseLR, rmseDT = compare_os_rmse(lrlearner, dtlearner, X, Y)
|
||||
|
||||
# share results
|
||||
print()
|
||||
print("best4RT() results")
|
||||
print(f"RMSE LR : {rmseLR}")
|
||||
print(f"RMSE DT : {rmseDT}")
|
||||
if rmseDT < 0.9 * rmseLR:
|
||||
print("DT < 0.9 LR: pass")
|
||||
else:
|
||||
print("DT >= 0.9 LR: fail")
|
||||
print
|
||||
|
||||
if __name__=="__main__":
|
||||
test_code()
|
||||
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Reference in New Issue