Change best for LinReg to return optimal data
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@ -1,52 +1,88 @@
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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
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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
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or to make it available on publicly viewable websites including repositories
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such as github and gitlab. This copyright statement should not be removed
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or edited.
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We do grant permission to share solutions privately with non-students such
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as potential employers. However, sharing with other current or future
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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 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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"""
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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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||||
|
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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
|
||||
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 pandas as pd
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import math
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def best4LinReg(seed=1489683273):
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"""
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This function should return a dataset (X and Y) that will work better for
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linear regression than decision trees.
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We make Y a simple linear combination of X. That will give the Linear
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Regression algorithm a very easy time (no RMSE at all) and beat the DT
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easily.
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"""
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np.random.seed(seed)
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X = np.random.random(size=(100, 2)) * 200 - 100
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Y = X[:, 0] * -2 + X[:, 1] * 3
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return X, Y
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def best4DT(seed=1489683273):
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"""
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This function should return a dataset that will work better for decision
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trees than linear regression.
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"""
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# Z = np.append(X, Y.reshape(Y.shape[0], 1), 1)
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# pd.DataFrame(Z).to_csv("Z.csv", header=None, index=None)
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# np.random.seed(seed)
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# X = np.random.random(size=(100, 10))*1000-100
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# Y = np.random.random(size=(100,))*1000-100
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np.random.seed(seed)
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# X_1 = np.random.random(size=(100, 1))*200-100
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# X_2 = np.random.random(size=(100, 1))*200-100
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# X_3 = np.random.random(size=(100, 1))*200-100
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# X_4 = np.random.random(size=(100, 1))*200-100
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# X = np.concatenate([X_1, X_2, X_3, X_4], 1)
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# XXX: I honestly don't know how to help the DTLearner, yet.
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X_1 = np.asarray([i for i in range(1, 101)]).reshape(100, 1)
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X_2 = np.asarray([i for i in range(100, 1100, 10)]).reshape(100, 1)
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X_3 = np.asarray([i for i in range(200, 300)]).reshape(100, 1)
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X_4 = np.asarray([i for i in range(300, 400)]).reshape(100, 1)
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X_5 = np.asarray([i for i in range(1, 101)]).reshape(100, 1)
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X_6 = np.asarray([i for i in range(1, 101)]).reshape(100, 1)
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X_7 = np.asarray([i for i in range(1, 101)]).reshape(100, 1)
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X_8 = np.asarray([i for i in range(1, 101)]).reshape(100, 1)
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X = np.concatenate([X_1, X_2, X_3, X_4, X_5, X_6, X_7, X_8], 1)
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# Y = X[:, 0] * 2 + X[:, 1] * 3
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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 'felixm' # 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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@ -1,100 +1,104 @@
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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)
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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
|
||||
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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"""
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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
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from gen_data import best4LinReg, best4DT
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# compare two learners' rmse out of sample
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def compare_os_rmse(learner1, learner2, X, Y):
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# compute how much of the data is training and testing
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train_rows = int(math.floor(0.6* X.shape[0]))
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test_rows = X.shape[0] - train_rows
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# separate out training and testing data
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train = np.random.choice(X.shape[0], size=train_rows, replace=False)
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test = np.setdiff1d(np.array(range(X.shape[0])), train)
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trainX = X[train, :]
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trainY = Y[train]
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testX = X[test, :]
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testY = Y[test]
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# train the learners
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learner1.addEvidence(trainX, trainY) # train it
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learner2.addEvidence(trainX, trainY) # train it
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# evaluate learner1 out of sample
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predY = learner1.query(testX) # get the predictions
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rmse1 = math.sqrt(((testY - predY) ** 2).sum()/testY.shape[0])
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# evaluate learner2 out of sample
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predY = learner2.query(testX) # get the predictions
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rmse2 = math.sqrt(((testY - predY) ** 2).sum()/testY.shape[0])
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return rmse1, rmse2
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def test_code():
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# create two learners and get data
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lrlearner = lrl.LinRegLearner(verbose = False)
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dtlearner = dt.DTLearner(verbose = False, leaf_size = 1)
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X, Y = best4LinReg()
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# compare the two learners
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rmseLR, rmseDT = compare_os_rmse(lrlearner, dtlearner, X, Y)
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# share results
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print()
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print("best4LinReg() results")
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print(f"RMSE LR : {rmseLR}")
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print(f"RMSE DT : {rmseDT}")
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if rmseLR < 0.9 * rmseDT:
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print("LR < 0.9 DT: pass")
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else:
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print("LR >= 0.9 DT: fail")
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print
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# get data that is best for a random tree
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lrlearner = lrl.LinRegLearner(verbose = False)
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dtlearner = dt.DTLearner(verbose = False, leaf_size = 1)
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X, Y = best4DT()
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# compare the two learners
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rmseLR, rmseDT = compare_os_rmse(lrlearner, dtlearner, X, Y)
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# share results
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print()
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print("best4RT() results")
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print(f"RMSE LR : {rmseLR}")
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print(f"RMSE DT : {rmseDT}")
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if rmseDT < 0.9 * rmseLR:
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print("DT < 0.9 LR: pass")
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else:
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print("DT >= 0.9 LR: fail")
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print
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if __name__=="__main__":
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test_code()
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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)
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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
|
||||
GT honor code violation.
|
||||
|
||||
-----do not edit anything above this line---
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"""
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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
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from gen_data import best4LinReg, best4DT
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# compare two learners' rmse out of sample
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def compare_os_rmse(learner1, learner2, X, Y):
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# compute how much of the data is training and testing
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train_rows = int(math.floor(0.6 * X.shape[0]))
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test_rows = X.shape[0] - train_rows
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# separate out training and testing data
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train = np.random.choice(X.shape[0], size=train_rows, replace=False)
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test = np.setdiff1d(np.array(range(X.shape[0])), train)
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trainX = X[train, :]
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trainY = Y[train]
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testX = X[test, :]
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testY = Y[test]
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# train the learners
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learner1.addEvidence(trainX, trainY) # train it
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learner2.addEvidence(trainX, trainY) # train it
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# evaluate learner1 out of sample
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predY = learner1.query(testX) # get the predictions
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rmse1 = math.sqrt(((testY - predY) ** 2).sum()/testY.shape[0])
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# evaluate learner2 out of sample
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predY = learner2.query(testX) # get the predictions
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rmse2 = math.sqrt(((testY - predY) ** 2).sum()/testY.shape[0])
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return rmse1, rmse2
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def test_code():
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# create two learners and get data
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lrlearner = lrl.LinRegLearner(verbose=False)
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dtlearner = dt.DTLearner(verbose=False, leaf_size=1)
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X, Y = best4LinReg()
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# compare the two learners
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rmseLR, rmseDT = compare_os_rmse(lrlearner, dtlearner, X, Y)
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# share results
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print()
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print("best4LinReg() results")
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print(f"RMSE LR : {rmseLR}")
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print(f"RMSE DT : {rmseDT}")
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if rmseLR < 0.9 * rmseDT:
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print("LR < 0.9 DT: pass")
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else:
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print("LR >= 0.9 DT: fail")
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print
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# get data that is best for a random tree
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lrlearner = lrl.LinRegLearner(verbose=False)
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dtlearner = dt.DTLearner(verbose=False, leaf_size=1)
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X, Y = best4DT()
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# compare the two learners
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rmseLR, rmseDT = compare_os_rmse(lrlearner, dtlearner, X, Y)
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# share results
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print()
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print("best4RT() results")
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print(f"RMSE LR : {rmseLR}")
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print(f"RMSE DT : {rmseDT}")
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if rmseDT < 0.9 * rmseLR:
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print("DT < 0.9 LR: pass")
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else:
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print("DT >= 0.9 LR: fail")
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print
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if __name__ == "__main__":
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test_code()
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