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Change best for LinReg to return optimal data

master
Felix Martin 2020-10-05 17:27:09 -04:00
parent a662e302db
commit 381670705b
2 changed files with 192 additions and 152 deletions
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60
defeat_learners/gen_data.py
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@ -26,27 +26,63 @@ GT ID: 900897987 (replace with your GT ID)
"""
import numpy as np
import pandas as pd
import math
# this function should return a dataset (X and Y) that will work
# better for linear regression than decision trees
def best4LinReg(seed=1489683273):
"""
This function should return a dataset (X and Y) that will work better for
linear regression than decision trees.
We make Y a simple linear combination of X. That will give the Linear
Regression algorithm a very easy time (no RMSE at all) and beat the DT
easily.
"""
np.random.seed(seed)
X = np.zeros((100,2))
Y = np.random.random(size = (100,))*200-100
# Here's is an example of creating a Y from randomly generated
# X with multiple columns
# Y = X[:,0] + np.sin(X[:,1]) + X[:,2]**2 + X[:,3]**3
X = np.random.random(size=(100, 2)) * 200 - 100
Y = X[:, 0] * -2 + X[:, 1] * 3
return X, Y
def best4DT(seed=1489683273):
"""
This function should return a dataset that will work better for decision
trees than linear regression.
"""
# Z = np.append(X, Y.reshape(Y.shape[0], 1), 1)
# pd.DataFrame(Z).to_csv("Z.csv", header=None, index=None)
# np.random.seed(seed)
# X = np.random.random(size=(100, 10))*1000-100
# Y = np.random.random(size=(100,))*1000-100
np.random.seed(seed)
X = np.zeros((100,2))
Y = np.random.random(size = (100,))*200-100
# X_1 = np.random.random(size=(100, 1))*200-100
# X_2 = np.random.random(size=(100, 1))*200-100
# X_3 = np.random.random(size=(100, 1))*200-100
# X_4 = np.random.random(size=(100, 1))*200-100
# X = np.concatenate([X_1, X_2, X_3, X_4], 1)
# XXX: I honestly don't know how to help the DTLearner, yet.
X_1 = np.asarray([i for i in range(1, 101)]).reshape(100, 1)
X_2 = np.asarray([i for i in range(100, 1100, 10)]).reshape(100, 1)
X_3 = np.asarray([i for i in range(200, 300)]).reshape(100, 1)
X_4 = np.asarray([i for i in range(300, 400)]).reshape(100, 1)
X_5 = np.asarray([i for i in range(1, 101)]).reshape(100, 1)
X_6 = np.asarray([i for i in range(1, 101)]).reshape(100, 1)
X_7 = np.asarray([i for i in range(1, 101)]).reshape(100, 1)
X_8 = np.asarray([i for i in range(1, 101)]).reshape(100, 1)
X = np.concatenate([X_1, X_2, X_3, X_4, X_5, X_6, X_7, X_8], 1)
# Y = X[:, 0] * 2 + X[:, 1] * 3
Y = np.random.random(size=(100,)) * 200 - 100
return X, Y
def author():
return 'tb34' #Change this to your user ID
if __name__=="__main__":
def author():
return 'felixm' # Change this to your user ID
if __name__ == "__main__":
print("they call me Tim.")

24
defeat_learners/testbest4.py
View File

@ -28,10 +28,12 @@ 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]))
train_rows = int(math.floor(0.6 * X.shape[0]))
test_rows = X.shape[0] - train_rows
# separate out training and testing data
@ -43,24 +45,25 @@ def compare_os_rmse(learner1, learner2, X, Y):
testY = Y[test]
# train the learners
learner1.addEvidence(trainX, trainY) # train it
learner2.addEvidence(trainX, trainY) # train it
learner1.addEvidence(trainX, trainY) # train it
learner2.addEvidence(trainX, trainY) # train it
# evaluate learner1 out of sample
predY = learner1.query(testX) # get the predictions
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
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)
lrlearner = lrl.LinRegLearner(verbose=False)
dtlearner = dt.DTLearner(verbose=False, leaf_size=1)
X, Y = best4LinReg()
# compare the two learners
@ -78,8 +81,8 @@ def test_code():
print
# get data that is best for a random tree
lrlearner = lrl.LinRegLearner(verbose = False)
dtlearner = dt.DTLearner(verbose = False, leaf_size = 1)
lrlearner = lrl.LinRegLearner(verbose=False)
dtlearner = dt.DTLearner(verbose=False, leaf_size=1)
X, Y = best4DT()
# compare the two learners
@ -96,5 +99,6 @@ def test_code():
print("DT >= 0.9 LR: fail")
print
if __name__=="__main__":
if __name__ == "__main__":
test_code()