83 lines
2.8 KiB
Python
83 lines
2.8 KiB
Python
"""
|
|
Test a learner. (c) 2015 Tucker Balch
|
|
|
|
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---
|
|
"""
|
|
|
|
import numpy as np
|
|
import math
|
|
import LinRegLearner as lrl
|
|
import DTLearner as dtl
|
|
import RTLearner as rtl
|
|
import BagLearner as bgl
|
|
import InsaneLearner as isl
|
|
import sys
|
|
|
|
if __name__=="__main__":
|
|
if len(sys.argv) != 2:
|
|
print("Usage: python testlearner.py <filename>")
|
|
sys.exit(1)
|
|
inf = open(sys.argv[1])
|
|
# data = np.array([list(map(float,s.strip().split(',')[1:]))
|
|
# for s in inf.readlines()[1:]])
|
|
data = np.array([list(map(float,s.strip().split(',')[1:]))
|
|
for s in inf.readlines()[1:]])
|
|
|
|
# compute how much of the data is training and testing
|
|
train_rows = int(0.6* data.shape[0])
|
|
test_rows = data.shape[0] - train_rows
|
|
|
|
# separate out training and testing data
|
|
trainX = data[:train_rows,0:-1]
|
|
trainY = data[:train_rows,-1]
|
|
testX = data[train_rows:,0:-1]
|
|
testY = data[train_rows:,-1]
|
|
|
|
print(f"{testX.shape}")
|
|
print(f"{testY.shape}")
|
|
|
|
# create a learner and train it
|
|
# learner = lrl.LinRegLearner(verbose = True) # create a LinRegLearner
|
|
learner = dtl.DTLearner(verbose = True) # create a LinRegLearner
|
|
# learner = rtl.RTLearner(verbose = True) # create a LinRegLearner
|
|
# learner = bgl.BagLearner(dtl.DTLearner, bags=50) # create a LinRegLearner
|
|
# learner = isl.InsaneLearner()
|
|
learner.addEvidence(trainX, trainY)
|
|
print(learner.author())
|
|
|
|
# evaluate in sample
|
|
predY = learner.query(trainX) # get the predictions
|
|
rmse = math.sqrt(((trainY - predY) ** 2).sum()/trainY.shape[0])
|
|
print()
|
|
print("In sample results")
|
|
print(f"RMSE: {rmse}")
|
|
c = np.corrcoef(predY, y=trainY)
|
|
print(f"corr: {c[0,1]}")
|
|
|
|
# evaluate out of sample
|
|
predY = learner.query(testX) # get the predictions
|
|
rmse = math.sqrt(((testY - predY) ** 2).sum()/testY.shape[0])
|
|
print()
|
|
print("Out of sample results")
|
|
print(f"RMSE: {rmse}")
|
|
c = np.corrcoef(predY, y=testY)
|
|
print(f"corr: {c[0,1]}")
|