81 lines
2.6 KiB
Python
81 lines
2.6 KiB
Python
"""
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Test a learner. (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
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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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"""
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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 dtl
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import sys
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if __name__=="__main__":
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if len(sys.argv) != 2:
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print("Usage: python testlearner.py <filename>")
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sys.exit(1)
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inf = open(sys.argv[1])
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# data = np.array([list(map(float,s.strip().split(',')[1:]))
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# for s in inf.readlines()[1:]])
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data = np.array([list(map(float,s.strip().split(',')[1:]))
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for s in inf.readlines()])
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# XXX: Get rid of some rows and columns for easier development.
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# XXX: Remove later for testing!
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# data = data[:10,5:]
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# compute how much of the data is training and testing
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train_rows = int(0.6* data.shape[0])
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test_rows = data.shape[0] - train_rows
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# separate out training and testing data
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trainX = data[:train_rows,0:-1]
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trainY = data[:train_rows,-1]
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testX = data[train_rows:,0:-1]
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testY = data[train_rows:,-1]
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print(f"{testX.shape}")
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print(f"{testY.shape}")
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# create a learner and train it
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# learner = lrl.LinRegLearner(verbose = True) # create a LinRegLearner
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learner = dtl.DTLearner(verbose = True) # create a LinRegLearner
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learner.addEvidence(trainX, trainY)
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print(learner.author())
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# evaluate in sample
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predY = learner.query(trainX) # get the predictions
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rmse = math.sqrt(((trainY - predY) ** 2).sum()/trainY.shape[0])
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print()
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print("In sample results")
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print(f"RMSE: {rmse}")
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c = np.corrcoef(predY, y=trainY)
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print(f"corr: {c[0,1]}")
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# evaluate out of sample
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predY = learner.query(testX) # get the predictions
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rmse = math.sqrt(((testY - predY) ** 2).sum()/testY.shape[0])
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print()
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print("Out of sample results")
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print(f"RMSE: {rmse}")
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c = np.corrcoef(predY, y=testY)
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print(f"corr: {c[0,1]}")
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