Start working on project assess learners.

assess_learners/testlearner.py

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+"""
+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 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:]])
+
+    # 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.addEvidence(trainX, trainY) # train it
+    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]}")