model = linear_model.logReg(maxEvals=400) model.fit(XBin, yBin) print("\nlogReg Training error %.3f" % utils.classification_error(model.predict(XBin), yBin)) print("logReg Validation error %.3f" % utils.classification_error(model.predict(XBinValid), yBinValid)) print("# nonZeros: %d" % (model.w != 0).sum()) elif question == "2.1": data = utils.load_dataset("logisticData") XBin, yBin = data['X'], data['y'] XBinValid, yBinValid = data['Xvalid'], data['yvalid'] model = linear_model.logRegL2(lammy=1.0, maxEvals=400) model.fit(XBin, yBin) print("\nlogRegL2 Training error %.3f" % utils.classification_error(model.predict(XBin), yBin)) print("logRegL2 Validation error %.3f" % utils.classification_error(model.predict(XBinValid), yBinValid)) print("# nonZeros: %d" % (model.w != 0).sum()) elif question == "2.2": data = utils.load_dataset("logisticData") print(type(data)) XBin, yBin = data['X'], data['y'] XBinValid, yBinValid = data['Xvalid'], data['yvalid'] model = linear_model.logRegL1(L1_lambda=1.0, maxEvals=400)
XBinValid, yBinValid = data['Xvalid'], data['yvalid'] model = linear_model.logReg(maxEvals=400, verbose=1) model.fit(XBin,yBin) print("\nlogReg Training error %.3f" % utils.classification_error(model.predict(XBin), yBin)) print("logReg Validation error %.3f" % utils.classification_error(model.predict(XBinValid), yBinValid)) print("# nonZeros: %d" % (model.w != 0).sum()) elif question == "2.1": data = utils.load_dataset("logisticData") XBin, yBin = data['X'], data['y'] XBinValid, yBinValid = data['Xvalid'], data['yvalid'] # Fit logRegL2 model model = linear_model.logRegL2(lammy=1.0, maxEvals=400, verbose=1) model.fit(XBin,yBin) print("\nlogRegL2 Training error %.3f" % utils.classification_error(model.predict(XBin), yBin)) print("logRegL2 Validation error %.3f" % utils.classification_error(model.predict(XBinValid), yBinValid)) print("# nonZeros: %d" % (model.w != 0).sum()) elif question == "2.2": # Load Binary and Multi -class data data = utils.load_dataset("logisticData") XBin, yBin = data['X'], data['y'] XBinValid, yBinValid = data['Xvalid'], data['yvalid'] # Fit logRegL1 model model = linear_model.logRegL1(L1_lambda=1.0, maxEvals=400, verbose=1) model.fit(XBin,yBin)
model = linear_model.logReg(maxEvals=400) model.fit(XBin, yBin) print("\nlogReg Training error %.3f" % utils.classification_error(model.predict(XBin), yBin)) print("logReg Validation error %.3f" % utils.classification_error(model.predict(XBinValid), yBinValid)) print("# nonZeros: %d" % (model.w != 0).sum()) elif question == "2.1": data = utils.load_dataset("logisticData") XBin, yBin = data['X'], data['y'] XBinValid, yBinValid = data['Xvalid'], data['yvalid'] model = linear_model.logRegL2(maxEvals=400, l=1.0) model.fit(XBin, yBin) print("\nlogRegL2 Training error %.3f" % utils.classification_error(model.predict(XBin), yBin)) print("logRegL2 Validation error %.3f" % utils.classification_error(model.predict(XBinValid), yBinValid)) print("# nonZeros: %d" % (model.w != 0).sum()) elif question == "2.2": data = utils.load_dataset("logisticData") XBin, yBin = data['X'], data['y'] XBinValid, yBinValid = data['Xvalid'], data['yvalid'] model = linear_model.logRegL1(l=1.0, maxEvals=400) model.fit(XBin, yBin)