# Fit logRegL1 model
model = linear_model.logRegL1(L1_lambda=1.0, maxEvals=400, verbose=1)
model.fit(XBin,yBin)
print("\nlogRegL1 Training error %.3f" % utils.classification_error(model.predict(XBin),yBin))
print("logRegL1 Validation error %.3f" % utils.classification_error(model.predict(XBinValid), yBinValid))
print("# nonZeros: %d" % (model.w != 0).sum())
elif question == "2.3":
# Load Binary and Multi -class data
data = utils.load_dataset("logisticData")
XBin, yBin = data['X'], data['y']
XBinValid, yBinValid = data['Xvalid'], data['yvalid']
# Fit logRegL0 model
model = linear_model.logRegL0(L0_lambda=1.0, maxEvals=400)
model.fit(XBin,yBin)
print("\nTraining error %.3f" % utils.classification_error(model.predict(XBin),yBin))
print("Validation error %.3f" % utils.classification_error(model.predict(XBinValid), yBinValid))
print("# nonZeros: %d" % (model.w != 0).sum())
elif question == "2.5":
data = utils.load_dataset("logisticData")
XBin, yBin = data['X'], data['y']
XBinValid, yBinValid = data['Xvalid'], data['yvalid']
lrl2 = LogisticRegression(fit_intercept=False)
lrl2.fit(XBin,yBin)
print("\nTraining error %.3f" % utils.classification_error(lrl2.predict(XBin),yBin))
print("Validation error %.3f" % utils.classification_error(lrl2.predict(XBinValid), yBinValid))
model = linear_model.logRegL1(L1_lambda=1.0, maxEvals=400)
model.fit(XBin, yBin)
print("\nlogRegL1 Training error %.3f" %
utils.classification_error(model.predict(XBin), yBin))
print("logRegL1 Validation error %.3f" %
utils.classification_error(model.predict(XBinValid), yBinValid))
print("# nonZeros: %d" % (model.w != 0).sum())
elif question == "2.3":
data = utils.load_dataset("logisticData")
XBin, yBin = data['X'], data['y']
XBinValid, yBinValid = data['Xvalid'], data['yvalid']
model = linear_model.logRegL0(L0_lambda=0.00001, maxEvals=400)
model.fit(XBin, yBin)
print("\nTraining error %.3f" %
utils.classification_error(model.predict(XBin), yBin))
print("Validation error %.3f" %
utils.classification_error(model.predict(XBinValid), yBinValid))
print("# nonZeros: %d" % (model.w != 0).sum())
elif question == "2.5":
data = utils.load_dataset("logisticData")
XBin, yBin = data['X'], data['y']
XBinValid, yBinValid = data['Xvalid'], data['yvalid']
# TODO
#L1-Regression with sklearn
XBin, yBin = data['X'], data['y']
XBinValid, yBinValid = data['Xvalid'], data['yvalid']
model = linear_model.logRegL1(lammy=1.0, maxEvals=400)
model.fit(XBin,yBin)
print("\nlogRegL1 Training error %.3f" % utils.classification_error(model.predict(XBin),yBin))
print("logRegL1 Validation error %.3f" % utils.classification_error(model.predict(XBinValid), yBinValid))
print("# nonZeros: %d" % (model.w != 0).sum())
elif question == "2.3":
data = utils.load_dataset("logisticData")
XBin, yBin = data['X'], data['y']
XBinValid, yBinValid = data['Xvalid'], data['yvalid']
model = linear_model.logRegL0(l0_lammy=1.0, maxEvals=400)
model.fit(XBin,yBin)
print("\nTraining error %.3f" % utils.classification_error(model.predict(XBin),yBin))
print("Validation error %.3f" % utils.classification_error(model.predict(XBinValid), yBinValid))
print("# nonZeros: %d" % (model.w != 0).sum())
elif question == "2.5":
data = utils.load_dataset("logisticData")
XBin, yBin = data['X'], data['y']
XBinValid, yBinValid = data['Xvalid'], data['yvalid']
data = utils.load_dataset("logisticData")
XBin, yBin = data['X'], data['y']
XBinValid, yBinValid = data['Xvalid'], data['yvalid']
print("# nonZeros: %d" % (model.w != 0).sum())
if question == "1.2":
# Fit logRegL1 model
model = linear_model.logRegL1(lammy=1.0, maxEvals=400)
model.fit(XBin, yBin)
print("\nlogRegL1 Training error %.3f" %
utils.classification_error(model.predict(XBin), yBin))
print("logRegL1 Validation error %.3f" %
utils.classification_error(model.predict(XBinValid), yBinValid))
print("# nonZeros: %d" % (model.w != 0).sum())
if question == "1.3":
# Fit logRegL0 model
model = linear_model.logRegL0(L0=1.0, maxEvals=400)
model.fit(XBin, yBin)
print("\nTraining error %.3f" %
utils.classification_error(model.predict(XBin), yBin))
print("Validation error %.3f" %
utils.classification_error(model.predict(XBinValid), yBinValid))
print("# nonZeros: %d" % (model.w != 0).sum())
if question == "3":
# Run Q3 given example - Fit One-vs-all Least Squares
model = linear_model.leastSquaresClassifier()
model.fit(XMulti, yMulti)
print("leastSquaresClassifier Training error %.3f" %
utils.classification_error(model.predict(XMulti), yMulti))
