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models.py
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2070 lines (1893 loc) · 76.7 KB
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from sklearn.linear_model import SGDRegressor, PassiveAggressiveRegressor
from util import regression_performance, plot_hyperparameter, \
learning_curve, record_results
from sklearn.model_selection import KFold
from controls import print_controls, ctrls
import numpy as np
import pandas as pd
import os
import copy
import traceback
"""
Steps to evaluate a ML technique:
1. Get the data!
2. Visualize the data through plots.
3. Train the model.
4. Evaluate the model.
-- Use cross validation (if validation data is not available)
to tune hyperparameters.
-- Look out for overfitting and underfitting.
-- Repeat Step 3 to find the best model.
"""
#######################################################################
# Stochastic Gradient Descent
#######################################################################
def run_multiple_SGD(X, y, **kwargs):
"""
Runs Stochastic Gradient Descent algorithm multiple times
using different seeds on the regression data.
Parameters
--------------------
X -- tuple of length 3,
1. numpy matrix of shape (n_1,d), features for training
2. numpy matrix of shape (n_2,d), features for validation
3. numpy matrix of shape (n_3,d), features for test
y -- tuple of length 3,
1. numpy matrix of shape (n_1,1), targets for training
2. numpy matrix of shape (n_2,1), targets for validation
3. numpy matrix of shape (n_3,1), targets for test
"""
total_training_instances = len(X[0])
controls = ctrls()
if 'parameter' not in kwargs:
perfusion_param = None
else:
perfusion_param = kwargs.pop('parameter')
if 'patch_radius' not in kwargs:
patch_radius = None
else:
patch_radius = kwargs.pop('patch_radius')
seeds = raw_input('Enter seeds (separated by space): ')
seeds = [int(s) for s in seeds.split()]
total_training_instances = len(X[0])
batch_size = 100
# Shuffle data
indices = np.arange(0, total_training_instances)
np.random.seed(seeds[0])
np.random.shuffle(indices)
train_data = np.matrix([np.asarray(X[0])[i] for i in indices])
outcomes = np.matrix([[y[0].A1[i]] for i in indices])
best_penalty = 'l2'
print 'Choose a penalty (regularization term) to be used.'
print '1: none'
print '2: l2'
print '3: l1'
print '4: elasticnet'
pick_penalty = raw_input('Enter value (default: 2): ')
if pick_penalty == controls['Quit']:
return
if pick_penalty == '1':
best_penalty = 'none'
elif pick_penalty == '3':
best_penalty = 'l1'
elif pick_penalty == '4':
best_penalty = 'elasticnet'
print 'Enter range of alpha term (default 0.0001) for penalty ' + best_penalty
start = raw_input('\tEnter lower bound (inclusive) of range: ')
end = raw_input('\tEnter upper bound (exclusive) of range: ')
incr = raw_input('\tEnter increment: ')
alpha_range = np.arange(float(start), float(end), float(incr))
min_test_err = None
best_alpha = None
for a in alpha_range:
avg_test_errs = np.array([])
print "Testing alpha " + str(a)
for seed in seeds[1:]:
model = SGDRegressor(
penalty=best_penalty,
alpha = a,
random_state=np.random.RandomState(seed)
)
for i in xrange(0, total_training_instances, batch_size):
data = train_data[i:i+batch_size]
out = outcomes[i:i+batch_size]
model = model.partial_fit(data, out.A1)
y_pred = model.predict(X[2])
avg_test_errs = np.append(avg_test_errs,[
regression_performance(y[2].A1,y_pred,'rms')
])
err = avg_test_errs.mean()
if min_test_err is None or err < min_test_err:
min_test_err = err
best_alpha = a
best_learn = 'optimal' # Default
# 'none', 'l2', 'l1', or 'elasticnet'
print 'Choose a learning rate schedule to be used.'
print '1: Constant'
print '2: Optimal'
print '3: Inverse Scaling'
pick_learn = raw_input('Enter value (default: 2): ')
if pick_learn == controls['Quit']:
return
if pick_learn == '1':
best_learn = 'constant'
elif pick_learn == '3':
best_learn = 'invscaling'
print 'Enter range of initial learning rate (default 0.01) '
start = raw_input('\tEnter lower bound (inclusive) of range: ')
end = raw_input('\tEnter upper bound (exclusive) of range: ')
incr = raw_input('\tEnter increment: ')
learn_range = np.arange(float(start), float(end), float(incr))
min_test_err = None
best_learnRate = None
for a in learn_range:
avg_test_errs = np.array([])
print "Testing initial rate" + str(a)
for seed in seeds[1:]:
model = SGDRegressor(
learning_rate=best_learn,
eta0 = a,
random_state=np.random.RandomState(seed)
)
for i in xrange(0, total_training_instances, batch_size):
data = train_data[i:i+batch_size]
out = outcomes[i:i+batch_size]
model = model.partial_fit(data, out.A1)
y_pred = model.predict(X[2])
avg_test_errs = np.append(avg_test_errs,[
regression_performance(
y[2].A1,
y_pred,
'rms'
)])
err = avg_test_errs.mean()
if min_test_err is None or err < min_test_err:
min_test_err = err
best_learnRate = a
best_powT = 0.25
if best_learn == 'invscaling':
print 'Enter range of exponent for inverse scaling (default 0.25) '
start = raw_input('\tEnter lower bound (inclusive) of range: ')
end = raw_input('\tEnter upper bound (exclusive) of range: ')
incr = raw_input('\tEnter increment: ')
exp_range = np.arange(float(start), float(end), float(incr))
min_test_err = None
best_powT = None
for a in exp_range:
print "Testing exponent " + str(a)
avg_test_errs = np.array([])
for seed in seeds[1:]:
model = SGDRegressor(
learning_rate=best_learn,
eta0 = a,
power_t = a,
random_state=np.random.RandomState(seed)
)
for i in xrange(0, total_training_instances, batch_size):
data = train_data[i:i+batch_size]
out = outcomes[i:i+batch_size]
model = model.partial_fit(data, out.A1)
y_pred = model.predict(X[2])
avg_test_errs = np.append(avg_test_errs,[
regression_performance(
y[2].A1,
y_pred,
'rms'
)])
err = avg_test_errs.mean()
if min_test_err is None or err < min_test_err:
min_test_err = err
best_powT = a
# default = 0.1
print 'Enter range of epsilon.'
start = raw_input('\tEnter lower bound (inclusive) of range: ')
end = raw_input('\tEnter upper bound (exclusive) of range: ')
incr = raw_input('\tEnter increment: ')
epsilon_range = np.arange(float(start), float(end), float(incr))
min_test_err = None
best_epsilon = None
for e in epsilon_range:
avg_test_errs = np.array([])
print "Testing epsilon value " + str(e)
for seed in seeds[1:]:
model = SGDRegressor(
epsilon=e,
random_state=np.random.RandomState(seed)
)
for i in xrange(0, total_training_instances, batch_size):
data = train_data[i:i + batch_size]
out = outcomes[i:i + batch_size]
model = model.partial_fit(data, out.A1)
y_pred = model.predict(X[2])
avg_test_errs = np.append(avg_test_errs, [
regression_performance(y[2].A1, y_pred, 'rms')
])
err = avg_test_errs.mean()
if min_test_err is None or err < min_test_err:
min_test_err = err
best_epsilon = e
print 'Enter range of epochs.'
start = raw_input('\tEnter lower bound (inclusive) of range: ')
end = raw_input('\tEnter upper bound (exclusive) of range: ')
incr = raw_input('\tEnter increment: ')
epoch_range = np.arange(int(start), int(end), int(incr))
min_test_err = None
best_epochs = None
for e in epoch_range:
avg_test_errs = np.array([])
print "Testing epoch number " + str(e)
for seed in seeds[1:]:
model = PassiveAggressiveRegressor(
random_state=np.random.RandomState(seed)
)
for rnd in xrange(e):
for i in xrange(0, total_training_instances, batch_size):
data = train_data[i:i + batch_size]
out = outcomes[i:i + batch_size]
model = model.partial_fit(data, out.A1)
y_pred = model.predict(X[2])
avg_test_errs = np.append(avg_test_errs, [
regression_performance(y[2].A1, y_pred, 'rms')
])
err = avg_test_errs.mean()
if min_test_err is None or err < min_test_err:
min_test_err = err
best_epochs = e
attributes = [
'Perfusion Parameter',
'Model',
'Patch Radius',
'Batch Size',
'Total Number of Examples Trained',
'Trial',
'Training RMSE',
'Test RMSE',
'Training R^2 Score',
'Test R^2 Score',
'Penalty (Regularization)',
'Alpha',
'Epsilon',
'Learning Rate',
'eta0', # The initial learning rate [default 0.01].
'Exponent (inv scaling)', # The exponent for inverse scaling learning rate [default 0.25].
'Epochs',
'Fit Intercept?',
'Shuffle?',
'Loss Function',
'Warm Start?',
'Average'
] ## might add/ change one of these attributes
# Pick values for parameters
results = {}
for a in attributes:
results[a] = []
trials = raw_input('Enter number of trials: ')
trials = int(trials)
indices = np.arange(0, total_training_instances)
np.random.shuffle(indices)
train_data = np.matrix([np.asarray(X[0])[i] for i in indices])
outcomes = np.matrix([[y[0].A1[i]] for i in indices])
np.random.seed(None)
print "Begin multiple trials test"
# Create model with tuned parameters
prev = -1
for trial in xrange(trials):
if int(((trial*100.0)/trials)) % 10 == 0 and int(((trial*100.0)/trials)) != prev:
#display a note every 10% so user knows program didn't freeze
prev = int(((trial*100.0)/trials))
print "Trials " + str(prev) + "% complete"
model = SGDRegressor(
penalty=best_penalty,
alpha=best_alpha,
epsilon=best_epsilon,
fit_intercept=True,
n_iter=1, # Not applicable for partial fit
shuffle=True,
random_state=None,
loss='epsilon_insensitive',
average=False,
learning_rate=best_learn,
eta0 = best_learnRate,
power_t=best_powT
)
for rnd in xrange(best_epochs):
for i in xrange(0, total_training_instances, batch_size):
data = train_data[i:i + batch_size]
out = outcomes[i:i + batch_size]
model = model.partial_fit(data, out.A1)
results['Perfusion Parameter'].append(perfusion_param)
results['Model'].append('SGD')
results['Patch Radius'].append(patch_radius)
results['Batch Size'].append(batch_size)
results['Penalty (Regularization)'].append(best_penalty)
results['Alpha'].append(best_alpha)
results['Average'].append(False)
results['Epsilon'].append(best_epsilon)
results['Fit Intercept?'].append(True)
results['Shuffle?'].append(True)
results['Epochs'].append(best_epochs)
results['Loss Function'].append('epsilon_insensitive')
results['Warm Start?'].append(False)
results['Learning Rate'].append(best_learn)
results['Trial'].append(trial + 1)
results['eta0'].append(best_learnRate)
results['Exponent (inv scaling)'].append(best_powT)
results['Total Number of Examples Trained'].append(
total_training_instances
)
# Compute training performance
y_pred = model.predict(X[0])
overall_train_perf = regression_performance(y[0].A1,y_pred,'rms')
results['Training RMSE'].append(overall_train_perf)
overall_train_perf = regression_performance(y[0].A1,y_pred,'r2-score')
results['Training R^2 Score'].append(overall_train_perf)
# Compute test performance using test data
y_pred = model.predict(X[2])
test_perf = regression_performance(y[2].A1,y_pred,'rms')
results['Test RMSE'].append(test_perf)
test_perf = regression_performance(y[2].A1,y_pred,'r2-score')
results['Test R^2 Score'].append(test_perf)
record_results(results, attributes, **{
'title': 'trial results'
})
def run_SGD(X, y, **kwargs):
"""
Runs Stochastic Gradient Descent on the regression data.
Parameters
--------------------
X -- tuple of length 3,
1. numpy matrix of shape (n_1,d), features for training
2. numpy matrix of shape (n_2,d), features for validation
3. numpy matrix of shape (n_3,d), features for test
y -- tuple of length 3,
1. numpy matrix of shape (n_1,1), targets for training
2. numpy matrix of shape (n_2,1), targets for validation
3. numpy matrix of shape (n_3,1), targets for test
"""
print 'Examining Stochastic Gradient Descent for Linear Regression...'
total_training_instances = len(X[0])
controls = ctrls()
if 'parameter' not in kwargs:
perfusion_param = None
else:
perfusion_param = kwargs.pop('parameter')
if 'patch_radius' not in kwargs:
patch_radius = None
else:
patch_radius = kwargs.pop('patch_radius')
attributes = [
'Perfusion Parameter',
'Model',
'Patch Radius',
'Batch Size',
'Total Number of Examples Trained',
'Training RMSE',
'Test RMSE',
'Training R^2 Score',
'Test R^2 Score',
'Penalty (Regularization)',
'Alpha',
'Epsilon',
'Learning Rate',
'eta0', # The initial learning rate [default 0.01].
'Exponent (inv scaling)', # The exponent for inverse scaling learning rate [default 0.25].
'Fit Intercept?',
'Shuffle?',
'Random Seed',
'Loss Function',
'Warm Start?',
'Average'
]
# Pick values for parameters
results = {}
for a in attributes:
results[a] = []
batch_size = 100 # Default
size = raw_input('Enter batch size (default: ' +
str(batch_size) + '): ')
if size == controls['Quit']:
return
if size != '':
batch_size = int(size)
# Enter a seed in order to reproduce results (even if the shuffle option
# is not set to True)
seed = None # Default
pick_seed = raw_input('Enter seed of random number generator to '
'shuffle: ')
if pick_seed == controls['Quit']:
return
if pick_seed != '':
seed = int(pick_seed)
# Shuffle data
indices = np.arange(0, total_training_instances)
np.random.seed(seed)
np.random.shuffle(indices)
train_data = np.matrix([np.asarray(X[0])[i] for i in indices])
outcomes = np.matrix([[y[0].A1[i]] for i in indices])
best_penalty = 'l2' # Default
# 'none', 'l2', 'l1', or 'elasticnet'
print 'Choose a penalty (regularization term) to be used.'
print '1: none'
print '2: l2'
print '3: l1'
print '4: elasticnet'
pick_penalty = raw_input('Enter value (default: 2): ')
if pick_penalty == controls['Quit']:
return
if pick_penalty == '1':
best_penalty = 'none'
elif pick_penalty == '3':
best_penalty = 'l1'
elif pick_penalty == '4':
best_penalty = 'elasticnet'
print 'Tuning regularization penalty with alpha term...'
while True:
comp = raw_input('Compare errors for range of alpha values? [Y/n] ')
if comp == 'Y':
start = raw_input('Enter lower bound (inclusive) of range: ')
end = raw_input('Enter upper bound (exclusive) of range: ')
incr = raw_input('Enter increment: ')
alpha_range = np.arange(float(start), float(end), float(incr))
avg_train_perf = []
avg_val_perf = []
for a in alpha_range:
train_perf = []
val_perf = []
# Use cross validation to tune parameter
kf = KFold()
for train, val in kf.split(train_data):
X_train, X_val = train_data[train], train_data[val]
y_train, y_val = outcomes[train].A1, outcomes[val].A1
model = SGDRegressor(
penalty=best_penalty,
alpha = a,
random_state=np.random.RandomState(seed)
)
for i in xrange(0, len(X_train), batch_size):
model = model.partial_fit(
X_train[i:i + batch_size],
y_train[i:i + batch_size]
)
y_pred = model.predict(X_train)
train_perf.append(regression_performance(
y_train,
y_pred,
'rms'
))
y_pred = model.predict(X_val)
val_perf.append(regression_performance(
y_val,
y_pred,
'rms'
))
avg_train_perf.append(
np.sum(train_perf) * 1.0 / len(train_perf)
)
avg_val_perf.append(
np.sum(val_perf) * 1.0 / len(val_perf)
)
plot_hyperparameter(
alpha_range,
avg_train_perf,
avg_val_perf,
**{
'parameter' : r'Regularization l2 with multiplier $alpha$',
'score' : 'Root Mean Squared Error'
})
elif comp == controls['Quit']:
return
else:
break
best_alpha = 0.0001 # Default
alpha_pick = raw_input('Choose value of alpha (default: ' + str(best_alpha) + '): ')
if alpha_pick != '':
best_alpha = float(alpha_pick)
best_learn = 'optimal' # Default
# 'none', 'l2', 'l1', or 'elasticnet'
print 'Choose a learning rate schedule to be used.'
print '1: Constant'
print '2: Optimal'
print '3: Inverse Scaling'
pick_learn = raw_input('Enter value (default: 2): ')
if pick_learn == controls['Quit']:
return
if pick_learn == '1':
best_learn = 'constant'
elif pick_learn == '3':
best_learn = 'invscaling'
print 'Tuning initial learning rate for ' + best_learn + '...'
while True:
comp = raw_input('Compare errors for range of initial learning rates (default 0.01)? [Y/n] ')
if comp == 'Y':
start = raw_input('Enter lower bound (inclusive) of range: ')
end = raw_input('Enter upper bound (exclusive) of range: ')
incr = raw_input('Enter increment: ')
learnRate_range = np.arange(float(start), float(end), float(incr))
avg_train_perf = []
avg_val_perf = []
for a in learnRate_range:
train_perf = []
val_perf = []
print "Testing initial learn rate " + str(a)
# Use cross validation to tune parameter
kf = KFold()
for train, val in kf.split(train_data):
X_train, X_val = train_data[train], train_data[val]
y_train, y_val = outcomes[train].A1, outcomes[val].A1
model = SGDRegressor(
penalty=best_penalty,
alpha = best_alpha,
learning_rate=best_learn,
eta0 = a,
random_state=np.random.RandomState(seed)
)
for i in xrange(0, len(X_train), batch_size):
model = model.partial_fit(
X_train[i:i + batch_size],
y_train[i:i + batch_size]
)
y_pred = model.predict(X_train)
train_perf.append(regression_performance(
y_train,
y_pred,
'rms'
))
y_pred = model.predict(X_val)
val_perf.append(regression_performance(
y_val,
y_pred,
'rms'
))
avg_train_perf.append(
np.sum(train_perf) * 1.0 / len(train_perf)
)
avg_val_perf.append(
np.sum(val_perf) * 1.0 / len(val_perf)
)
plot_hyperparameter(
learnRate_range,
avg_train_perf,
avg_val_perf,
**{
'parameter' : r'Learning Rate type with initial learning rate $eta0$',
'score' : 'Root Mean Squared Error'
})
elif comp == controls['Quit']:
return
else:
break
best_learnRate = 0.01 # Default
learnrate_PICK = raw_input('Choose value of initial learn rate for ' + best_learn + '(default: ' +
str(best_learnRate) + '): ')
if learnrate_PICK == controls['Quit']:
return
if learnrate_PICK != '':
best_learnRate = float(learnrate_PICK)
best_powT = 0.25
if best_learn == 'invscaling':
print 'Tuning exponent for inverse scaling learning rate...'
while True:
comp = raw_input('Compare errors for range of exponents for inv scaling? [Y/n] ')
if comp == 'Y':
start = raw_input('Enter lower bound (inclusive) of range: ')
end = raw_input('Enter upper bound (exclusive) of range: ')
incr = raw_input('Enter increment: ')
exponent_range = np.arange(float(start), float(end), float(incr))
avg_train_perf = []
avg_val_perf = []
for a in exponent_range:
train_perf = []
val_perf = []
#print "Testing inv scaling exponent: " + str(a)
# Use cross validation to tune parameter
kf = KFold()
for train, val in kf.split(train_data):
X_train, X_val = train_data[train], train_data[val]
y_train, y_val = outcomes[train].A1, outcomes[val].A1
model = SGDRegressor(
penalty=best_penalty,
alpha = best_alpha,
learning_rate=best_learn,
eta0 = best_learnRate,
power_t=a,
random_state=np.random.RandomState(seed)
)
for i in xrange(0, len(X_train), batch_size):
model = model.partial_fit(
X_train[i:i + batch_size],
y_train[i:i + batch_size]
)
y_pred = model.predict(X_train)
train_perf.append(regression_performance(
y_train,
y_pred,
'rms'
))
y_pred = model.predict(X_val)
val_perf.append(regression_performance(
y_val,
y_pred,
'rms'
))
avg_train_perf.append(
np.sum(train_perf) * 1.0 / len(train_perf)
)
avg_val_perf.append(
np.sum(val_perf) * 1.0 / len(val_perf)
)
plot_hyperparameter(
exponent_range,
avg_train_perf,
avg_val_perf,
**{
'parameter' : r'Inv Scaling Learn with exponent $pow_t$',
'score' : 'Root Mean Squared Error'
})
elif comp == controls['Quit']:
return
else:
break
powT_PICK = raw_input('Choose value of exponent for inv scaling learn(default: ' +
str(best_powT) + '): ')
if powT_PICK == controls['Quit']:
return
if powT_PICK != '':
best_powT = float(powT_PICK)
print 'Tuning epsilon (threshold)...'
while True:
comp = raw_input('Compare errors for range of epsilon values? [Y/n] ')
if comp == 'Y':
start = raw_input('Enter lower bound (inclusive) of range: ')
end = raw_input('Enter upper bound (exclusive) of range: ')
incr = raw_input('Enter increment: ')
epsilon_range = np.arange(float(start), float(end), float(incr))
avg_train_perf = []
avg_val_perf = []
for e in epsilon_range:
train_perf = []
val_perf = []
#print e
# Use cross validation to tune parameter
kf = KFold()
for train, val in kf.split(train_data):
X_train, X_val = train_data[train], train_data[val]
y_train, y_val = outcomes[train].A1, outcomes[val].A1
model = SGDRegressor(
epsilon=e,
random_state=np.random.RandomState(seed)
)
for i in xrange(0, len(X_train), batch_size):
model = model.partial_fit(
X_train[i:i + batch_size],
y_train[i:i + batch_size]
)
y_pred = model.predict(X_train)
train_perf.append(regression_performance(
y_train,
y_pred,
'rms'
))
y_pred = model.predict(X_val)
val_perf.append(regression_performance(
y_val,
y_pred,
'rms'
))
avg_train_perf.append(
np.sum(train_perf) * 1.0 / len(train_perf)
)
avg_val_perf.append(
np.sum(val_perf) * 1.0 / len(val_perf)
)
plot_hyperparameter(
epsilon_range,
avg_train_perf,
avg_val_perf,
**{
'parameter' : r'Epsilon $\epsilon$',
'score' : 'Root Mean Squared Error'
})
elif comp == controls['Quit']:
return
else:
break
best_epsilon = 0.1 # Default
epsilon_pick = raw_input('Choose value of epsilon (default: ' +
str(best_epsilon) + '): ')
if epsilon_pick == controls['Quit']:
return
if epsilon_pick != '':
best_epsilon = float(epsilon_pick)
# shuffle
shuffle = True # Default
shuffle_pick = raw_input('Shuffle after each epoch (default: Y)? [Y/n] ')
if shuffle_pick == controls['Quit']:
return
if shuffle_pick == 'n':
shuffle = False
# fit_intercept
intercept = True # Default
pick_intercept = raw_input('Fit intercept (default: Y)? [Y/n] ')
if pick_intercept == controls['Quit']:
return
if pick_intercept == 'n':
intercept = False
# average
sgd_average = False # Default
avg_pick = raw_input('Take average of SGD weights (default: N)? [Y/n] ')
if avg_pick == controls['Quit']:
return
if avg_pick == 'Y':
sgd_average = True
# loss: 'squared_loss', 'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'
loss = 'squared_loss' # Default
print 'Choose a loss function to be used.'
print '1: Squared Loss'
print '2: Huber'
print '3: Epsilon Insensitive'
print '4: Squared Epsilon Insensitive'
pick_loss = raw_input('Enter value (default: 1): ')
if pick_loss == controls['Quit']:
return
elif pick_loss == '2':
loss = 'huber'
elif pick_loss == '3':
loss = 'epsilon_insensitive'
elif pick_loss == '4':
loss = 'squared_epsilon_insensitive'
##########################################################################
# Observe performance of model for each batch that has been trained on.
##########################################################################
resp = raw_input('See incremental performance? [Y/n] ')
if resp == controls['Quit']:
return
if resp == 'Y':
# Create model with tuned parameters
np.random.seed(seed)
model = SGDRegressor(
penalty=best_penalty,
alpha=best_alpha,
epsilon=best_epsilon,
fit_intercept=intercept,
n_iter=1, # Not applicable for partial fit
shuffle=shuffle,
random_state=seed,
loss=loss,
average=sgd_average,
learning_rate=best_learn,
eta0 = best_learnRate,
power_t=best_powT
)
# Observe how the model performs with increasingly more data
current_total_data = 0
incremental_sizes = []
prev = -1
for i in xrange(0, total_training_instances, batch_size):
#print i
if int(((i*100.0)/total_training_instances)) % 10 == 0 and int(((i*100.0)/total_training_instances)) != prev:
#display a note every 10% so user knows program didn't freeze
prev = int(((i*100.0)/total_training_instances))
print str(prev) + "% complete"
data = train_data[i:i + batch_size]
out = outcomes[i:i + batch_size]
model = model.partial_fit(data, out.A1)
current_total_data += len(data)
incremental_sizes.append(current_total_data)
results['Perfusion Parameter'].append(perfusion_param)
results['Model'].append('SGD')
results['Patch Radius'].append(patch_radius)
results['Batch Size'].append(batch_size)
results['Penalty (Regularization)'].append(best_penalty)
results['Alpha'].append(best_alpha)
results['Average'].append(sgd_average)
results['Epsilon'].append(best_epsilon)
results['Fit Intercept?'].append(intercept)
results['Shuffle?'].append(shuffle)
results['Random Seed'].append(seed)
results['Loss Function'].append(loss)
results['Warm Start?'].append(False)
results['Learning Rate'].append(best_learn)
results['eta0'].append(best_learnRate)
results['Exponent (inv scaling)'].append(best_powT)
results['Total Number of Examples Trained'].append(
current_total_data
)
# Compute training performance
y_pred = model.predict(X[0])
overall_train_perf = regression_performance(
y[0].A1,
y_pred,
'rms'
)
results['Training RMSE'].append(overall_train_perf)
overall_train_perf = regression_performance(
y[0].A1,
y_pred,
'r2-score'
)
results['Training R^2 Score'].append(overall_train_perf)
# Compute test performance using test data
y_pred = model.predict(X[2])
test_perf = regression_performance(
y[2].A1,
y_pred,
'rms'
)
results['Test RMSE'].append(test_perf)
test_perf = regression_performance(
y[2].A1,
y_pred,
'r2-score'
)
results['Test R^2 Score'].append(test_perf)
record_results(results, attributes, **{
'title': 'incremental results'
})
learning_curve(
incremental_sizes,
results['Training RMSE'],
results['Test RMSE'],
**{ 'score' : 'Root Mean Squared Error' }
)
final_result = {}
for attr in results:
final_result[attr] = [results[attr][-1]]
final_result['Epochs'] = [1]
attributes.append('Epochs')
record_results(final_result, attributes, **{
'title': 'final results'
})
# Print summary
print '================'
print 'SUMMARY'
print '================'
print 'Perfusion Parameter: ' + perfusion_param
print 'Patch Radius : ' + str(patch_radius)
print 'Model : Stochastic Gradient Descent (SGD)'
print 'Batch Size : ' + str(batch_size)
print '----------------'
print 'Model Parameters'
print '----------------'
print 'Penalty(Regulariz.): ' + best_penalty
print 'Alpha : ' + str(best_alpha)
print 'Epsilon : ' + str(best_epsilon)
print 'Fit Intercept : ' + str(intercept)
print 'Shuffle : ' + str(shuffle)
print 'Random Seed : ' + str(seed)
print 'Loss Function : ' + loss
print 'Warm Start : ' + str(False)
print 'Average : ' + str(sgd_average)
print 'Learning Rate Sched: ' + str(best_learn)
print 'Initial Learn Rate : ' + str(best_learnRate)
print 'Exponent (inv scale):' + str(best_powT)
print '----------------'
print 'Results'
print '----------------'
print 'Number of Epochs : 1'
print ('Final Training Root Mean Squared Error: ' +
str(final_result['Training RMSE'][0]))
print ('Final Training R^2 Score : ' +
str(final_result['Training R^2 Score'][0]))
print ('Final Test Root Mean Squared Error : ' +
str(final_result['Test RMSE'][0]))
print ('Final Test R^2 Score : ' +
str(final_result['Test R^2 Score'][0]))
print
##########################################################################
# Observe performance of model with more than 1 epoch.
##########################################################################
resp = raw_input('See performance with more than one epoch?\n'
'Parameters will remain the same. [Y/n] ')
if resp == controls['Quit']:
return
if resp == 'Y':
print 'Tuning number of epochs...'
final_result = {}
for a in attributes:
final_result[a] = []
final_result['Perfusion Parameter'].append(perfusion_param)
final_result['Model'].append('SGD')
final_result['Patch Radius'].append(patch_radius)
final_result['Batch Size'].append(batch_size)
final_result['Penalty (Regularization)'].append(best_penalty)
final_result['Alpha'].append(best_alpha)
final_result['Average'].append(sgd_average)
final_result['Epsilon'].append(best_epsilon)
final_result['Fit Intercept?'].append(intercept)
final_result['Shuffle?'].append(shuffle)
final_result['Random Seed'].append(seed)
final_result['Loss Function'].append(loss)
final_result['Warm Start?'].append(False)
final_result['Learning Rate'].append(best_learn)
final_result['eta0'].append(best_learnRate)
final_result['Exponent (inv scaling)'].append(best_powT)
final_result['Total Number of Examples Trained'].append(
total_training_instances
)
while True:
comp = raw_input('Compare errors for range of epoch values? [Y/n] ')
if comp == 'Y':
start = raw_input('Enter lower bound (inclusive) of range: ')
end = raw_input('Enter upper bound (exclusive) of range: ')
incr = raw_input('Enter increment: ')
n_range = np.arange(int(start), int(end), int(incr))
avg_train_perf = []