def rpart_fit_and_predict(self, all_data, known_data, rpart_cp, loss_matrix, prior_prob, bit_string='11111'):
"""
11-09-05
1st use known_data to get the fit model
2nd use the fit model to do prediction on all_data, result is prob for each class
11-09-05 add rpart_cp
11-17-05
add loss_matrix, prior_prob
return two pred
"""
sys.stderr.write("rpart fitting and predicting...\n")
r.library("rpart")
coeff_name_list = ['p_value', 'recurrence', 'connectivity', 'cluster_size', 'gradient']
formula_list = []
for i in range(len(bit_string)):
if bit_string[i] == '1':
formula_list.append(coeff_name_list[i])
#11-17-05 transform into array
all_data = array(all_data)
known_data = array(known_data)
set_default_mode(NO_CONVERSION)
data_frame = r.as_data_frame({"p_value":known_data[:,0], "recurrence":known_data[:,1], "connectivity":known_data[:,2], \
"cluster_size":known_data[:,3], "gradient":known_data[:,4], "is_correct":known_data[:,-1]})
if prior_prob:
prior_prob = [prior_prob, 1-prior_prob] #get the full list
fit = r.rpart(r("is_correct~%s"%'+'.join(formula_list)), data=data_frame, method="class", control=r.rpart_control(cp=rpart_cp),\
parms=r.list(prior=prior_prob, loss=r.matrix(loss_matrix) ) )
else:
fit = r.rpart(r("is_correct~%s"%'+'.join(formula_list)), data=data_frame, method="class", control=r.rpart_control(cp=rpart_cp),\
parms=r.list(loss=r.matrix(loss_matrix) ) )
set_default_mode(BASIC_CONVERSION)
pred_training = r.predict(fit, data_frame, type=["class"])
del data_frame
set_default_mode(NO_CONVERSION)
all_data_frame = r.as_data_frame({"p_value":all_data[:,0], "recurrence":all_data[:,1], "connectivity":all_data[:,2], \
"cluster_size":all_data[:,3], "gradient":all_data[:,4], "is_correct":all_data[:,-1]})
set_default_mode(BASIC_CONVERSION)
pred = r.predict(fit, all_data_frame, type=["class"]) #11-17-05 type=c("class")
del all_data_frame
sys.stderr.write("Done rpart fitting and predicting.\n")
return pred, pred_training
def _independent_betas_same_sources(self, tag_list, remove_tags_when_bad_regression, n_times_show_summary=3):
times_showed_summary = 0 # This allows us to print out some summary statistics without producing an overwhelming amount of output.
SUMMARY_STATS = ["beta", "stderr", "tstat", "pval"]
for tag in tag_list:
self._progress("Computing betas for tag %s." % tag, newline=True) # rmme: newline make false
rpy.set_default_mode(rpy.NO_CONVERSION) # Turn off conversion so that lm returns Robj.
data = rc.list(y=self.y[tag],X=self.X[tag])
model = "y~X-1" # Use -1 because X has an intercept already
if self.regtype=="Independent Linear":
try:
result = rc.lm(model,data=data)
except:
pdb.set_trace()
elif self.regtype=="Independent Logistic":
result = rc.glm(model,family=rc.binomial("logit"),data=data)
rpy.set_default_mode(rpy.BASIC_CONVERSION) # Return to normal conversion mode.
summary = rc.summary(result,correlation=rc.TRUE)
self._record_regression_stats(tag, summary)
beta_dict = dict()
sorted_sources = self.sorted_sources[tag]
coeff_matrix = summary["coefficients"]
for i in range(len(sorted_sources)):
try:
cur_source_dict = dict(zip(SUMMARY_STATS,coeff_matrix[i,:]))
except IndexError:
util.info("\tWARNING: Regression for %s didn't end up using all variables." % tag)
if remove_tags_when_bad_regression:
self._remove_tag(tag)
break # break from for-loop over sorted_sources; we don't continue out of the per-tag for loop until later when we check if tag is in self.features....
continue
try:
cur_source_dict["-log10(pval)"] = -log(cur_source_dict["pval"], 10)
except OverflowError:
pass
beta_dict[sorted_sources[i]] = cur_source_dict
if tag not in self.features: # We've removed this tag a few lines above, so skip it.
continue
self.beta[tag] = beta_dict
if times_showed_summary < n_times_show_summary:
self._print_regression_summary(tag, summary)
times_showed_summary += 1
def _mcmc_betas_same_sources(self, tag_list):
"""
The given tag_list contains tags that all have the same features
available. Train on the tags in tag_list using only the songs
in self.only_these_songs, or all available songs if
self.only_these_songs is None.
"""
if not self.production_run:
self.mcmc_reps = 75 # save time
rc.library("bayesm")
data = []
for tag in tag_list:
data.append(rc.list(X=self.X[tag],y=self.y[tag]))
rpy.set_default_mode(rpy.NO_CONVERSION) # Turn off conversion so that lm returns Robj.
data = rc.list(*data)
if self.regtype in ["Hierarchical Linear", "Hierarchical Mixture"]:
Data = rc.list(regdata=data)
elif self.regtype=="Hierarchical Logistic":
Data = rc.list(lgtdata=data)
if self.regtype=="Hierarchical Mixture":
Prior = rc.list(ncomp=self.ncomp)
Mcmc=rc.list(R=self.mcmc_reps)
rpy.set_default_mode(rpy.BASIC_CONVERSION)
try:
if self.regtype=="Hierarchical Linear":
output = rc.rhierLinearModel(Data=Data,Mcmc=Mcmc)
elif self.regtype=="Hierarchical Logistic":
output = rc.rhierBinLogit(Data=Data,Mcmc=Mcmc)
elif self.regtype=="Hierarchical Mixture":
output = rc.rhierLinearMixture(Data=Data,Prior=Prior,Mcmc=Mcmc)
except:
#pdb.set_trace()
self._info_about_r_error(tag_list)
return
beta_matrix = output['betadraw'].mean(axis=2) # nregressions x ncoeffs, averaged along third dim
matrix_index = 0
for tag in tag_list:
cur_tag_beta_vec = beta_matrix[matrix_index,:]
beta_dict_list = [dict([('beta', coeff)]) for coeff in cur_tag_beta_vec]
self.beta[tag] = dict(zip(self.sorted_sources[tag],beta_dict_list))
self.stats[tag] = dict() # I'm not currently storing any stats for hierarchical regressions.
matrix_index += 1
def rpart_fit(self, known_data, parameter_list, bit_string="11111"):
"""
11-09-05
1st use known_data to get the fit model
2nd use the fit model to do prediction on all_data, result is prob for each class
11-09-05 add rpart_cp
11-17-05
add loss_matrix, prior_prob
return two pred
11-23-05
split fit and predict. rpart_fit_and_predict() is split into rpart_fit() and rpart_predict()
11-27-05
r cleanup
03-17-06
use parameter_list instead
"""
if self.debug:
sys.stderr.write("Doing rpart_fit...\n")
# 03-17-06
rpart_cp, loss_matrix, prior_prob = parameter_list
# 11-27-05 r cleanup
from rpy import r
r.library("rpart")
coeff_name_list = ["p_value", "recurrence", "connectivity", "cluster_size", "gradient"]
formula_list = []
for i in range(len(bit_string)):
if bit_string[i] == "1":
formula_list.append(coeff_name_list[i])
# 11-17-05 transform into array
known_data = array(known_data)
set_default_mode(NO_CONVERSION)
data_frame = r.as_data_frame(
{
"p_value": known_data[:, 0],
"recurrence": known_data[:, 1],
"connectivity": known_data[:, 2],
"cluster_size": known_data[:, 3],
"gradient": known_data[:, 4],
"is_correct": known_data[:, -1],
}
)
if prior_prob:
prior_prob = [prior_prob, 1 - prior_prob] # get the full list
fit = r.rpart(
r("is_correct~%s" % "+".join(formula_list)),
data=data_frame,
method="class",
control=r.rpart_control(cp=rpart_cp),
parms=r.list(prior=prior_prob, loss=r.matrix(loss_matrix)),
)
else:
fit = r.rpart(
r("is_correct~%s" % "+".join(formula_list)),
data=data_frame,
method="class",
control=r.rpart_control(cp=rpart_cp),
parms=r.list(loss=r.matrix(loss_matrix)),
)
del data_frame
if self.debug:
sys.stderr.write("Done rpart_fit.\n")
return fit
