def submit_predictions(self, curs, schema_instance, prediction_pair2instance, cluster_id2properties):
sys.stderr.write("Submitting predictions...\n")
MpiPredictionFilter_instance = MpiPredictionFilter()
MpiPredictionFilter_instance.createGeneTable(curs, schema_instance.p_gene_table)
no_of_total_genes = get_no_of_total_genes(curs)
go_no2gene_no_set = get_go_no2gene_no_set(curs)
counter = 0
for prediction_pair, p_attr_instance in prediction_pair2instance.iteritems():
#1st fill those empty items
properties = cluster_id2properties[p_attr_instance.mcl_id]
vertex_set = properties[2]
p_attr_instance.p_value_cut_off = cal_hg_p_value(p_attr_instance.gene_no, p_attr_instance.go_no,\
vertex_set, no_of_total_genes, go_no2gene_no_set, r)
p_attr_instance.avg_p_value = p_attr_instance.p_value_cut_off
p_attr_instance.connectivity_cut_off = properties[0]
p_attr_instance.cluster_size_cut_off = len(vertex_set)
p_attr_instance.unknown_cut_off = properties[1]
MpiPredictionFilter_instance.submit_to_p_gene_table(curs, schema_instance.p_gene_table, p_attr_instance)
counter += 1
if self.report and counter%2000==0:
sys.stderr.write("%s%s"%('\x08'*20, counter))
if self.report:
sys.stderr.write("%s%s"%('\x08'*20, counter))
sys.stderr.write("Done.\n")
def run(self):
"""
11-09-05
11-09-05 add rpart_cp
11-10-05 add need_cal_hg_p_value
--db_connect()
--form_schema_tables()
--form_schema_tables()
--get_no_of_total_genes()
--get_go_no2gene_no_set()
--data_fetch()
--get_vertex_list()
--cal_hg_p_value()
--rpart_fit_and_predict()
--MpiPredictionFilter_instance....()
--record_data()
"""
(conn, curs) = db_connect(self.hostname, self.dbname, self.schema)
old_schema_instance = form_schema_tables(self.fname1)
new_schema_instance = form_schema_tables(self.fname2)
no_of_total_genes = get_no_of_total_genes(curs)
go_no2gene_no_set = get_go_no2gene_no_set(curs)
prediction_ls, all_data, known_data = self.data_fetch(curs, old_schema_instance, self.filter_type, self.is_correct_type, \
no_of_total_genes, go_no2gene_no_set, need_cal_hg_p_value)
"""
testing_acc_ls, training_acc_ls = self.rpart_validation(known_data, self.no_of_buckets, self.rpart_cp, \
self.loss_matrix, self.prior_prob)
print testing_acc_ls
print training_acc_ls
"""
pred, pred_training = self.rpart_fit_and_predict(all_data, known_data, self.rpart_cp, self.loss_matrix, self.prior_prob)
MpiPredictionFilter_instance = MpiPredictionFilter()
MpiPredictionFilter_instance.view_from_table(curs, old_schema_instance.splat_table, new_schema_instance.splat_table)
MpiPredictionFilter_instance.view_from_table(curs, old_schema_instance.mcl_table, new_schema_instance.mcl_table)
MpiPredictionFilter_instance.view_from_table(curs, old_schema_instance.pattern_table, new_schema_instance.pattern_table)
MpiPredictionFilter_instance.createGeneTable(curs, new_schema_instance.p_gene_table)
self.record_data(curs, MpiPredictionFilter_instance, prediction_ls, pred, new_schema_instance)
if self.commit:
curs.execute("end")
def run(self):
"""
11-09-05
11-09-05 add rpart_cp
11-10-05 add need_cal_hg_p_value
11-23-05
rpart_fit_and_predict() is split
2006-12-05
add need_output_data_for_R flag
--db_connect()
--form_schema_tables()
--form_schema_tables()
--get_no_of_total_genes()
--get_go_no2gene_no_set()
--data_fetch()
--get_vertex_list()
--cal_hg_p_value()
--output_data_for_R()
--rpart_fit()
--rpart_predict()
--rpart_predict()
--MpiPredictionFilter_instance....()
--record_data()
"""
(conn, curs) = db_connect(self.hostname, self.dbname, self.schema)
old_schema_instance = form_schema_tables(self.fname1)
new_schema_instance = form_schema_tables(self.fname2)
no_of_total_genes = get_no_of_total_genes(curs)
go_no2gene_no_set = get_go_no2gene_no_set(curs)
unknown_prediction_ls, known_prediction_ls, unknown_data, known_data = self.data_fetch(
curs,
old_schema_instance,
self.filter_type,
self.is_correct_type,
no_of_total_genes,
go_no2gene_no_set,
need_cal_hg_p_value,
)
if self.need_output_data_for_R: # 2006-12-05
self.output_data_for_R(known_data, "%s.known" % self.fname1)
self.output_data_for_R(unknown_data, "%s.unknown" % self.fname1)
"""
testing_acc_ls, training_acc_ls = self.rpart_validation(known_data, self.training_perc, self.rpart_cp, \
self.loss_matrix, self.prior_prob)
print testing_acc_ls
print training_acc_ls
"""
fit_model = self.fit_function_dict[self.type](known_data, self.parameter_list_dict[self.type], self.bit_string)
known_pred = self.predict_function_dict[self.type](fit_model, known_data)
unknown_pred = self.predict_function_dict[self.type](fit_model, unknown_data)
if self.debug:
if self.type == 2:
# randomForest's model has its own oob prediction
fit_model_py = fit_model.as_py(BASIC_CONVERSION)
print self.cal_accuracy(known_data, fit_model_py["predicted"], pred_type=1)
print self.cal_accuracy(known_data, known_pred, pred_type=self.type)
print self.cal_accuracy(unknown_data, unknown_pred, pred_type=self.type)
if self.commit:
MpiPredictionFilter_instance = MpiPredictionFilter()
MpiPredictionFilter_instance.view_from_table(
curs, old_schema_instance.splat_table, new_schema_instance.splat_table
)
MpiPredictionFilter_instance.view_from_table(
curs, old_schema_instance.mcl_table, new_schema_instance.mcl_table
)
MpiPredictionFilter_instance.view_from_table(
curs, old_schema_instance.pattern_table, new_schema_instance.pattern_table
)
MpiPredictionFilter_instance.createGeneTable(curs, new_schema_instance.p_gene_table)
self.record_data(
curs,
MpiPredictionFilter_instance,
unknown_prediction_ls,
unknown_pred,
new_schema_instance,
pred_type=self.type,
)
if (
self.type == 2
): # 2006-10-31 randomForest's model has its own oob prediction, but use rpart's way of storing prediction
fit_model_py = fit_model.as_py(BASIC_CONVERSION)
known_pred = fit_model_py["predicted"]
self.record_data(
curs,
MpiPredictionFilter_instance,
known_prediction_ls,
known_pred,
new_schema_instance,
pred_type=1,
)
else:
self.record_data(
curs,
MpiPredictionFilter_instance,
known_prediction_ls,
known_pred,
new_schema_instance,
pred_type=self.type,
)
curs.execute("end")