コード例 #1
0
	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")
コード例 #2
0
ファイル: rpart_prediction.py プロジェクト: polyactis/annot
	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")
コード例 #3
0
ファイル: rpart_prediction.py プロジェクト: polyactis/annot
    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")