Example #1
0
	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
Example #2
0
    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