mirror of https://github.com/VLSIDA/OpenRAM.git
Changed linear regression model to reference data in tech dir vs local ref.
This commit is contained in:
Hunter Nichols
parent
d111041385
commit
dcd20a250a
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@ -12,11 +12,13 @@ from globals import OPTS,find_exe,get_tool
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from .lib import *
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from .delay import *
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from .elmore import *
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from .linear_regression import *
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from .setup_hold import *
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from .functional import *
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from .simulation import *
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from .measurements import *
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from .model_check import *
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from .analytical_util import *
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debug.info(1,"Initializing characterizer...")
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OPTS.spice_exe = ""
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@ -1,10 +1,10 @@
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import diversipy as dp
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#import diversipy as dp
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import csv
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import math
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import numpy as np
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import os
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def get_data_names(self, file_name):
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def get_data_names(file_name):
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with open(file_name, newline='') as csvfile:
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csv_reader = csv.reader(csvfile, delimiter=' ', quotechar='|')
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row_iter = 0
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@ -13,7 +13,7 @@ def get_data_names(self, file_name):
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# Return names from first row
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return row[0].split(',')
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def get_data(self, file_name):
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def get_data(file_name):
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with open(file_name, newline='') as csvfile:
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csv_reader = csv.reader(csvfile, delimiter=' ', quotechar='|')
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row_iter = 0
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@ -36,12 +36,12 @@ def get_data(self, file_name):
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#print(data)
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return input_list
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def apply_samples_to_data(self, all_data, algo_samples):
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def apply_samples_to_data(all_data, algo_samples):
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# Take samples from algorithm and match them to samples in data
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data_samples, unused_data = [], []
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sample_positions = set()
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for sample in algo_samples:
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sample_positions.add(self.find_sample_position_with_min_error(all_data, sample))
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sample_positions.add(find_sample_position_with_min_error(all_data, sample))
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for i in range(len(all_data)):
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if i in sample_positions:
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@ -51,19 +51,19 @@ def apply_samples_to_data(self, all_data, algo_samples):
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return data_samples, unused_data
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def find_sample_position_with_min_error(self, data, sampled_vals):
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def find_sample_position_with_min_error(data, sampled_vals):
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min_error = 0
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sample_pos = 0
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count = 0
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for data_slice in data:
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error = self.squared_error(data_slice, sampled_vals)
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error = squared_error(data_slice, sampled_vals)
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if min_error == 0 or error < min_error:
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min_error = error
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sample_pos = count
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count += 1
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return sample_pos
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def squared_error(self, list_a, list_b):
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def squared_error(list_a, list_b):
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#print('a:',list_a, 'b:', list_b)
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error_sum = 0;
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for a,b in zip(list_a, list_b):
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@ -71,7 +71,7 @@ def squared_error(self, list_a, list_b):
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return error_sum
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def get_max_min_from_datasets(self, dir):
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def get_max_min_from_datasets(dir):
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if not os.path.isdir(dir):
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print("Input Directory not found:",dir)
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return [], [], []
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@ -80,7 +80,7 @@ def get_max_min_from_datasets(self, dir):
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data_files = [f for f in os.listdir(dir) if os.path.isfile(os.path.join(dir, f))]
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maxs,mins,sums,total_count = [],[],[],0
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for file in data_files:
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data = self.get_data(os.path.join(dir, file))
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data = get_data(os.path.join(dir, file))
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# Get max, min, sum, and count from every file
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data_max, data_min, data_sum, count = [],[],[], 0
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for feature_list in data:
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@ -102,11 +102,11 @@ def get_max_min_from_datasets(self, dir):
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avgs = [s/total_count for s in sums]
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return maxs,mins,avgs
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def get_data_and_scale(self, file_name, sample_dir):
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maxs,mins,avgs = self.get_max_min_from_datasets(sample_dir)
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def get_data_and_scale(file_name, sample_dir):
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maxs,mins,avgs = get_max_min_from_datasets(sample_dir)
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# Get data
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all_data = self.get_data(file_name)
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all_data = get_data(file_name)
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# Scale data from file
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self_scaled_data = [[] for _ in range(len(all_data[0]))]
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@ -117,7 +117,7 @@ def get_data_and_scale(self, file_name, sample_dir):
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return np.asarray(self_scaled_data)
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def rescale_data(self, data, old_maxs, old_mins, new_maxs, new_mins):
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def rescale_data(data, old_maxs, old_mins, new_maxs, new_mins):
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# unscale from old values, rescale by new values
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data_new_scaling = []
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for data_row in data:
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@ -130,21 +130,22 @@ def rescale_data(self, data, old_maxs, old_mins, new_maxs, new_mins):
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return data_new_scaling
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def sample_from_file(self, num_samples, file_name, sample_dir=None):
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def sample_from_file(num_samples, file_name, sample_dir=None):
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if sample_dir:
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maxs,mins,avgs = self.get_max_min_from_datasets(sample_dir)
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maxs,mins,avgs = get_max_min_from_datasets(sample_dir)
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else:
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maxs,mins,avgs = [], [], []
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# Get data
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all_data = self.get_data(file_name)
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all_data = get_data(file_name)
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# Get algorithms sample points, assuming hypercube for now
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num_labels = 1
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inp_dims = len(all_data) - num_labels
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#samples = dp.hycusampling.lhd_matrix(num_samples, inp_dims)/num_samples
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#samples = dp.hycusampling.halton(num_samples, inp_dims)
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samples = dp.hycusampling.random_uniform(num_samples, inp_dims)
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#samples = dp.hycusampling.random_uniform(num_samples, inp_dims)
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samples = None
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# Scale data from file
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@ -158,19 +159,19 @@ def sample_from_file(self, num_samples, file_name, sample_dir=None):
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for i in range(len(feature_list)):
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self_scaled_data[i].append((feature_list[i]-min_val)/(max_val-min_val))
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# Apply algorithm sampling points to available data
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sampled_data, unused_data = self.apply_samples_to_data(self_scaled_data,samples)
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sampled_data, unused_data = apply_samples_to_data(self_scaled_data,samples)
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#print(sampled_data)
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#unscale values and rescale using all available data (both sampled and unused points rescaled)
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if len(maxs)!=0 and len(mins)!=0:
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sampled_data = self.rescale_data(sampled_data, self_maxs,self_mins, maxs, mins)
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unused_new_scaling = self.rescale_data(unused_data, self_maxs,self_mins, maxs, mins)
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sampled_data = rescale_data(sampled_data, self_maxs,self_mins, maxs, mins)
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unused_new_scaling = rescale_data(unused_data, self_maxs,self_mins, maxs, mins)
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return np.asarray(sampled_data), np.asarray(unused_new_scaling)
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def unscale_data(self, data, ref_dir, pos=None):
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def unscale_data(data, ref_dir, pos=None):
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if ref_dir:
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maxs,mins,avgs = self.get_max_min_from_datasets(ref_dir)
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maxs,mins,avgs = get_max_min_from_datasets(ref_dir)
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else:
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print("Must provide reference data to unscale")
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return None
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@ -190,7 +191,7 @@ def unscale_data(self, data, ref_dir, pos=None):
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return unscaled_data
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def abs_error(self, labels, preds):
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def abs_error(labels, preds):
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total_error = 0
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for label_i, pred_i in zip(labels, preds):
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cur_error = abs(label_i[0]-pred_i[0])/label_i[0]
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@ -198,14 +199,14 @@ def abs_error(self, labels, preds):
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total_error += cur_error
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return total_error/len(labels)
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def max_error(self, labels, preds):
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def max_error(labels, preds):
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mx_error = 0
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for label_i, pred_i in zip(labels, preds):
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cur_error = abs(label_i[0]-pred_i[0])/label_i[0]
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mx_error = max(cur_error, mx_error)
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return mx_error
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def min_error(self, labels, preds):
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def min_error(labels, preds):
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mn_error = 1
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for label_i, pred_i in zip(labels, preds):
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cur_error = abs(label_i[0]-pred_i[0])/label_i[0]
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@ -13,6 +13,7 @@ from .setup_hold import *
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from .delay import *
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from .elmore import *
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from .charutils import *
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from .linear_regression import *
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import tech
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import numpy as np
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from globals import OPTS
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@ -584,9 +585,13 @@ class lib:
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def compute_delay(self):
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"""Compute SRAM delays for current corner"""
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if self.use_model:
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self.d = elmore(self.sram, self.sp_file, self.corner)
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char_results = self.d.analytical_delay(self.slews,self.loads)
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self.char_sram_results, self.char_port_results = char_results
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#FIXME: ML models only designed for delay. Cannot produce all values for Lib
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d = linear_regression()
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char_results = d.get_prediction()
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#self.d = elmore(self.sram, self.sp_file, self.corner)
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# char_results = self.d.analytical_delay(self.slews,self.loads)
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# self.char_sram_results, self.char_port_results = char_results
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else:
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self.d = delay(self.sram, self.sp_file, self.corner)
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if (self.sram.num_spare_rows == 0):
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@ -1,25 +1,64 @@
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# See LICENSE for licensing information.
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#
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# Copyright (c) 2016-2019 Regents of the University of California and The Board
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# of Regents for the Oklahoma Agricultural and Mechanical College
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# (acting for and on behalf of Oklahoma State University)
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# All rights reserved.
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#
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import os
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from sklearn.linear_model import LinearRegression
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import mapping
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from .analytical_util import *
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from globals import OPTS
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import debug
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reference_dir = "data"
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relative_data_path = "/sim_data"
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data_filename = "data.csv"
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tech_path = os.environ.get('OPENRAM_TECH')
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data_dir = tech_path+'/'+OPTS.tech_name+relative_data_path
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def run_model(x,y,test_x,test_y):
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mp = mapping.mapping()
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model = LinearRegression()
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model.fit(x, y)
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print(model.coef_)
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print(model.intercept_)
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class linear_regression():
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pred = model.predict(test_x)
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def get_prediction(self):
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#print(pred)
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unscaled_labels = mp.unscale_data(test_y.tolist(), reference_dir)
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unscaled_preds = mp.unscale_data(pred.tolist(), reference_dir)
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unscaled_labels, unscaled_preds = (list(t) for t in zip(*sorted(zip(unscaled_labels, unscaled_preds))))
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avg_error = mp.abs_error(unscaled_labels, unscaled_preds)
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max_error = mp.max_error(unscaled_labels, unscaled_preds)
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min_error = mp.min_error(unscaled_labels, unscaled_preds)
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train_sets = []
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test_sets = []
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file_path = data_dir +'/'+data_filename
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num_points_train = 5
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errors = {"avg_error": avg_error, "max_error":max_error, "min_error":min_error}
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return errors
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non_ip_samples, unused_samples = sample_from_file(num_points_train, file_path, data_dir)
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nip_features_subset, nip_labels_subset = non_ip_samples[:, :-1], non_ip_samples[:,-1:]
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nip_test_feature_subset, nip_test_labels_subset = unused_samples[:, :-1], unused_samples[:,-1:]
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train_sets = [(nip_features_subset, nip_labels_subset)]
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test_sets = [(nip_test_feature_subset, nip_test_labels_subset)]
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runs_per_model = 1
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for train_tuple, test_tuple in zip(train_sets, test_sets):
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train_x, train_y = train_tuple
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test_x, test_y = test_tuple
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errors = {}
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min_train_set = None
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for _ in range(runs_per_model):
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new_error = self.run_model(train_x, train_y, test_x, test_y, data_dir)
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debug.info(1, "Model Error: {}".format(new_error))
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def run_model(x,y,test_x,test_y, reference_dir):
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model = LinearRegression()
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model.fit(x, y)
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pred = model.predict(test_x)
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#print(pred)
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unscaled_labels = unscale_data(test_y.tolist(), reference_dir)
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unscaled_preds = unscale_data(pred.tolist(), reference_dir)
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unscaled_labels, unscaled_preds = (list(t) for t in zip(*sorted(zip(unscaled_labels, unscaled_preds))))
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avg_error = abs_error(unscaled_labels, unscaled_preds)
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max_error = max_error(unscaled_labels, unscaled_preds)
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min_error = min_error(unscaled_labels, unscaled_preds)
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errors = {"avg_error": avg_error, "max_error":max_error, "min_error":min_error}
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return errors
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