From 03d631b9e76e07e6f10e0b490e9c3739f91a795b Mon Sep 17 00:00:00 2001
From: ext-neveu_n <nneveu@hawk.iit.edu>
Date: Wed, 23 May 2018 15:23:36 -0500
Subject: [PATCH] adding function to read mldb databases
---
opal/analysis/pareto_fronts.py | 138 +++++++++++++++++++++++----------
1 file changed, 99 insertions(+), 39 deletions(-)
diff --git a/opal/analysis/pareto_fronts.py b/opal/analysis/pareto_fronts.py
index b76e376..568a9cf 100644
--- a/opal/analysis/pareto_fronts.py
+++ b/opal/analysis/pareto_fronts.py
@@ -1,44 +1,15 @@
# Author: Nicole Neveu
# Date: May 2018
-from opal.datasets.filetype import FileType
-from opal.statistics import statistics as stat
-from opal.datasets.DatasetBase import DatasetBase
-from opal.analysis import impl_beam
-import numpy as np
-import json
-import pylab as pl
+import numpy as np
import pandas as pd
-import matplotlib.pyplot as plt
-from matplotlib.widgets import Slider, Button, RadioButtons
-
-from collections import OrderedDict
-from optPilot.Annotate import AnnoteFinder
-import pyOPALTools.optPilot.OptPilotJsonReader as jsonreader
-
-
-def scaleData(vals):
- """
- Scale 1D data array from 0 to 1.
- Used to compare objectives with different units.
-
- Parameters
- ----------
- vals (numpy array) 1D array that holds any opal data
-
- Returns
- -------
- sacaled_vals (numpy array) 1D array scaled from 0 to 1
- """
- smax = np.max(vals)
- smin = np.min(vals)
- scaled_vals = (vals - smin)/smax
- return (scaled_vals)
+from opal.datasets.filetype import FileType
+from db import mldb
def pareto(x, y, dvars=0):
"""
- Find Pareto points for 2 objectives given
+ Find Pareto points for 2 objectives, given
all data recorded by optimization run.
These points are calculated independent
of generation. i.e. best points from all
@@ -46,13 +17,28 @@ def pareto(x, y, dvars=0):
Parameters
----------
- x (numpy array) array of first objective values
- y (numpy array) array of second objective values
+ x (numpy array) 1D array of first objective values
+ y (numpy array) 1D array of second objective values
Optionals
---------
- dvars
+ dvars (numpy array) ND array of design variables
+
+ Returns
+ -------
+ pfdict (dictionary) Dictionary that holds pareto front
+ values and corresponding design values
"""
+ #Check data is correct length
+ lx = len(x)
+ ly = len(y)
+ ld = len(dvars[:,0])
+ if lx==ly==ld:
+ pass
+ else:
+ print('Input data sizes do not match\n')
+ print('Please check input arrays')
+
#Making holders for my pareto fronts
pareto_y = []
pareto_x = []
@@ -60,6 +46,7 @@ def pareto(x, y, dvars=0):
w = np.arange(0,1.001, 0.001)
sx = scaleData(x)
sy = scaleData(y)
+
#Finding best point with respect to all weights (w)
for i in range(0, len(w)):
fobj = sy * w[i] + sx *(1-w[i])
@@ -69,11 +56,84 @@ def pareto(x, y, dvars=0):
pareto_pts = delete_repeats(pareto_x, pareto_y)
ind = np.array(pareto_pts.index.tolist())
- pdvar = dvars[ind, :]
+
+ #Check dvars is correct length
+ if dvars!=0:
+ pdvar = dvars[ind, :]
return(pareto_pts.ix[:,0], pareto_pts.ix[:,1], pdvar) #pareto_x, pareto_y, pdvar)
-def delete_repeats(x, y): #, z):
- df = pd.DataFrame({'x':x, 'y':y}) #, 'z':z})
+
+def get_all_data_db(dbpath):
+ """
+ Get all objectives and design variables
+ from every generation in an optimzation
+ database. Databases are made using OPAL
+ output from json files or stat files.
+ Functions to make databases can be found
+ in mldb.py.
+
+ Parameters
+ ----------
+ db (str) path to pickle file containing
+ database made with mldb.py
+
+ Returns
+ -------
+ data (dict) Dictonary containing all
+ objectives and design values
+ in optimization database.
+ """
+ data = {}
+ dbr = mldb.mldb()
+ dbr.load(dbpath)
+ #dvars = dbr.getXNames()
+ #obj = dbr.getYNames()
+ gens = dbr.getNumberOfSamples()
+
+
+ return(data)
+
+
+
+
+def scaleData(vals):
+ """
+ Scale 1D data array from 0 to 1.
+ Used to compare objectives with different units.
+
+ Parameters
+ ----------
+ vals (numpy array) 1D array that holds any opal data
+
+ Returns
+ -------
+ sacaled_vals (numpy array) 1D array scaled from 0 to 1
+ """
+ smax = np.max(vals)
+ smin = np.min(vals)
+ scaled_vals = (vals - smin)/smax
+ return (scaled_vals)
+
+
+def delete_repeats(x, y, z=0):
+ """
+ Delete repeated pareto front values, if any.
+
+ Parameters
+ ----------
+ x (numpy array) 1D array of first objective values
+ y (numpy array) 1D array of second objective values
+
+ Optionals
+ ---------
+ z (numpy array) ND array of second design variables
+
+ """
+ if z==0:
+ df = pd.DataFrame({'x':x, 'y':y}) #, 'z':z})
+ else:
+ df = pd.DataFrame({'x':x, 'y':y, 'z':z})
+
return df.drop_duplicates(subset=['x', 'y'], keep='first')
--
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