# -*- coding: utf-8 -*- """ Created on Sat Jul 4 16:22:48 2020 @author: joost """ import pandas as pd import numpy as np from data_load import load_csv import matplotlib.pyplot as plt # loading dataframes n_list = [8,10,12,14,16] frames = [load_csv("data_trim/data_trim_3-regular_unweighted_INT_"+str(n)+".csv") for n in n_list] df = pd.concat(frames) p_max = 8 # plot configuration font = {'family' : 'normal', 'size' : 16} plt.rc('font', **font) plt.figure(figsize=(10,6)) ax = plt.subplot(111) #colors = ['midnightblue', 'navy', 'darkblue', 'mediumblue', 'blue','dodgerblue', 'royalblue', 'cornflowerblue', 'red'] for c, p in enumerate(range(p_max,0,-1)): p_array = [np.mean(df[(df['p'] == p) & (df['n_nodes'] == n)]['Fp']/df[(df['p'] == p) & (df['n_nodes'] == n)]['Cmax']) for n in n_list] plt.plot(n_list, p_array, linestyle = 'solid', label = 'p = '+str(p), marker = 'o')
import numpy as np from data_load import load_csv import matplotlib.pyplot as plt # # loading dataframes # # ER 0.75 # n_list = [6,7,8,9,10,11,12,13,14,15] # frames = [load_csv("data/data_ER-075_unweighted_INT_"+str(n)+".csv") for n in [12,13,14,15]] # frames.append(load_csv("data/data_ER-075_unweighted_INT_4-12.csv")) # df = pd.concat(frames) # p_max = 8 # ER 0.50 n_list = [6, 7, 8, 9, 10, 11, 12, 13, 14, 15] frames = [ load_csv("data/data_ER-050_unweighted_INT_11-14.csv"), load_csv("data/data_ER-050_unweighted_INT_15-16.csv"), load_csv("data/data_ER-050_unweighted_INT_6-10.csv") ] df = pd.concat(frames) p_max = 8 # plot configuration font = {'family': 'normal', 'size': 16} plt.rc('font', **font) plt.figure(figsize=(10, 6)) ax = plt.subplot(111) #colors = ['midnightblue', 'navy', 'darkblue', 'mediumblue', 'blue','dodgerblue', 'royalblue', 'cornflowerblue', 'red'] for c, p in enumerate(range(p_max, 0, -1)):
import pandas as pd import numpy as np from data_load import load_csv import matplotlib.pyplot as plt # plot configuration font = {'family': 'normal', 'size': 16} plt.rc('font', **font) plt.figure(figsize=(10, 6)) # loading dataframe n = 15 filename = "data/data_ER-075_unweighted_INT_15.csv" df = load_csv(filename) # eine keer p_max = 8 #int(np.max(df['p'])) seed_max = 10 N = seed_max mean_n_evals = np.zeros(p_max) mean_cum_n_evals = np.zeros(p_max) attr = 'n_Fp_evals' df_new = df[(df['n_nodes'] == n) & (df['seed'] < seed_max) & (df['p'] <= p_max)][attr].iloc() # df.loc[df['p'] == 10][['gammas']] - Wybe
Created on Thu Jul 2 22:22:51 2020 @author: joost """ import pandas as pd from data_load import load_csv import numpy as np from pyquil_base import brute_force from tqdm import tqdm import networkx as nx from GW import goemans_williamson as gw # Comparing runtime of RI and INT # unweighted dfu1 = load_csv('data/data_3-regular_unweighted_INT_12.csv') dfu2 = load_csv('data/data_3-regular_unweighted_INT_14.csv') dfu3 = load_csv('data/data_3-regular_unweighted_INT_8-10-16.csv') # weighted dfw1 = load_csv('data/data_3-regular_weighted_INT_8-10-12.csv') dfw2 = load_csv('data/data_3-regular_weighted_INT_14-16.csv') # loading the data s.t. they are equivalent (same p, same N) p_max = 8 seed_max = 20 # unweighted u8 = dfu3.loc[lambda df: df['p'] <= p_max, :].loc[ lambda df: df['seed'] < seed_max, :].loc[lambda df: df['n_nodes'] == 8, :] u10 = dfu3.loc[lambda df: df['p'] <= p_max, :].loc[ lambda df: df['seed'] < seed_max, :].loc[lambda df: df['n_nodes'] == 10, :]
from pyquil_interp_function import interp_pyquil import pandas as pd import networkx as nx from data_load import load_csv from GW import goemans_williamson # creating dataframe without overwriting (so additing) # if you start a new set of data, make sure you start with a fresh file filename = 'data_2-regular.csv' overwrite = False if overwrite == True: output = pd.DataFrame() else: output = load_csv(filename) p_max = 10 # maxdepth n_min, n_max = 3, 20 N = 1 # number of graphs per node number n d = 2 # degree of regular graphs for n in range(n_min, n_max + 1): for s in range(N): G = nx.cycle_graph(n) graph_type = str(d) + '-regular_' + str(n) + '-nodal' results = interp_pyquil(G, p_max) for i, results_i in results.items(): results_i['graph_name'] = graph_type
from data_load import load_csv import matplotlib.pyplot as plt # plot configuration font = {'family' : 'normal', 'size' : 16} plt.rc('font', **font) plt.figure(figsize=(10,6)) # loading dataframe n = 16 pre = "data_trim/data_trim_3-regular_" post =".csv" filename = "unweighted_INT_"+str(n) df = load_csv(pre+filename+post); # eine keer # exponent or exponent of square root SQR = True p_max = int(np.max(df['p'])) N = len(set(df['seed'])) mean_runtime = np.zeros(p_max) mean_cum_time = np.zeros(p_max) # df.loc[df['p'] == 10][['gammas']] - Wybe for i in range(0,len(df),p_max): runtime = np.array([df['time'][i+j] for j in range(p_max)])
# -*- coding: utf-8 -*- """ Created on Wed Jul 1 13:13:02 2020 @author: joost """ from data_load import load_csv import numpy as np import networkx as nx from pyquil_interp_function import brute_force import matplotlib.pyplot as plt folder = 'data/' df_u3R = load_csv(folder+'data_3-regular_unweighted_INT_14.csv', True) df_w3R = load_csv(folder+'data_3-regular_weighted_INT_14-16.csv', True) # trimming data last_graph = 200 u3R = df_u3R[df_u3R['n_nodes']==14][:last_graph].iloc() w3R = df_w3R[df_w3R['n_nodes']==14][:last_graph].iloc() p_max = 10 for df in [w3R]: plt.figure() r_sum = np.zeros(p_max) label = True for i in range(0,last_graph,p_max): print("graph", i,"/",last_graph)
import pandas as pd import numpy as np from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_absolute_error from sklearn.model_selection import train_test_split from data_load import load_csv from sklearn import preprocessing test = load_csv('test') train = load_csv('train') # y = LabelBinarizer().fit_transform(y) lb = preprocessing.LabelBinarizer() train.Sex = lb.fit_transform(train.Sex) # print(train.Sex) # print(train.shape, train.head()) features = ['Pclass','Sex','SibSp','Parch','SibSp','Parch'] # Create target object and call it y y = train.Survived # Create X X = train[features] print(X.shape) X = X.dropna() print(X.shape) # print('before',X.shape) # print('after',X.shape) # Split into validation and training data train_X, val_X, train_y, val_y = train_test_split(X, y,test_size=0.1, random_state=1) # print(val_y.loc[val_y == 0])
# -*- coding: utf-8 -*- """ Created on Sat Jul 4 16:22:48 2020 @author: joost """ import pandas as pd import numpy as np from data_load import load_csv import matplotlib.pyplot as plt # loading dataframes n_list = [4,5,6,7,8,9,10,11,12] df1 = load_csv("data/data_complete_unweighted_INT_4-12.csv") df2 = pd.DataFrame() frames = [df1, df2] df = pd.concat(frames) p_max = 8 p_list = range(p_max,0,-1) # plot configuration font = {'family' : 'normal', 'size' : 16} plt.rc('font', **font) plt.figure(figsize=(10,6)) ax = plt.subplot(111)
# -*- coding: utf-8 -*- """ Created on Sat Jul 4 16:22:48 2020 @author: joost """ import pandas as pd import numpy as np from data_load import load_csv import matplotlib.pyplot as plt # loading dataframes n_list = [8, 10, 12, 14, 16] df = load_csv('data/data_3-regular_unweighted_RI_8-16.csv') p_max = 8 p_list = range(p_max, 0, -1) # plot configuration font = {'size': 16} plt.rc('font', **font) plt.figure(figsize=(10, 6)) ax = plt.subplot(111) for c, p in enumerate(p_list): r_array = np.array([ np.mean(df[(df['p'] == p) & (df['n_nodes'] == n)]['Fp'] / df[(df['p'] == p) & (df['n_nodes'] == n)]['Cmax'])
import numpy as np from data_load import load_csv import matplotlib.pyplot as plt # plot configuration font = {'family': 'normal', 'size': 16} plt.rc('font', **font) plt.figure(figsize=(10, 6)) # loading dataframe n = 16 pre = "data_trim/data_trim_3-regular_" post = ".csv" filename = "unweighted_INT_" + str(n) df = load_csv(pre + filename + post) # eine keer # exponent or exponent of square root SQR = True p_max = int(np.max(df['p'])) N = len(set(df['seed'])) mean_n_evals = np.zeros(p_max) mean_cum_evals = np.zeros(p_max) # df.loc[df['p'] == 10][['gammas']] - Wybe for i in range(0, len(df), p_max): n_evals = np.array([df['n_Fp_evals'][i + j] for j in range(p_max)]) cum_evals = np.array([
Created on Mon Jun 29 15:50:15 2020 @author: joost """ import pandas as pd import numpy as np from data_load import load_csv import matplotlib.pyplot as plt from math import ceil # this is easiest (and luckily turns out right in this case, in the future I'll add another column with C_max) import matplotlib.pylab as pylab from GW import goemans_williamson # loading dataframe filename = 'data/data_3-regular_unweighted_INT_12.csv' df = load_csv(filename); # eine keer p_max = 10 # plotting params fontsize = 20 # 'x-large' params = {'legend.fontsize': fontsize, 'figure.figsize': (10, 8), 'axes.labelsize': fontsize+4, 'axes.titlesize':fontsize+4, 'xtick.labelsize':fontsize-2, 'ytick.labelsize':fontsize-2} #pylab.rcParams.update(params) # figure 1 plt.figure() for i in range(0,280,10):