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plot_CT_coefficients.py
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plot_CT_coefficients.py
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from sklearn.datasets import make_classification
from matplotlib import pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, f1_score, classification_report,confusion_matrix,roc_curve, roc_auc_score, precision_score, recall_score, precision_recall_curve
from sklearn.preprocessing import PolynomialFeatures
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.pipeline import Pipeline
import pandas as pd
import numpy as np
import seaborn as snn
import os
from random import randrange
from scipy.stats import norm
#import matplotlib.mlab as mlab
def initialize(noct):
data=[]
for i in range(noct):
data.append([])
return data
R=[50,75,100,150]
C=[1,0.1,10]
filename=['LRF_L2_multi','LRF_L2_ovr','LRF_L1_multi','LRF_L1_ovr','LRF_elasticnet_multi','LRF_elasticnet_ovr']
strategy=['L2_mul','L2_ovr','L1_mul','L1_ovr','EN_mul','EN_ovr']
maindir='coefficients_strategy_wise/'
answer=os.path.isdir(maindir)
if answer==True:
pass
else:
os.mkdir(maindir)
f=open('BiologicalNameOfCT.dat')
nameOfCellType={}
for line in f:
l=line.split('\t')
nameOfCellType[int(l[0])]=l[1]
noct=len(nameOfCellType)
for j in range(len(R)):
data=initialize(noct)
xlabel=[]
for fi in range(len(filename)):
for i in range(len(C)):
name=filename[fi]+'/matrix_coefficients_'+str(C[i])+'_'+str(R[j])+'.dat'
try:
d=np.genfromtxt(open(name, "rb"), delimiter=',', skip_header=0)
flag=True
#print(d.shape)
except FileNotFoundError:
flag=False
if flag:
xlabel.append(strategy[fi]+',C='+str(C[i]))
for k in range(noct):
data[k].append(d[k])
data=np.array(data)
for i in range(noct):
plt.figure(figsize=(5,3))
b=snn.heatmap(data[i],yticklabels=xlabel)
plt.xlabel('features')
plt.title('R = '+str(R[j])+', '+ nameOfCellType[i])
plt.ylabel('strategies')
_, ylabels= plt.yticks()
b.set_yticklabels(ylabels, size = 5)
plt.tight_layout()
plt.savefig(maindir+'weight_matrix_'+str(nameOfCellType[i])+'_R='+str(R[j])+'.png')
plt.close()
print(data.shape,xlabel)