def EmissionMat(model_1,model_n_m):
print "\n We compute the emission state probability matrix from the confusion matrix for the first classifier: \n"
X3=model_n_m.cm_normalized
X3=X3.T
X3=np.array([X3[0],X3[2],X3[1]])
X3=X3.T
EmissionMat=np.zeros(shape=(5,5))
EmissionMat[0,0]=model_1.cm_normalized[0,0]
EmissionMat[4,4]=model_1.cm_normalized[0,0]
EmissionMat[1:5,0]=(1-model_1.cm_normalized[0,0])/3
EmissionMat[0:4,4]=(1-model_1.cm_normalized[0,0])/3
### Bricolage
EmissionMat[1:4,1:4]=X3
EmissionMat[1:4,1:3]+=-EmissionMat[3,0]*2/3
### On modifie car la diag n'est pas assez bonne...
EmissionMat[3,2:4]=[0.4,0.5]
EmissionMat[0,1:4]=sum(model_1.cm_normalized[0,1:3])/3
EmissionMat[4,1:4]=sum(model_1.cm_normalized[0,1:3])/3
EmissionMat=abs(EmissionMat).astype('float') / abs(EmissionMat).sum(axis=1)[:, np.newaxis]
## Put something better then abs...
plot_matrix(EmissionMat,title="Emission matrix",names=["M_B","G1","S","G2","M_E"])
plt.show()
return(EmissionMat)
def final_classif_HMM(data,obj_norm,
y_name_3state="Type",classif_Mitose="MitoseOrNot",
classif_3state="3state",classif_final="Pred_fusion",
classif_hmm="HMM",
ratio=5.9/60,
plot=True,
obs_number=0):
print "Here we are going to join the corrected data (from R) to our current data in Python \n "
data.ix[data.HMM==1,classif_hmm]="M"
data.ix[data.HMM==2,classif_hmm]="1"
data.ix[data.HMM==3,classif_hmm]="S"
data.ix[data.HMM==4,classif_hmm]="2"
data.ix[data.HMM==5,classif_hmm]="M"
to_join=pd.Series(data[classif_hmm])
to_join.index=[int(el) for el in to_join.index]
obj_norm.data=obj_norm.data.join(to_join)
obj_norm.update()
if hasattr(obj_norm, 'train'):
print "Recap of our data: \n "
print obj_norm.train[["traj",y_name_3state,classif_Mitose,classif_3state,classif_final,classif_hmm]].head()
i=0
G1=[]
S=[]
G2=[]
CC=[]
print "We are going to count the lengths of the G1 phase, the S phase and the G2 phase: \n"
print "To quickly asses we print the trajectory and his corrected trajectory, for sequence number:" + str(obs_number)
for el in obj_norm.Group_of_traj:
new_obs=el[1][classif_hmm]
if i==obs_number:
test=np.array(el[1][classif_final])
test_hmm=np.array(el[1][classif_hmm])
print classif_final+": \n"
print test
print "\n Corrected HMM: \n"
print test_hmm
i+=1
if not check_rotate(new_obs):
G1.append(Measure(new_obs,'1',_last=True))
S.append(Measure(new_obs,'S',_last=True,_first=True))
G2.append(Measure(new_obs,'2',_first=True))
CC.append(Measure(new_obs,'M'))
elif not check_rotate(new_obs[:-1]):
G1.append(Measure(new_obs[:-1],'1',_last=True))
S.append(Measure(new_obs[:-1],'S',_last=True,_first=True))
G2.append(Measure(new_obs[:-1],'2',_first=True))
CC.append(Measure(new_obs[:-1],'M'))
elif not check_rotate(new_obs[:-2]):
G1.append(Measure(new_obs[:-2],'1',_last=True))
S.append(Measure(new_obs[:-2],'S',_last=True,_first=True))
G2.append(Measure(new_obs[:-2],'2',_first=True))
CC.append(Measure(new_obs[:-2],'M'))
else:
G1.append(-1)
S.append(-1)
G2.append(-1)
CC.append(-1)
G1_p=[el*ratio for el in G1 if el>-1]
S_p= [el*ratio for el in S if el>-1]
G2_p=[el*ratio for el in G2 if el>-1]
CC_p=[el*ratio for el in CC if el>-1]
res = {'mean' : pd.Series([np.mean(G1_p), np.mean(S_p), np.mean(G2_p),np.mean(CC_p)], index=['G1', 'S', 'G2','CellCycle']),
'Standard deviation' : pd.Series([np.std(G1_p),np.std(S_p),np.std(G2_p),np.std(CC_p)], index=['G1', 'S', 'G2','CellCycle']),
'Accepted trajectories': pd.Series([len(G1_p),len(S_p),len(G2_p),len(CC_p)], index=['G1', 'S', 'G2','CellCycle'])
}
print "Number of total trajectories: "+str(len(G1))
G1_p={"val":G1_p,"name":"G1"}
S_p={"val":S_p,"name":"S"}
G2_p={"val":G2_p,"name":"G2"}
CC_p={"val":CC_p,"name":"CellCycle"}
if plot:
for el in [G1_p,S_p,G2_p,CC_p]:
plt.hist(el["val"],bins=int(0.75*len(el["val"])))
plt.title(el["name"]+" lengths distribution")
plt.xlabel("times (hours)")
plt.ylabel("Frequency")
plt.show()
if hasattr(obj_norm, 'train'):
temp_X=obj_norm.train.ix[pd.notnull(obj_norm.train[classif_hmm]),[classif_hmm,y_name_3state]]
print temp_X[y_name_3state].value_counts()
cm=confusion_matrix(temp_X[y_name_3state],temp_X[classif_hmm])
print "We reach an accuracy of %5.3f \n" %(float(cm.trace())/cm.sum())
cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
plot_matrix(cm_normalized,title="Confusion matrix for the HMM classification")
return(obj_norm,pd.DataFrame(res))
def MitoseClassif(obj_norm,
y_name_3state="Type",classif_Mitose="MitoseOrNot",
num_str="0015"):
print "\n We first load the unnormalized data: \n"
if os.path.isfile("H2b_data.csv"):
print "The file existed so I loaded it."
H2b = Traj_data(file_name="H2b_data.csv",pkl_traj_file="./Pkl_file")
else:
H2b=Traj_data()
H2b.extracting(num_str,"both_channels_0015.hdf5",'primary')
## Extracting the hdf5 file for the primary channel (H2b)
H2b.Add_traj(normalize=False)## ,num_traj=10) ## (you can reduce the number of traj)
## Adding Alice's work on tracking to have trajectories
file_loc="0015_PCNA.xml"
H2b.label_finder(file_loc)
## Finding associated labels by minimizing distance by click and distance of cell
H2b.renaming_and_merge()
## renaming the labels to have G1=="1", S=="S", G2=="2" and M=="M"
#This procedure may take a long time.
H2b.data.to_csv('H2b_data.csv',index=False,header=True)
print "\n We train a classifier for mitosis or not: \n"
obj_unnorm=H2b
train_file="MitoseClassif.arff"
train_1=Reader()
train_1.arrf_read(train_file)
train_1.renaming_for_mitosis()
train_1.data["label"].value_counts()
kfold=3
if train_1.Var_missing[0] in train_1.data.columns:
train_1.missing_features_data()
values=[100 + i*10 for i in range(15)]
model_1=RandomForest_Autotunner(values)
model_1.tunning(train_1.data[train_1.names],train_1.data["label"],kfold,plot=True,fit_new_model=True)
plt.show()
model_1.cm_normalized = model_1.cm.astype('float') / model_1.cm.sum(axis=1)[:, np.newaxis]
plot_matrix(model_1.cm_normalized,title="Normalized confusion matrix",names=["M","O","S"])
plt.show()
## To reduce computation and none useless things, we remove instances that do not belong to trajectories.
obj_norm.data=obj_norm.data.ix[pd.notnull(obj_norm.data["traj"]),obj_norm.data.columns]
obj_unnorm.data=obj_unnorm.data.ix[pd.notnull(obj_unnorm.data["traj"]),obj_unnorm.data.columns]
obj_norm.update()
obj_unnorm.update()
## Predicting model 1
index_no_missing=obj_norm.data[obj_norm.names].dropna(axis=0, how='any').index
obj_norm.data.ix[index_no_missing,classif_Mitose]=model_1.predict(obj_unnorm.data.ix[index_no_missing,train_1.names])
## Carefull, we put the unnormalized data in the above prediction.
print "\n A bit of statistics on the overall predictions: \n"
print "Frequency of predicted values for the Mitosis or not classifier: \n"
print obj_norm.data[classif_Mitose].value_counts()
print "\n We were however not able to predict %d instances because of missing values" % (obj_norm.data.shape[0]-len(index_no_missing))
obj_norm.data
obj_norm.update()
### Giving priority to the first classif...
model_1.names_to_give=train_1.names
return(obj_norm,model_1)
