diff tools/myTools/bin/class_and_consensus.py @ 1:7e5c71b2e71f draft default tip

Uploaded

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/tools/myTools/bin/class_and_consensus.py	Wed Dec 22 16:00:34 2021 +0000
@@ -0,0 +1,62 @@
+#!/usr/bin/env python3
+import pandas as pd
+from sklearn.naive_bayes import GaussianNB
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.svm import SVC
+from collections import Counter
+import sys
+def main():
+    train=sys.argv[1].split(',') #train attractors
+    df_attr=pd.DataFrame()
+    for j in train:
+        dfj=pd.read_csv(j, delim_whitespace=True,index_col = ["name"])
+        df_attr=pd.concat([df_attr,dfj],axis=0)
+    #df_attr=df_attr.drop_duplicates()
+    df_perturb=pd.read_csv(sys.argv[2], delim_whitespace=True,index_col=[0,1])
+    
+    df_labels=pd.read_csv(sys.argv[3], delim_whitespace=True,index_col = ["name"]) # kmeans results
+    labels=df_labels['clusters'].tolist()
+    
+    gnb = GaussianNB()
+    gnb.fit(df_attr,labels)  #do knn with attractor landscape
+    perturb_lab=gnb.predict(df_perturb) # predict clusters for perturbations
+    #create dataframe of perturabtion and clusters
+    NB_label=pd.DataFrame(index=df_perturb.index) 
+    NB_label['clusters']=perturb_lab
+    
+    regressor = RandomForestClassifier(n_estimators=100, random_state=1)
+    regressor.fit(df_attr,labels)  #do knn with attractor landscape
+    perturb_lab=regressor.predict(df_perturb) # predict clusters for perturbations
+    #create dataframe of perturabtion and clusters
+    RF_label=pd.DataFrame(index=df_perturb.index) 
+    RF_label['clusters']=perturb_lab
+
+
+    svm=SVC(gamma='auto',random_state=4) #intialize knn
+    svm.fit(df_attr,labels)  #do knn with attractor landscape
+    perturb_lab=svm.predict(df_perturb) # predict clusters for perturbations
+    #create dataframe of perturabtion and clusters
+    SVM_label=pd.DataFrame(index=df_perturb.index) 
+    SVM_label['clusters']=perturb_lab
+
+    consensus=[]
+
+    for df in (SVM_label,NB_label,RF_label):
+        df=df.where(df==0, None)
+        df['count']=df.apply(lambda x: x.count(), axis=1)
+        if df.index.nlevels>1:
+            consensus.append(df.loc[df['count']>=1].index.get_level_values('perturbation').to_list())
+        else:
+            consensus.append(df.loc[df['count']>1].index.to_list())
+    cencount=Counter(x for sublist in consensus for x in sublist)
+
+    if df.index.nlevels>1:
+        keys = [k for k, v in cencount.items() if v >=len(df.index.unique('replicate').tolist())*2]
+    else:
+        keys = [k for k, v in cencount.items()]
+    
+    with open('crit1perts.txt','w') as f:
+        for item in keys:
+            f.write("%s\n" % item)
+
+main()
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