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| author | elavington <elavington@hotmail.com> | 2017-08-30 15:47:56 -0400 |
|---|---|---|
| committer | elavington <elavington@hotmail.com> | 2017-08-30 15:47:56 -0400 |
| commit | 00fec851a688f0f3b65e8619391b17a96d16799a (patch) | |
| tree | f87b275e149e7bcb5aae2c669a99e26193beb2ac /cgi-bin/classifier.py | |
| parent | aec4083e9a37b58a2eb9584818d9dab740e89121 (diff) | |
| download | cressdna-00fec851a688f0f3b65e8619391b17a96d16799a.tar.gz cressdna-00fec851a688f0f3b65e8619391b17a96d16799a.zip | |
Commit changes
Diffstat (limited to 'cgi-bin/classifier.py')
| -rwxr-xr-x | cgi-bin/classifier.py | 377 |
1 files changed, 313 insertions, 64 deletions
diff --git a/cgi-bin/classifier.py b/cgi-bin/classifier.py index aa97555..ecf7c15 100755 --- a/cgi-bin/classifier.py +++ b/cgi-bin/classifier.py @@ -1,64 +1,313 @@ -<<<<<<< HEAD -#!/home/erik/bin/python3.6m -======= -#!/home/erik/bin/python3.6 ->>>>>>> f7d7849fcd6ce02a59db8c5fadc29d1962476493 - -#import packages to be used -from sklearn.svm import SVC -from sklearn.feature_extraction.text import CountVectorizer -from sklearn.preprocessing import StandardScaler -from sklearn.externals import joblib -import cgi, cgitb - -cgitb.enable() -form=cgi.FieldStorage() -if form.getvalue('fasta'): - alignment = form.getvalue('fasta') - alignment=[alignment] - name=form.getvalue('seqname') - size=len(alignment[0]) -else: - alignment = ["MPSKKSGPQPHKRWVFTLNNPSEEEKNKIRELPISLFDYFVCGEEGLEEGRTAHLQGFANFAKKQTFNKVKWYFGARCHIEKAKGTDQQNKEYCSKEGHILIECGAPRNQGKRSDLSTAYFDYQQSGPPGMVLLNCCPSCRSSLSEDYYFAILEDCWRTINGGTRRPI"] - name='demo' - size=len(alignment[0]) - -<<<<<<< HEAD -html = open("./var/www/html/CRESSresults.html") -======= -html = open("./www.html/CRESSresults.html") ->>>>>>> f7d7849fcd6ce02a59db8c5fadc29d1962476493 -page=html.read() - - -AAs=['a','c','d','e','f','g','h','i','k','l','m','n','p','q','r','s','t','v','w','y'] -clf=joblib.load("./cgi-bin/SVM_linear_aa_clf.pkl") -StSc=joblib.load("./cgi-bin/UniqRepsGemys_6089_StSCALER.pkl") -cv=CountVectorizer(analyzer='char',ngram_range=(1,1),vocabulary=AAs) - - -#initialize text data vectorizer - -dataVect=cv.transform(alignment) - -#Scale the data to the training set -X=StSc.transform(dataVect.astype("float64")) - -#make predictions for the original dataset -results=",".join([name,clf.predict(X)[0]]) -results=",".join([results,str(size)]) -#for i in results: - #print(i[0],"\t",i[1]) - -output = page.format(prediction=results) -"""f=open('test.html','w') -f.write(output) -f.close()""" -print (output) - - -<<<<<<< HEAD -quit() -======= -quit() ->>>>>>> f7d7849fcd6ce02a59db8c5fadc29d1962476493 +#!/home/erik/bin/python3.6
+
+#import packages to be used
+from sklearn.svm import SVC
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.preprocessing import StandardScaler
+from sklearn.externals import joblib
+import cgi, cgitb
+
+#----------------------------------------------\
+# Parse the web-form information to variables \
+# \_______________________________________________________
+# |
+cgitb.enable()
+form=cgi.FieldStorage()
+alignment = form.getvalue('fasta')
+if alignment.startswith(">"): #naive check for FASTA format
+ list=alignment.split(">")
+ book={}
+ for a in list:
+ tempList=a.splitlines()
+ nameLine=tempList.pop(0)
+ name=nameLine.split(" ")[0]
+ seq="".join(tempList)
+ book[name]=seq
+ seqList=[]
+ lenList=[]
+ nameList=[]
+ for i in book:
+ nameList.append(i)
+ seqList.append(book[i])
+ lenList.append(str(len(book[i])))
+
+ if len(seqList)=0: #check for empty sequence list
+ seqList = ["MPSKKSGPQPHKRWVFTLNNPSEEEKNKIRELPISLFDYFVCGEEGLEEGRTAHLQGFANFAKKQTFNKVKWYFGARCHIEKAKGTDQQNKEYCSKEGHILIECGAPRNQGKRSDLSTAYFDYQQSGPPGMVLLNCCPSCRSSLSEDYYFAILEDCWRTINGGTRRPI"]
+ nameList=['demo']
+ lenList=[str(len(alignment[0]))]
+
+else:
+ seqList = ["MPSKKSGPQPHKRWVFTLNNPSEEEKNKIRELPISLFDYFVCGEEGLEEGRTAHLQGFANFAKKQTFNKVKWYFGARCHIEKAKGTDQQNKEYCSKEGHILIECGAPRNQGKRSDLSTAYFDYQQSGPPGMVLLNCCPSCRSSLSEDYYFAILEDCWRTINGGTRRPI"]
+ nameList=['demo']
+ lenList=[str(len(alignment[0]))]
+
+#--------------------------------------------------------------------------------------------------------+
+
+#----------------------------------------------\
+# predict genus of input sequences \
+# \_______________________________________________________
+# |
+#list of amino acids as vocabulary for the CountVectorizer
+AAs=['a','c','d','e','f','g','h','i','k','l','m','n','p','q','r','s','t','v','w','y']
+
+#load the classifier and scaler
+clf=joblib.load("./cgi-bin/SVM_linear_aa_clf.pkl")
+StSc=joblib.load("./cgi-bin/UniqRepsGemys_6089_StSCALER.pkl")
+cv=CountVectorizer(analyzer='char',ngram_range=(1,1),vocabulary=AAs)
+
+#initialize text data vectorizer
+dataVect=cv.transform(seqList)
+
+#Scale the data to the training set
+X=StSc.transform(dataVect.astype("float64"))
+
+#make predictions for the original dataset
+predictions=clf.predict(X)
+
+
+#----------------------------------------------\
+# Build HTML table of results \
+# \_______________________________________________________
+# |
+results=""""""
+for k in len(seqList):
+ results+="""<tr><td>{0}</td><td>{1}</td><td>{2}</td></tr>""".format(nameList[k],lenList[k],predictions[k])
+if "demo" in nameList:
+ results+="""<p>There seems to have been an error.<br>If you are expecting more than one prediction or
+ do not see the name you entered please try the submission form again, making sure that the input is in FASTA format."""
+
+#----------------------------------------------\
+# Build output page \
+# \_______________________________________________________
+# |
+#build output page parts
+#Header and CSS Style bits
+header="""
+<!DOCTYPE html>
+
+<html>
+<head>
+<style>
+* {box-sizing: border-box}
+body {font-family: "Lato", sans-serif;}
+/* Style the tab */
+div.tab {
+ float: left;
+ border: 1px solid #ccc;
+ background-color: #f1f1f1;
+ width: 20%;
+ height: 250px;
+}
+/* Style the buttons inside the tab */
+div.tab button {
+ display: block;
+ background-color: inherit;
+ color: black;
+ padding: 22px 16px;
+ width: 100%;
+ border: none;
+ outline: none;
+ text-align: left;
+ cursor: pointer;
+ transition: 0.3s;
+ font-size: 17px;
+}
+/* Change background color of buttons on hover */
+div.tab button:hover {
+ background-color: #ddd;
+}
+/* Create an active/current "tab button" class */
+div.tab button.active {
+ background-color: #1acefc;
+}
+/* Style the tab content */
+.tabcontent {
+ float: left;
+ padding: 0px 12px;
+ border: 1px solid #ccc;
+ width: 80%;
+ min-height: 250px;
+}
+table {
+ border-collapse: collapse;
+ width: 80%;
+}
+
+th, td {
+ text-align: left;
+ padding: 8px;
+}
+
+tr:nth-child(even){background-color: #f2f2f2}
+
+th {
+ background-color: #ff0000;
+ color: white;
+}
+
+</style>
+</head>
+"""
+
+#Page contents, first part
+body1="""
+<body>
+
+<p>Welcome to CRESSdna.org</p>
+
+<div class="tab">
+ <button class="tablinks" onclick="openTab(event, 'Home')" id="defaultOpen">Home</button>
+ <button class="tablinks" onclick="openTab(event, 'Taxonomy')">Taxonomy</button>
+ <button class="tablinks" onclick="openTab(event, 'Contact')">Contact</button>
+ <button class="tablinks" onclick="openTab(event, 'Results')">Results</button>
+ </div>
+
+<div id="Home" class="tabcontent">
+ <h3>Home</h3>
+ <p>Part of the <a href='http://www.nsf.gov/pubs/2010/nsf10513/nsf10513.htm'>National Science Foundation's Assembling the Tree of Life</a>.</p>
+ <img src='nsf1.jpg' alt='Sponsored with a Grant from the National Science Foundation'>
+</div>
+
+<div id="Taxonomy" class="tabcontent">
+ <h3>Taxonomy</h3>
+ <p>Please enter only one word as the name(no space) and only one Rep sequence</p>
+ <form action="./cgi-bin/classifier.py" method="post"><br>
+ <input type="text" name="seqname" value="seqID"><br>
+ <textarea rows="4" cols="50" name="fasta" input type="submit">
+Enter ONE Rep protein sequence here...</textarea>
+ <br>
+ <input type="reset">
+ <input type="submit">
+</form>
+ <p>
+ <ul>
+ <li>This classifier requires Rep protein sequence to be:</li>
+ <ul>
+ <li>Complete</li>
+ <li>Unaligned</li>
+ <li>in FASTA format</li>
+ </ul>
+ <p>And has been trained on the following Genera:</p>
+ <li>Circoviridae</li>
+ <ul>
+ <li>Circovirus</li>
+ <li>Cyclovirus</li>
+ </ul>
+ <li>Nanoviridae</li>
+ <ul>
+ <li>Babuvirus</li>
+ <li>Nanovirus</li>
+ </ul>
+ <li>Genomoviridae</li>
+ <ul>
+ <li>Gemycircularvirus</li>
+ <li>Gemygorvirus</li>
+ <li>Gemykibivirus</li>
+ <li>Gemykolovirus</li>
+ <li>Gemykrogvirus</li>
+ <li>Gemyvongvirus</li>
+ </ul>
+ <li>Geminiviridae</li>
+ <ul>
+ <li>Becurtovirus</li>
+ <li>Begomovirus</li>
+ <li>Capulavirus</li>
+ <li>Curtovirus</li>
+ <li>Eragrovirus</li>
+ <li>Grablovirus</li>
+ <li>Mastrevirus</li>
+ <li>Turncurtovirus</li>
+ </ul>
+ <li>Smacovirus</li>
+</ul> </p>
+</div>
+<div id="Contact" class="tabcontent">
+ <h3>Contact</h3>
+ <p>Questions or comments? Send us an email:</p>
+ <p>email At domain Dot something</p>
+</div>
+
+<div id="Results" class="tabcontent">
+ <h3>Results</h3>
+ <p>Results from Taxonomy prediction</p>
+ <table>
+ <tr>
+ <th>Sequence Name</th>
+ <th>Length</th>
+ <th>Prediction</th>
+ </tr>
+"""
+
+#Page contents, second part (results fit between body1 and body2)
+body2="""
+</table>
+ <p>This classifier will return the best fit of the submitted sequence to the training data.<br>
+Currently included in the training data:<br>
+<li>Circoviridae</li>
+ <ul>
+ <li>Circovirus</li>
+ <li>Cyclovirus</li>
+ </ul>
+ <li>Nanoviridae</li>
+ <ul>
+ <li>Babuvirus</li>
+ <li>Nanovirus</li>
+ </ul>
+ <li>Genomoviridae</li>
+ <ul>
+ <li>Gemycircularvirus</li>
+ <li>Gemygorvirus</li>
+ <li>Gemykibivirus</li>
+ <li>Gemykolovirus</li>
+ <li>Gemykrogvirus</li>
+ <li>Gemyvongvirus</li>
+ </ul>
+ <li>Geminiviridae</li>
+ <ul>
+ <li>Becurtovirus</li>
+ <li>Begomovirus</li>
+ <li>Capulavirus</li>
+ <li>Curtovirus</li>
+ <li>Eragrovirus</li>
+ <li>Grablovirus</li>
+ <li>Mastrevirus</li>
+ <li>Turncurtovirus</li>
+ </ul>
+ <li>Smacovirus</li>
+<br><br>
+</p>
+</div>
+
+<script>
+function openTab(evt, tabTitle) {
+ var i, tabcontent, tablinks;
+ tabcontent = document.getElementsByClassName("tabcontent");
+ for (i = 0; i < tabcontent.length; i++) {
+ tabcontent[i].style.display = "none";
+ }
+ tablinks = document.getElementsByClassName("tablinks");
+ for (i = 0; i < tablinks.length; i++) {
+ tablinks[i].className = tablinks[i].className.replace(" active", "");
+ }
+ document.getElementById(tabTitle).style.display = "block";
+ evt.currentTarget.className += " active";
+}
+// Get the element with id="defaultOpen" and click on it
+document.getElementById("defaultOpen").click();
+</script>
+</body>
+"""
+
+#close the Page
+footer="""
+</html>
+"""
+
+#build the output page
+page=header+body1+results+body2+footer
+
+#send the output as html
+output = page.format()
+print (output)
+
+quit()
\ No newline at end of file |