"Commit_code"

corona_pred.py

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+import numpy as np
+import pandas as pd
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.naive_bayes import MultinomialNB
+import pickle
+import sys
+
+#print('Reading file...')
+infile = sys.argv[1]
+covid19df = pd.read_csv(infile)
+
+# function to convert sequence strings into k-mer words, default size = 6 (hexamer words)
+kmer_size = 6
+NGram = 4
+#KFold_val = 10
+def getKmers(sequence, size=kmer_size):
+ return [sequence[x:x+size].lower() for x in range(len(sequence) - size + 1)]
+
+#print('Creating token using K_Mer...')
+covid19df['words'] = covid19df.apply(lambda x: getKmers(x['SEQ']), axis=1)
+
+covid_texts = list(covid19df['words'])
+#test_labels = np.array(covid19df.pop('CLASS'))
+
+#print('Converting token to list...')
+for item in range(len(covid_texts)):
+ covid_texts[item] = ' '.join(covid_texts[item])
+
+
+#print('Performing Count Vectorization...')
+cv = pickle.load(open('countVectTrain.pkl', 'rb'))
+X = cv.transform(covid_texts)
+
+# load the model from disk
+filename = 'corona_pred.pkl'
+model = pickle.load(open(filename, 'rb'))
+test_pred = model.predict(X)
+pred_prob = model.predict_proba(X)
+test_pred_prob = pred_prob.max(1)*100
+
+covid19df = covid19df.drop('words', axis=1)
+
+df_test_pred = pd.DataFrame(data=test_pred, index=None, columns=["pred_label"])
+#df_test_labels = pd.DataFrame(data=test_labels, index=None, columns=["test_label"])
+df_pred_prob = pd.DataFrame(data=test_pred_prob, index=None, columns=["pred_prob_percentage"])
+
+covid19df.reset_index(inplace = True, drop = True)
+df_test_pred.reset_index(inplace = True, drop = True)
+#df_test_labels.reset_index(inplace = True, drop = True)
+df_out = pd.concat([covid19df, df_test_pred, df_pred_prob], axis=1)
+df_out.to_csv('corona_pred_out.csv', index=False)
+
+#mylist = str("Patient ID,Class <br>")
+#mylist = str("<table border = 1 ><tr><th>Sequence ID</th><th>&nbsp;&nbsp;&nbsp;Class</th><th>&nbsp;&nbsp;&nbsp;Probability (in %)</th></tr><br>")
+
+#for row in range(df_out.shape[0]):
+# mylist = mylist + "<tr><td>" + df_out.iloc[row,0] + "</td>" + "<td>&nbsp;&nbsp;&nbsp;" + str(df_out.iloc[row,2]) + "</td>" + "<td>&nbsp;&nbsp;&nbsp;" + str(df_out.iloc[row,3]) + "</td></tr><br>"
+# mylist = mylist + df_out.iloc[row,0] + "," + str(df_out.iloc[row,2]) + " <br>"
+
+#mylist = mylist + "</table>"
+#print(mylist)
+df_out = df_out.drop('SEQ', axis=1)
+df_out_html = df_out.to_html(index = False,justify = 'center')
+import re
+df_out_html = re.sub(r'PID', r'Sequence ID', df_out_html)
+df_out_html = re.sub(r'pred_label', r'Predicted Class', df_out_html)
+df_out_html = re.sub(r'pred_prob_percentage', r'Probability (in %)', df_out_html)
+print(df_out_html)

corona_train.py

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+import numpy as np
+import pandas as pd
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.naive_bayes import MultinomialNB
+import pickle
+
+# function to convert sequence strings into k-mer words, default size = 6 (hexamer words)
+kmer_size = 6
+NGram = 4
+#KFold_val = 10
+def getKmers(sequence, size=kmer_size):
+ return [sequence[x:x+size].lower() for x in range(len(sequence) - size + 1)]
+
+print('Reading file...')
+#covid19df= pd.read_csv('SARS_MERS_COV_train.csv')
+covid19df= pd.read_csv('sars_mers_cov_other_train.csv')
+
+print('Creating token using K_Mer...')
+covid19df['words'] = covid19df.apply(lambda x: getKmers(x['SEQ']), axis=1)
+covid19df = covid19df.drop('SEQ', axis=1)
+covid_texts = list(covid19df['words'])
+
+print('Converting token to list...')
+for item in range(len(covid_texts)):
+ covid_texts[item] = ' '.join(covid_texts[item])
+y_data = covid19df["CLASS"].values
+
+print('Performing Count Vectorization...')
+cv = CountVectorizer(ngram_range=(NGram,NGram))
+X = cv.fit_transform(covid_texts)
+pickle.dump(cv, open('countVectTrain.pkl', 'wb'))
+
+print('Creating Classifiers...')
+NB_classifier = MultinomialNB(alpha=0.1)
+
+NB_classifier.fit(X, y_data)
+# save the model to disk
+filename = 'corona_pred.pkl'
+pickle.dump(NB_classifier, open(filename, 'wb'))