--- a
+++ b/src/logisticregression_cancer.py
@@ -0,0 +1,66 @@
+import time
+from datetime import datetime
+import csv
+import numpy as np
+from sklearn.linear_model import LogisticRegression
+from sklearn.decomposition import PCA
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import Pipeline
+from sklearn.cross_validation import train_test_split
+from sklearn.cross_validation import StratifiedShuffleSplit
+from sklearn.grid_search import GridSearchCV
+from sklearn.cross_validation import StratifiedKFold
+
+
+print "Script start at ", datetime.now().isoformat()
+
+X=np.load('F:/NYU/Hackathon/numpy_array.npy')
+Y=X[:,:3] #patient_id cancer_type tissue_type
+X=X[:,3:] #rpm
+
+RS=np.random.RandomState(90)
+perm=RS.permutation(678)
+
+Y=Y[perm]
+X=X[perm]
+
+X_train, X_test, Y_train, Y_test = train_test_split(X, Y[:,1], test_size=0.25, random_state=30, stratify=Y[:,1])
+
+pipe=Pipeline([('pca',PCA()), ('scaled',StandardScaler()), ('logistic_regression',LogisticRegression())])
+
+pca_val=[1,2,4,13,1046]
+
+C_vals = np.logspace(-9,9, num=19, base=2)
+
+gs=GridSearchCV(pipe, dict(pca__n_components=pca_val, logistic_regression__C=C_vals), cv=10, verbose=100)
+gs.fit(X_train, Y_train)
+
+score=gs.score(X_test, Y_test)
+
+print score
+print 'best_score'
+print gs.best_score_
+print 'best_estimator'
+print gs.best_estimator_
+print 'best_params'
+print gs.best_params_
+
+outfile="grid_logisticregression_cancer_search_scores_{0}.out".format(int(time.time()))
+
+with open(outfile, "w") as scoreFile:
+    writer = csv.writer(scoreFile, delimiter = ",")
+    paramKeys = list(gs.grid_scores_[0].parameters.keys())
+
+    writer.writerow(['mean']+ paramKeys)
+    
+    for i in gs.grid_scores_:
+        output = list()
+        output.append(i.mean_validation_score)
+
+        for k in paramKeys:
+            output.append(i.parameters.get(k))
+
+        writer.writerow(output)
+
+
+print "Script end at ", datetime.now().isoformat()
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