scRNA-seq_analysis

pipelines/16_train_doublets_classifier/svm.py

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+import pandas as pd
+from sklearn.metrics import classification_report
+from sklearn.metrics import confusion_matrix
+from sklearn.svm import SVC
+from sklearn.model_selection import GridSearchCV
+import pickle
+
+import sys
+from os.path import join
+args = sys.argv
+save_to = args[1]
+
+print("Loading data ...")
+X = pd.read_csv(join(save_to, "./data.csv"), sep = ",", index_col = 0).values
+y = pd.read_csv(join(save_to, "./labels.csv")).values[:, 0].reshape(-1, 1).ravel()
+
+from sklearn.decomposition import PCA
+pca = PCA(n_components = .8)
+X = pca.fit_transform(X)
+
+modelFile = open(join(save_to, "pca.pickle"), "wb")
+print(modelFile)
+modelFile.write(pickle.dumps(pca))
+modelFile.close()
+
+print(X.shape)
+
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=19)
+
+params = {"C":[1, 10, 100, 300]}
+
+print("Creating the model and fitting the data ...")
+model = GridSearchCV(SVC(probability = False, kernel = "rbf"), params, cv=3)
+model.fit(X_train, y_train)
+
+print("Testing ...")
+pred = model.predict(X)
+cls_report = classification_report(y, pred, target_names = model.classes_)
+print(cls_report)
+with open(join(save_to, "classification_report.txt"), "w")  as cl_f:
+    cl_f.write(cls_report)
+
+print("Saving model and confusion matrix to disk ...")
+cnf_matrix = confusion_matrix(y, pred)
+df = pd.DataFrame(cnf_matrix)
+df.columns = model.classes_
+df.to_csv(join(save_to, "confusion_matrix.csv"))
+
+modelFile = open(join(save_to, "model.pickle"), "wb")
+modelFile.write(pickle.dumps(model))
+modelFile.close()
+
+print(model.best_params_)