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Author: tirthajyoti

Memory-profiling/Scalene/linearmodel.py

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+import pandas as pd 
+import pickle
+import numpy as np
+from sklearn.linear_model import LinearRegression
+from sklearn.datasets import make_regression
+
+NUM_FEATURES = 10
+NUM_SAMPLES = 1000
+
+# Make data
+def make_data():
+    X,y = make_regression(n_samples=NUM_SAMPLES,n_features=NUM_FEATURES,
+                      n_informative=NUM_FEATURES,noise=0.5)
+    data = pd.DataFrame(X,columns=['X'+str(i) for i in range(1,NUM_FEATURES+1)],dtype=np.float16)
+    data['y']=np.array(y,dtype=np.float16)
+    return data
+
+# Test/Train
+def test_train(data):
+    X_train,y_train = data.iloc[:int(NUM_SAMPLES/2)].drop(['y'],axis=1),data.iloc[:int(NUM_SAMPLES/2)]['y']
+    X_test,y_test = data.iloc[int(NUM_SAMPLES/2):].drop(['y'],axis=1),data.iloc[int(NUM_SAMPLES/2):]['y']
+    return (X_train,y_train,X_test,y_test)
+
+# Fitting
+def fitting(X_train,y_train):
+    lm = LinearRegression(n_jobs=1)
+    lm.fit(X_train,y_train)
+    del X_train
+    del y_train
+    return lm
+
+# Saving model
+def save(lm):
+    with open('LinearModel.sav',mode='wb') as f:
+        pickle.dump(lm,f)
+        
+def model_run(model,testfile):
+    """
+    Loads and runs a sklearn linear model
+    """
+    lm = pickle.load(open(model, 'rb'))
+    X_test = pd.read_csv(testfile)
+    _= lm.predict(X_test)
+    return None
+
+if __name__ == '__main__':
+    data = make_data()
+    X_train,y_train,X_test,y_test = test_train(data)
+    #X_test.to_csv("Test.csv",index=False)
+    lm = fitting(X_train,y_train)
+    save(lm)
+    model_run('LinearModel.sav','Test.csv')