print 'Please give frovedis_server calling command as the first argument \n(e.g. "mpirun -np 2 -x /opt/nec/nosupport/frovedis/ve/bin/frovedis_server")' quit() FrovedisServer.initialize(argvs[1]) mat = pd.read_csv("./input/train_1.csv") lbl = np.array([1, 0, 0, 1], dtype=np.float64) lbl1 = np.array([0.2, 0.3, 0.8, 0.6]) c_temp = 0 r_temp = 0 # fitting input matrix and label on DecisionTree Classifier object dtc1 = DecisionTreeClassifier() try: dtc = dtc1.fit(mat, lbl) except TypeError, e: c_temp = 1 # fitting input matrix and label on DecisionTree Regressor object dtr1 = DecisionTreeRegressor() try: dtr = dtr1.fit(mat, lbl1) except TypeError, e: r_temp = 1 if c_temp == 1 and r_temp == 1: print("Status : Passed") else: print("Status : Failed") FrovedisServer.shut_down()
class_weight=None, presort=False, verbose = 0) dtc = dtc1.fit(mat,lbl) dtc.debug_print() # predicting on train model print("predicting on DecisionTree classifier model: ") dtcm = dtc.predict(mat[2:3]) print dtcm print("Accuracy score for predicted DecisionTree Classifier model") print dtc.score(mat,lbl) # fitting input matrix and label on DecisionTree Regressor object dtr1 = DecisionTreeRegressor(criterion='mse', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=1, min_impurity_decrease=0.0, min_impurity_split=None, class_weight=None, presort=False, verbose = 0) lbl1 = np.array([1.2,0.3,1.1,1.9]) dtr = dtr1.fit(mat,lbl1) dtr.debug_print() # predicting on train model print("predicting on DecisionTree Regressor model: ") dtrm = dtr.predict(mat[2:3]) print dtrm print("Root mean square for predicted DecisionTree Regressor model") print dtr.score(mat,lbl1) if (lbl[2] == dtcm) and (lbl1[2] == dtrm): print("Status: Passed")
lbl = np.array([0.0, 1.0, 1.0, 0.0]) # fitting input matrix and label on DecisionTree Classifier object dtc1 = DecisionTreeClassifier(max_depth=None) dtc = dtc1.fit(mat, lbl) dtc.debug_print() # predicting on train model print("predicting on DecisionTree classifier model: ") dtcm = dtc.predict(mat) print dtcm print("Accuracy score for predicted DecisionTree Classifier model") print dtc.score(mat, lbl) # fitting input matrix and label on DecisionTree Regressor object dtr1 = DecisionTreeRegressor(max_depth=None) lbl1 = np.array([1.2, 0.3, 1.1, 1.9]) dtr = dtr1.fit(mat, lbl1) dtr.debug_print() # predicting on train model print("predicting on DecisionTree Regressor model: ") dtrm = dtr.predict(mat) print dtrm print("Root mean square for predicted DecisionTree Regressor model") print dtr.score(mat, lbl1) if (lbl == dtcm).all() and (dtrm == lbl1).all(): print("Status: Passed") else: print("Status: Failed") #clean-up
[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0], [1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0]]) lbl = np.array([0.0, 1.0, 1.0, 0.0]) lbl1 = np.array([0.2,0.1,0.8,0.6]) c_temp = 0 r_temp = 0 # fitting input matrix and label on DecisionTree Classifier object dtc1 = DecisionTreeClassifier(min_samples_leaf=-1) try: dtc = dtc1.fit(mat,lbl) except ValueError, e: c_temp = 1 # fitting input matrix and label on DecisionTree Regressor object dtr1 = DecisionTreeRegressor(min_samples_leaf=-1) try: dtr = dtr1.fit(mat,lbl1) except ValueError, e: r_temp = 1 if c_temp == 1 and r_temp == 1: print("Status : Passed") else: print("Status : Failed") FrovedisServer.shut_down()
# fitting input matrix and label on DecisionTree Classifier object dtc = DecisionTreeClassifier(criterion='gini', max_depth=5) dtc.fit(mat, lbl) #dtc.debug_print() # predicting on train model print("predicting on DecisionTree classifier model: ") print(dtc.predict(mat)) print("predicting probability on DecisionTree classifier model: ") print(dtc.predict_proba(mat)) print("prediction accuracy: %.4f" % (dtc.score(mat, lbl))) # regression data from sklearn.datasets import load_boston mat, lbl = load_boston(return_X_y=True) # fitting input matrix and label on DecisionTree Regressor object dtr = DecisionTreeRegressor(criterion='mse', max_depth=5) dtr.fit(mat, lbl) #dtr.debug_print() # predicting on train model print("predicting on DecisionTree Regressor model: ") print(dtr.predict(mat)) print("prediction score: %.4f" % (dtr.score(mat, lbl))) #clean-up #dtc.release() #dtr.release() FrovedisServer.shut_down()
[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0], [1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0]]) lbl = np.array([0.0, 1.0, 1.0, 0.0]) lbl1 = np.array([0.2,0.1,0.8,0.6]) c_temp = 0 r_temp = 0 # fitting input matrix and label on DecisionTree Classifier object dtc1 = DecisionTreeClassifier(criterion='mse') try: dtc = dtc1.fit(mat,lbl) except ValueError, e: c_temp = 1 # fitting input matrix and label on DecisionTree Regressor object dtr1 = DecisionTreeRegressor(criterion='gini') try: dtr = dtr1.fit(mat,lbl1) except ValueError, e: r_temp = 1 if c_temp == 1 and r_temp == 1: print("Status : Passed") else: print("Status : Failed") FrovedisServer.shut_down()