from sklearn.neighbors import KNeighborsClassifier
from load_dataset import load_dataset
from load_dataset import split_data
from load_dataset import accuracy_metric
import numpy as np
if __name__ == "__main__":
X_train, Y_train, MPIDs_train, X_valid, Y_valid, MPIDs_valid, X_test, Y_test, MPIDs_test = split_data(
load_dataset('unit_cell_data_16.csv', 0.2))
print("Training set information:")
print("Positive examples: " + str(sum(Y_train)))
print("Negative examples: " + str(len(Y_train) - sum(Y_train)))
clf = KNeighborsClassifier(n_neighbors=2, weights='distance')
clf.fit(X_train, Y_train)
Y_valid_predictions = np.zeros(len(Y_valid))
for i, example in enumerate(X_valid):
Y_valid_predictions[i] = clf.predict(example.reshape(1, -1))
accuracy_metric(Y_valid_predictions, Y_valid)
from sklearn.ensemble import RandomForestClassifier
from load_dataset import load_dataset
from load_dataset import split_data
from load_dataset import accuracy_metric
import numpy as np
if __name__ == "__main__":
X_train, Y_train, MPIDs_train, X_valid, Y_valid, MPIDs_valid, X_test, Y_test, MPIDs_test = split_data(
load_dataset('material_average_data.csv', 0.2))
print("Training set information:")
print("Positive examples: " + str(sum(Y_train)))
print("Negative examples: " + str(len(Y_train) - sum(Y_train)))
clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, Y_train)
Y_valid_predictions = np.zeros(len(Y_valid))
for i, example in enumerate(X_valid):
Y_valid_predictions[i] = clf.predict(example.reshape(1, -1))
accuracy_metric(Y_valid_predictions, Y_valid)
total_exps = len(total)
print("Total Examples: " + str(total_exps))
for i in range(16):
col_name = "elem" + str(i)
col_arr = np.array(total[col_name])
num_elem_i = np.sum(col_arr > 0)
print("Num elem " + str(i) + ": " + str(num_elem_i))
Y_arr = np.array(total["min emittance"])
pos_exps = np.sum(Y_arr <= 0.2)
print("Num pos exps (<= 0.2): " + str(pos_exps))
inf_exps = np.sum(Y_arr == float('inf'))
print("Num inf exps: " + str(inf_exps))
total_arr = np.array(total)
print(total_arr[0].shape)
total_no_inf = np.array([
total_arr[i] for i in range(total_exps)
if total_arr[i, 48] != float('inf')
])
print(total_no_inf.shape)
X_train, Y_train, MPIDs_train, X_valid, Y_valid, MPIDs_valid, X_test, Y_test, MPIDs_test = split_data(
load_dataset(0.2))
print("Training set information:")
print("Positive examples: " + str(sum(Y_train)))
print("Negative examples: " + str(len(Y_train) - sum(Y_train)))
import numpy as np
from sklearn.linear_model import LinearRegression
from load_dataset import load_dataset
from load_dataset import split_data
if __name__ == "__main__":
X_train, Y_train, MPIDs_train, X_valid, Y_valid, MPIDs_valid, X_test, Y_test, MPIDs_test = split_data(
load_dataset(threshold=-1))
lin_clf = LinearRegression().fit(X_train, Y_train)
predictions = lin_clf.predict(X_test)