from automatminer import MatPipe
from sklearn.model_selection import train_test_split
import sys
import pandas as pd
import numpy as np
import os
#a=sys.argv[1]
i = {'composition': [sys.argv[1]]}
rpath = sys.argv[2]
df = pd.DataFrame(i)
filename = 'D:/FYP_files/Machine_learning/pipeline/p_files/MatPipe_predict_thirdelongation_from_composition.p'
#MatPipe_predict_thirdelongation_from_composition.p
#MatPipe_predict_Ultimate_fourthtime_from_composition.p
pipe = MatPipe.load(filename)
if __name__ == '__main__':
df = pipe.predict(df)
df.to_csv('%s/elongation.csv' % rpath)
random_state=20190301,
test_size=0.2)
test_true = test['K_VRH']
test = test.drop(columns=["K_VRH"])
# MatPipe uses an sklearn-esque BaseEstimator API for fitting pipelines and
# predicting properties. Fitting a pipe trains it to the input data; predicting
# with a pipe will output predictions.
pipe.fit(train, target="K_VRH")
# Now we can predict our outputs. They'll appear in a column called
# "K_VRH predicted".
test_predicted = pipe.predict(test, "K_VRH")["K_VRH predicted"]
# Let's see how we did:
from sklearn.metrics import mean_absolute_error
mae = mean_absolute_error(test_true, test_predicted)
print("MAE on {} samples: {}".format(len(test_true), mae))
# Save a text digest of the pipeline.
pipe.digest(filename="digest.txt")
# You can now save your model
pipe.save("mat.pipe")
# Then load it later and make predictions on new data
pipe_loaded = MatPipe.load("mat.pipe")
# You have reached the end of the basic tutorial. Please see the other tutorials
# or the online documentation for more info!