import os
import sys
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from sklearn import svm
from sklearn import metrics
import pickle
from sklearn.neural_network import MLPClassifier

if len(sys.argv) < 2:
    print("no input csv file")
    exit(0)



df = pd.read_csv(sys.argv[1])

# print(df, df.columns[0:len(df.columns)])

xcols = [ i for i in df.columns]
targ = xcols.pop()
# print(xcols)
X = df.loc[:,xcols ].values
print(X.shape)
Y = df.loc[:,targ].values
print(Y.shape)

# X = StandardScaler().fit_transform(X)
print(X)
pca_model = PCA(n_components=128)
pcaofX = pca_model.fit_transform(X)
print("shapeofX after pca",pcaofX.shape, ", cum Sum of variance ratio",pca_model.explained_variance_ratio_.cumsum()[-1])

# pcaofX = X

X_train, X_test, Y_train, Y_test = train_test_split(pcaofX, Y, test_size=0.3,random_state=109)

print("train shape",X_train.shape)

svm_model = svm.SVC(kernel="linear")
svm_model.fit(X_train,Y_train)

pred = svm_model.predict(X_test)
print(pred)

print("Accuracy:",metrics.accuracy_score(Y_test, pred))

pickle.dump(pca_model, open("pca_model.sav", 'wb'))
pickle.dump(svm_model, open("svm_model.sav", 'wb'))


# clf = MLPClassifier(activation='relu' ,solver='lbfgs', alpha=1e-9, hidden_layer_sizes = (128,8096,128), random_state=1, max_iter=3500)

# clf.fit(X_train,Y_train)
# pred2 = clf.predict(X_test)
# pickle.dump(clf, open("clf.sav", 'wb'))

# print("Accuracy:",metrics.accuracy_score(Y_test, pred2))