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2fii.py
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import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegressionCV
import metrics
from sklearn.feature_selection import chi2
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import KFold # import KFold
from sklearn.cross_validation import cross_val_score, cross_val_predict
from sklearn.utils import shuffle
import statsmodels.formula.api as smf
from sklearn.cross_validation import train_test_split
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve
from sklearn.metrics import auc
from itertools import cycle
from scipy import interp
counter=0
chunks=1
i=1
value=19/chunks
value_train=69/chunks
print(value)
for l in range(1,2):
counter = 0
mean_chunk_arr = []
# print('#####################################################################IN L=', l,
# '###############################')
for inner in range(1,l+1):
# print('In chunk:',inner)
value = int(19 / l)
value_train=int(69/l)
counter+=1
start=int((value*counter)-value)
end=int((value*counter))
start_train=int((value_train*counter)-value_train)
end_train=((value_train*counter))
dataframe_test=pd.read_csv('C:/Users/Shanu/PycharmProjects/Arem/AReM/my_dataset_test_multiclass.csv')
dataframe_train = pd.read_csv(
'C:/Users/Shanu/PycharmProjects/Arem/AReM/my_dataset_multiclass.csv')
# dataframe.iloc[np.random.permutation(len(dataframe))]
# dataframe.reindex(np.random.permutation(dataframe.index))
# dataframe.sample(frac=1)
# dataframe=datafram1.values[:,:16]
#
# df_norm = (dataframe - dataframe.mean()) / (dataframe.max() - dataframe.min())
# # scaler = preprocessing.StandardScaler()
# # df_norm = scaler.fit_transform(dataframe)
# # df_norm = pd.DataFrame.as_matrix().astype(np.float)
# # df_norm = df_norm[np.isfinite(df_norm['FeatureColumn'])]
# df_norm.drop(['s2min', 's4min','s6min','target'], axis=1, inplace=True)
# # print(dataset.shape)
# df_norm['target']=dataframe['target']
# pd.DataFrame.to_csv(df_norm, path_or_buf='C:/Users/Shanu/PycharmProjects/Arem/AReM/my_normalized_dataset_test_multiclass.csv',
# index=False)
# # s2min and s4 min are useless
dataset_test=dataframe_test.values
dataset_train=dataframe_train.values
x_shuf=dataset_train[:,0:18]
y_shuf=dataset_train[:,18:]
x_shuf_test=dataset_test[:,0:18]
y_shuf_test=dataset_test[:,18:]
# print(x_train.shape)
# print(y_train.shape)
# x_train_prime, y_train_prime = shuffle(x_shuf,y_shuf ,random_state=0)
# x_test_prime, y_test_prime = shuffle(x_shuf_test,y_shuf_test ,random_state=0)
x_test_prime, y_test_prime = x_shuf_test,y_shuf_test
x_train_prime, y_train_prime = x_shuf,y_shuf
x_train=x_train_prime[start_train:end_train,0:18]
y_train=y_train_prime[start_train:end_train,0:18]
x_test=x_test_prime[start:end,0:18]
y_test=y_test_prime[start:end,0:18]
######################cross validation
# kf = KFold(n_splits=5) # Define the split - into 2 folds
# kf.get_n_splits(x_train) # returns the number of splitting iterations in the cross-validator
# print(kf)
# KFold(n_splits=2, random_state=None, shuffle=True)
y=[]
my_score_arr=[]
# for k,(train_index, test_index) in enumerate(kf.split(x_train,y_train)):
# print('TRAIN:', train_index)
# print('TEST:', test_index,'\n')
# X_train_K, X_test_K = x_train[train_index], x_train[test_index]
# y_train_K, y_test_K = y_train[train_index], y_train[test_index]
# X_train_K, X_test_K , y_train_K, y_test_K = train_test_split(x_train, y_train, test_size=0.3, random_state=0)
# model = LogisticRegression(penalty='l1',solver='saga',multi_class='multinomial')
model=MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)
model.fit(x_train,y_train)
preds = model.predict(x_test)
# print(X_test_K)
# model.fit(x_train[train_index], y_train[train_index])
my_score=model.score(x_test, y_test)
my_score_arr.append(my_score)
mean_chunk=np.mean(my_score_arr)
mean_chunk_arr.append(mean_chunk)
conf_matrix = confusion_matrix(y_test, preds)
sns.set()
ax = sns.heatmap(conf_matrix, annot=True, cmap="YlGnBu",
cbar_kws={'label': 'No. of Classified/Missclassified Datapoints'})
heading_conf = 'Confusion matrix for Naive Bayes with Multinomial Prior L=' + str(l)
ax.set_title(heading_conf)
plt.show()
error=mean_squared_error(y_test,preds)
print('Naive bayes with Multinomial classifier for L=',l,':',np.mean(mean_chunk_arr))