"""

Multinomial Naive Bayes

"""

#%% Importing packages

import pandas as pd

import numpy as np

from sklearn import preprocessing

from sklearn.naive_bayes import MultinomialNB as MNB

from sklearn.model_selection import RepeatedKFold

from sklearn.metrics import accuracy_score

from operator import add

import functools as fun

#%% Importing and Defining Train/Test Sets

df = pd.read_csv("newData.csv")

# Remove null values in target

df = df[np.logical_not(df.RecruiterRelationship.isna())]

#df.head(5)

#df.columns

y = df['RecruiterRelationship']

X = df[['majorityStatus','CountryOfExploitation','typeOfExploitConcatenated']]

#%% Data Quality

def Qual_Stats(df):

return pd.DataFrame(report, columns = row.keys()).sort_values(by=['Missing %'], axis=0, ascending=False).reset_index(drop = True)

report = Qual_Stats(df)

report[['Feature','Missing %']]

#%% Encode Variables

#- Target (we can use le_y() to decode outputs after prediction)

y = y.replace(np.NaN, "-99").astype('category')

filt = np.logical_not(y == '-99')

#filt = np.logical_not(np.logical_or(y == 'Unknown', y == '-99'))

#filt = np.logical_not(np.logical_or(np.logical_or(y == 'Unknown', y == '-99'), y == 'Various'))

y = y[filt]

#- Predictors

X = pd.get_dummies(X, dummy_na = True)

X = X[filt]

le_y = preprocessing.LabelEncoder()

y = le_y.fit_transform(y)

#%% Initialize model

clf = MNB()

#%% Cross Validation using Stratisfied 10-Fold

kf = RepeatedKFold(n_splits=10, n_repeats=10, random_state=0)

scores = []

for train_idx, test_idx in kf.split(X,y):

#print("TRAIN:", train_idx, "TEST:", test_idx)

X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]

y_train, y_test = y[train_idx], y[test_idx]

model = clf.fit(X_train, y_train)

predictions = model.predict(X_test)

scores.append(accuracy_score(y_test, predictions))

print("Model training complete!")

print('Average 10-Fold Accuracy: {}'.format(np.mean(scores)))

#%%

class_probs = []

for i in range(0,len(clf.predict_proba(X_test))):

class_probs.append(list(clf.predict_proba(X_test)[i]))

print("Concatenation Complete!")

#%%

final_probs = fun.reduce(lambda x,y: list(map(add,x,y)), class_probs)

print("Addition complete!")

#%%

avg_class_probs = list(map(lambda x: (x/len(clf.predict_proba(X_test)))*100, final_probs))

print(avg_class_probs)

target_cat = le_y.inverse_transform(range(0,17))

kfold_predictions = pd.DataFrame(list(map(lambda x,y: (x,y), target_cat, avg_class_probs)))

kfold_predictions.to_csv("Predicted_Classes.csv", index = False)