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actors_labelisation.py
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actors_labelisation.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Jun 23 12:21:56 2020
@author: Alex
"""
import math
import pandas as pd
import math
import pandas as pd
from sklearn.preprocessing import LabelEncoder, LabelBinarizer, OrdinalEncoder, OneHotEncoder
def imputation_previous_value(movie_ratings):
'''
Imputation in genre2, genre3, stars2 and stars3 of the previus value tu avoid Nan value
in order to analyse the data.
:param dataframe movie_ratings: dataframe with all the dataframe from movies
:return dataframe movie_ratings:dataframe with all the dataframe from movies
:rtype: dataframe
'''
i = 0
for genre in movie_ratings['genres2']:
string_test = isinstance(genre, str)
if genre != genre :
movie_ratings['genres2'][i] = movie_ratings['genres1'][i]
else:
pass
i += 1
i = 0
for genre in movie_ratings['genres3']:
string_test = isinstance(genre, str)
if genre != genre :
movie_ratings['genres3'][i] = movie_ratings['genres2'][i]
else:
pass
i += 1
# Same proccess for stars : We copy the last stars to avoid Nan cells
i = 0
for genre in movie_ratings['stars2']:
string_test = isinstance(genre, str)
if genre != genre :
movie_ratings['stars2'][i] = movie_ratings['stars1'][i]
else:
pass
i += 1
i = 0
for genre in movie_ratings['stars3']:
string_test = isinstance(genre, str)
if genre != genre :
movie_ratings['stars3'][i] = movie_ratings['stars2'][i]
else:
pass
i += 1
print(movie_ratings.info())
return movie_ratings
def labelisation(movie_ratings,genres1,genres2,genres3,stars1,stars2,stars3):
'''
Labelisation of genre2, genre3, stars2 and stars3 in order to analyse the data.
:param dataframe movie_ratings: dataframe with all the dataframe from movies
:return dataframe movie_ratings:dataframe with all the dataframe from movies
:rtype: dataframe
'''
df = movie_ratings
print(df.info())
col1 = genres1 #genre 1
col2 = genres2 #genre 2
col3 = genres3 #genre 3
col4 = stars1 #stars 1
col5 = stars2 #stars 2
col6 = stars3 #stars 3
##########################################################
################### PARTIE GENRES ######################
##########################################################
first_part = []
second_part = []
third_part = []
y_genre = df.iloc[:,col1]
y_genre = y_genre.append(df.iloc[:,col2])
y_genre = y_genre.append(df.iloc[:,col3])
#y_genre = y_genre.reset_index()
#y_genre = y_genre.drop(["index"],axis=1)
tier = int(len(y_genre)/3)
tier2 = int((len(y_genre)/3)*2)
encoder = LabelEncoder()
normal_y = encoder.fit_transform(y_genre)
i = 0
while i < len(normal_y):
if i < tier:
first_part.append(normal_y[i])
i += 1
elif i >= tier and i < tier2:
second_part.append(normal_y[i])
i += 1
elif i >= tier2:
third_part.append(normal_y[i])
i += 1
movie_ratings['genres1'] = first_part
movie_ratings['genres2'] = second_part
movie_ratings['genres3'] = third_part
##########################################################
################### PARTIE STARS #######################
##########################################################
first_part = []
second_part = []
third_part = []
y_stars = df.iloc[:,col4]
y_stars = y_stars.append(df.iloc[:,col5])
y_stars = y_stars.append(df.iloc[:,col6])
#y_stars = y_stars.reset_index()
#y_stars = y_stars.drop(["index"],axis=1)
tier = int(len(y_stars)/3)
tier2 = int((len(y_stars)/3)*2)
encoder2 = LabelEncoder()
normal_y2 = encoder2.fit_transform(y_stars)
i = 0
while i < len(normal_y2):
if i < tier:
first_part.append(normal_y2[i])
i += 1
elif i >= tier and i < tier2:
second_part.append(normal_y2[i])
i += 1
elif i >= tier2:
third_part.append(normal_y2[i])
i += 1
movie_ratings['stars1'] = first_part
movie_ratings['stars2'] = second_part
movie_ratings['stars3'] = third_part
label_genre = []
label_genre = encoder.inverse_transform(normal_y)
label_star = []
label_star = encoder2.inverse_transform(normal_y2)
df = pd.DataFrame({'index': normal_y,'genres':label_genre})
df.to_csv('correspondances_genres.csv')
df2 = pd.DataFrame({'index': normal_y2,'stars':label_star})
df2.to_csv('correspondances_stars.csv')
return movie_ratings
def return_genre_label(genre):
'''
Return the label of a genre entered in the function launch_prediction
:param String genre: genre of the movie
:return sub: label of the genre
:rtype: int
'''
df = pd.read_csv('correspondances_genres.csv')
subset = df['index'] [ df.genres == genre].values
sub = subset[0]
return sub
def return_star_label(stars):
'''
Return the label of a star entered in the function launch_prediction
:param String genre: star of the movie
:return sub: label of the star
:rtype: int
'''
df = pd.read_csv('correspondances_stars.csv')
subset = df['index'] [ df.stars == stars].values
sub = subset[0]
return sub