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Aula 4 Video 1 - Conhecendo DecisionTreeRegressor





In [201]:



    


import pandas as pd
import numpy as np 
from sklearn import tree
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

Download Dataset: movies_multilinear_reg.csv





In [202]:



    


filmes = pd.read_csv('datasets/movies_multilinear_reg.csv')



    











In [203]:



    


filmes.head(5)



    


















    
Out[203]:











  
    

      
      movieId
      Titulo
      Documentary
      Sci-Fi
      Mystery
      Horror
      Romance
      Thriller
      Crime
      Fantasy
      Comedy
      Animation
      Children
      Drama
      Adventure
      Duracao
      Investimento
      Bilheteria
    

  
  
    

      0
      1
      Toy Story (1995)
      0
      0
      0
      0
      0
      0
      0
      1
      1
      1
      1
      0
      1
      103.468310
      11.048216
      5623234.602
    

    

      1
      2
      Jumanji (1995)
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      1
      0
      1
      112.337916
      14.927678
      5714951.757
    

    

      2
      3
      Grumpier Old Men (1995)
      0
      0
      0
      0
      1
      0
      0
      0
      1
      0
      0
      0
      0
      116.245732
      27.114597
      9524339.124
    

    

      3
      4
      Waiting to Exhale (1995)
      0
      0
      0
      0
      1
      0
      0
      0
      1
      0
      0
      1
      0
      120.317732
      4.994242
      6331568.779
    

    

      4
      5
      Father of the Bride Part II (1995)
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      0
      0
      110.023572
      19.142246
      6409617.277
    

  




















In [204]:



    


filmes.shape



    


















    
Out[204]:








(9125, 18)

















In [205]:



    


filmes_caract = filmes[filmes.columns[2:17]]
filmes_labels = filmes[filmes.columns[17:]]



    











In [206]:



    


filmes_caract.head(5)



    


















    
Out[206]:











  
    

      
      Documentary
      Sci-Fi
      Mystery
      Horror
      Romance
      Thriller
      Crime
      Fantasy
      Comedy
      Animation
      Children
      Drama
      Adventure
      Duracao
      Investimento
    

  
  
    

      0
      0
      0
      0
      0
      0
      0
      0
      1
      1
      1
      1
      0
      1
      103.468310
      11.048216
    

    

      1
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      1
      0
      1
      112.337916
      14.927678
    

    

      2
      0
      0
      0
      0
      1
      0
      0
      0
      1
      0
      0
      0
      0
      116.245732
      27.114597
    

    

      3
      0
      0
      0
      0
      1
      0
      0
      0
      1
      0
      0
      1
      0
      120.317732
      4.994242
    

    

      4
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
      0
      0
      110.023572
      19.142246
    

  




















In [207]:



    


filmes_labels.head(5)



    


















    
Out[207]:











  
    

      
      Bilheteria
    

  
  
    

      0
      5623234.602
    

    

      1
      5714951.757
    

    

      2
      9524339.124
    

    

      3
      6331568.779
    

    

      4
      6409617.277
    

  




















In [208]:



    


treino, teste, treino_labels, teste_labels = train_test_split(filmes_caract, filmes_labels)



    











In [209]:



    


print('Shape do treino {}, Shape do teste {}'.format(treino.shape, teste.shape))



    


















    






Shape do treino (6843, 15), Shape do teste (2282, 15)

















In [210]:



    


porc_treino = len(treino)/len(filmes_caract)*100
porc_teste =  len(teste)/len(filmes_caract)*100
print('Proporção Treino / Teste: {0:.2f}% / {1:.2f}%'.format(porc_treino, porc_teste))



    


















    






Proporção Treino / Teste: 74.99% / 25.01%

















In [211]:



    


treino_arr = np.array(treino).reshape(len(treino),15)
teste_arr = np.array(teste).reshape(len(teste),15)



    











In [212]:



    


print('Tipo e shape do array treino {} - {}'.format(type(treino_arr), treino_arr.shape))



    


















    






Tipo e shape do array treino <class 'numpy.ndarray'> - (6843, 15)

















In [213]:



    


print('Tipo e shape do array teste {} - {}'.format(type(teste_arr), teste_arr.shape))



    


















    






Tipo e shape do array teste <class 'numpy.ndarray'> - (2282, 15)

















In [214]:



    


treino_labels_arr = np.array(treino_labels).reshape(len(treino_labels),1)
teste_labels_arr = np.array(teste_labels).reshape(len(teste_labels),1)

Usando modelo DecisionTreeRegressor





In [215]:



    


modelo = tree.DecisionTreeRegressor()
modelo.fit(treino_arr, treino_labels_arr)



    


















    
Out[215]:








DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,
           max_leaf_nodes=None, min_impurity_decrease=0.0,
           min_impurity_split=None, min_samples_leaf=1,
           min_samples_split=2, min_weight_fraction_leaf=0.0,
           presort=False, random_state=None, splitter='best')

















In [216]:



    


score_treino = modelo.score(treino_arr, treino_labels_arr)
score_teste = modelo.score(teste_arr, teste_labels_arr)
print('DecisionTreeRegressor: Acertamos {0:.2f}% no treino, Acertamos {1:.2f}% no teste'.
      format(score_treino * 100, score_teste * 100))



    


















    






DecisionTreeRegressor: Acertamos 100.00% no treino, Acertamos 66.12% no teste

Usando modelo LinearRegression





In [217]:



    


modelo_reg = LinearRegression()
modelo_reg.fit(treino_arr, treino_labels_arr)



    


















    
Out[217]:








LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

















In [218]:



    


score_treino = modelo_reg.score(treino_arr, treino_labels_arr)
score_teste = modelo_reg.score(teste_arr, teste_labels_arr)
print('LinearRegression: Acertamos {0:.2f}% no treino, Acertamos {1:.2f}% no teste'.
      format(score_treino * 100, score_teste * 100))



    


















    






LinearRegression: Acertamos 82.61% no treino, Acertamos 83.25% no teste

Aula 4 Video 2 - Classificadores e métricas





In [219]:



    


modelo_depth5 = tree.DecisionTreeRegressor(max_depth=5)
modelo_depth5.fit(treino_arr, treino_labels_arr)



    


















    
Out[219]:








DecisionTreeRegressor(criterion='mse', max_depth=5, max_features=None,
           max_leaf_nodes=None, min_impurity_decrease=0.0,
           min_impurity_split=None, min_samples_leaf=1,
           min_samples_split=2, min_weight_fraction_leaf=0.0,
           presort=False, random_state=None, splitter='best')

















In [220]:



    


score_max5_treino = modelo_depth5.score(treino_arr, treino_labels_arr)
score_max5_teste = modelo_depth5.score(teste_arr, teste_labels_arr)
print('DecisionTreeRegressor(max_depth=5): Acertamos {0:.2f}% no treino, Acertamos {1:.2f}% no teste'.
      format(score_max5_treino * 100, score_max5_treino * 100))



    


















    






DecisionTreeRegressor(max_depth=5): Acertamos 80.73% no treino, Acertamos 80.73% no teste

















In [221]:



    


zootopia = [0,0,0,0,0,0,0,1,1,1,1,0,1,110,27.74456356]
modelo_depth5.predict([zootopia])



    


















    
Out[221]:








array([ 7899402.77132227])

Gostos do usuário

Download Dataset: avaliacoes_usuario.csv





In [222]:



    


gostos = pd.read_csv('datasets/avaliacoes_usuario.csv')
gostos.head(5)



    


















    
Out[222]:











  
    

      
      Titulo
      Documentary
      Sci-Fi
      Mystery
      Horror
      Romance
      Thriller
      Crime
      Fantasy
      Comedy
      Animation
      Children
      Drama
      Adventure
      Duracao
      Investimento
      Gostou
    

  
  
    

      0
      Nixon (1995)
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      0
      114.496547
      7.930748
      1
    

    

      1
      Leaving Las Vegas (1995)
      0
      0
      0
      0
      1
      0
      0
      0
      0
      0
      0
      1
      0
      110.140191
      18.276555
      1
    

    

      2
      Persuasion (1995)
      0
      0
      0
      0
      1
      0
      0
      0
      0
      0
      0
      1
      0
      105.747597
      16.582232
      1
    

    

      3
      Babe (1995)
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
      1
      0
      126.131978
      13.004553
      1
    

    

      4
      Carrington (1995)
      0
      0
      0
      0
      1
      0
      0
      0
      0
      0
      0
      1
      0
      85.025469
      14.418120
      1
    

  




















In [223]:



    


caract = gostos[gostos.columns[1:16]]
labels = gostos[gostos.columns[16:]]



    











In [224]:



    


treino, teste, treino_labels, teste_labels = train_test_split(caract, labels)



    











In [225]:



    


treino = np.array(treino).reshape(len(treino), 15)
teste = np.array(teste).reshape(len(teste), 15)
treino_labels = treino_labels.values.ravel()
teste_labels = teste_labels.values.ravel()



    











In [226]:



    


print('Tipos: treino {}, teste {}, treino_labels {}, teste_labels {}'.
      format(type(treino), type(teste), type(treino_labels), type(teste_labels)))



    


















    






Tipos: treino <class 'numpy.ndarray'>, teste <class 'numpy.ndarray'>, treino_labels <class 'numpy.ndarray'>, teste_labels <class 'numpy.ndarray'>

Usando LogisticRegression





In [227]:



    


from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score



    











In [228]:



    


model = LogisticRegression()
model.fit(treino, treino_labels)



    


















    
Out[228]:








LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)

















In [229]:



    


previsoes = model.predict(teste)
acuracia = accuracy_score(teste_labels, previsoes)
print('Acuracia com LogisticRegression no teste: {0:.2f}%'.format(acuracia * 100))



    


















    






Acuracia com LogisticRegression no teste: 80.00%

Usando DecissionTreeClassifier





In [230]:



    


modelo = tree.DecisionTreeClassifier()
modelo.fit(treino, treino_labels)



    


















    
Out[230]:








DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,
            splitter='best')

















In [231]:



    


previsoes = modelo.predict(teste)
acuracia = accuracy_score(teste_labels, previsoes)
print('Acuracia com DecisionTreeClassifier no teste: {0:.2f}%'.format(acuracia * 100))
previsoes = modelo.predict(treino)
acuracia = accuracy_score(treino_labels, previsoes)
print('Acuracia com DecisionTreeClassifier com treino: {0:.2f}%'.format(acuracia * 100))



    


















    






Acuracia com DecisionTreeClassifier no teste: 70.00%
Acuracia com DecisionTreeClassifier com treino: 100.00%

Usando DecisionTreeClassifier(max_depth=5)





In [232]:



    


modelo_max5 = tree.DecisionTreeClassifier(max_depth=5)
modelo_max5.fit(treino, treino_labels)
previsoes = modelo_max5.predict(teste)
acuracia = accuracy_score(teste_labels, previsoes)
print('Acuracia com DecisionTreeClassifier(max_depth=5) no teste: {0:.2f}%'.format(acuracia * 100))
previsoes = modelo_max5.predict(treino)
acuracia = accuracy_score(treino_labels, previsoes)
print('Acuracia com DecisionTreeClassifier(max_depth=5) com treino: {0:.2f}%'.format(acuracia * 100))



    


















    






Acuracia com DecisionTreeClassifier(max_depth=5) no teste: 75.29%
Acuracia com DecisionTreeClassifier(max_depth=5) com treino: 85.63%

















In [ ]:

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