In [1]:
import numpy as np
import pandas as pd
# Creamos las categorias
sexo=['M','F','Otro']
estatura= ['<150cm','150-170cm','170-190cm','>190cm']
cabello=['Corto','Mediano (hasta orejas)','Largo (supera hombros)']
color=['Rojo','Rubio','Castaño','Negro','Otro']
delineador=['Si','No']
In [51]:
# Creacion de datos artificiales
v_sexo=np.random.choice(sexo, 1000)
v_estatura=np.random.choice(estatura, 1000)
v_cabello=np.random.choice(cabello, 1000)
v_color=np.random.choice(color, 1000)
#v_delineador=np.random.choice(delineador, 100)
In [65]:
# Creacion del dataframe
df=pd.DataFrame()
df['Sexo']=v_sexo
df['Estatura']=v_estatura
df['Cabello']=v_cabello
df['Color']=v_color
df['Delineador'] = np.where(((df['Sexo']=='F') & ((df['Estatura'] == '<150cm')|(df['Estatura'] == '150-170cm'))) |
((df['Sexo']=='M') & ((df['Estatura'] == '150-170cm')|(df['Estatura'] == '>190cm'))),
'Si', 'No')
df.head()
Out[65]:
| Sexo | Estatura | Cabello | Color | Delineador | |
|---|---|---|---|---|---|
| 0 | Otro | 150-170cm | Corto | Rojo | No |
| 1 | M | >190cm | Largo (supera hombros) | Otro | Si |
| 2 | Otro | 170-190cm | Largo (supera hombros) | Negro | No |
| 3 | Otro | >190cm | Largo (supera hombros) | Rojo | No |
| 4 | Otro | >190cm | Corto | Rojo | No |
In [66]:
df.Delineador.value_counts()
Out[66]:
No 677 Si 323 Name: Delineador, dtype: int64
In [67]:
# Split en train y test
X= df.drop(columns='Delineador')
y= df['Delineador']
#from sklearn.preprocessing import OneHotEncoder
X_new=pd.get_dummies(X)
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X_new, y, test_size=0.33, random_state=42)
In [68]:
# Entrenar el arbol
from sklearn import tree
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X_train, y_train)
In [69]:
y_train.unique()
Out[69]:
array(['No', 'Si'], dtype=object)
In [70]:
# Graficando
from matplotlib import pyplot as plt
fig = plt.figure(figsize=(18,10))
_ = tree.plot_tree(clf,feature_names=X_train.columns,
class_names=y_train.unique(),
filled=True)
In [71]:
y_pred= clf.predict(X_test)
In [72]:
from sklearn.metrics import classification_report
print(classification_report(y_true=y_test,y_pred=y_pred))
precision recall f1-score support
No 1.00 1.00 1.00 215
Si 1.00 1.00 1.00 115
accuracy 1.00 330
macro avg 1.00 1.00 1.00 330
weighted avg 1.00 1.00 1.00 330
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