In [2]:
!pip install funpymodeling
Collecting funpymodeling
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Collecting seaborn<0.11.0,>=0.10.1
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Installing collected packages: seaborn, funpymodeling
Attempting uninstall: seaborn
Found existing installation: seaborn 0.11.2
Uninstalling seaborn-0.11.2:
Successfully uninstalled seaborn-0.11.2
Successfully installed funpymodeling-0.1.7 seaborn-0.10.1
In [3]:
#Importacion de las librerias
import pandas as pd
import seaborn as sns
from pandas_profiling import ProfileReport
from funpymodeling.exploratory import cat_vars, num_vars
import numpy as np
Preparacion de Datos¶
In [4]:
# Carga de datos
data=pd.read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-01-21/spotify_songs.csv")
# Removemos duplicados de canciones:
data=data.drop_duplicates(subset="track_id")
#Nos quedamos unicamente con las variables numericas
x_data=data.drop(cat_vars(data), axis=1)
# Sacamos algunas variables adicionales que no aportan valor
x_data=x_data.drop(['key','speechiness', 'mode', 'tempo', 'duration_ms'], axis=1)
Creación del modelo de PCA¶
In [5]:
#Importamos la libreria para el escalado de los datos
from sklearn.preprocessing import StandardScaler
#Generamos el objeto
scaler = StandardScaler()
#Aplicamos la transformacion
x_scaled = scaler.fit_transform(x_data)
#Importante: Los datos no tienen que tener nulos y deben ser todos numericos
Generamos el modelo y fiteamos:
In [6]:
#Importamos la libreria
from sklearn.decomposition import PCA
#Generamos el objeto
model_pca = PCA()
#Aplicamos pca
x_pca=model_pca.fit_transform(x_scaled)
In [7]:
#Variaanza explicada de las componentes
var_explicada_pca = model_pca.explained_variance_ratio_
var_explicada_pca
Out[7]:
array([0.26718277, 0.17844569, 0.14004096, 0.12199857, 0.10753268,
0.08223481, 0.0748838 , 0.02768073])
Interpretación:
La primer componente aporta el 26 % de la varianza explicada, la segunda el 17% y así sucesivamente
UMAP¶
UMAP es un método de reducción de dimensionalidad no lineal y es muy eficaz para visualizar agrupaciones o grupos de puntos de datos y sus proximidades relativas.
Link de Interes:
In [9]:
!pip3 install umap-learn
Collecting umap-learn
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Collecting pynndescent>=0.5
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Building wheels for collected packages: umap-learn, pynndescent
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Successfully built umap-learn pynndescent
Installing collected packages: pynndescent, umap-learn
Successfully installed pynndescent-0.5.4 umap-learn-0.5.1
In [10]:
import umap #pip3 install umap-learn
In [11]:
#Generamos el objeto para la estandarizacion
x_scaled = StandardScaler()
#Aplicamos la estandarizacion
x_scaled = x_scaled.fit_transform(x_data)
In [12]:
#Obtenemos el objeto umap
model_umap = umap.UMAP()
In [13]:
#Ejecutamos el umap
model_umap_fit_transform = model_umap.fit_transform(x_scaled)
model_umap_fit_transform.shape
/usr/local/lib/python3.7/dist-packages/numba/np/ufunc/parallel.py:363: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled. warnings.warn(problem)
Out[13]:
(28356, 2)
Visualización interactiva con Plotly!
In [14]:
data2 = data.copy() #Hacemos un copy
data2['cancion'] = data2['track_artist'] + ' | ' + data2['track_name'] #Creamos una nueva variable llamada: cancion
data2[['dim1', 'dim2']] = model_umap_fit_transform #Agregamos las dos dimensiones generadas por umap
In [15]:
import plotly.express as px
fig = px.scatter(data2, x="dim1", y="dim2", color="track_popularity", hover_data=['cancion'])
fig.show()
Created in