[FIXED] linear regression loop for residuals scatterplot

Issue

I'm running a linear regression simulation, each model according to a different value of the "label" variable. I can print metrics for each model, but I'm not able to run a different scatterplot por each model. All the graphs are reproduced in a single scatterplot. I would like to run a metric and a different scatterplot for each model

import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from scipy.stats import binom
from scipy.stats import norm
import numpy as np

from scipy.stats import norm
# generate random numbers from N(0,1)
x = norm.rvs(size=10000,loc=0,scale=1)
y = norm.rvs(size=10000,loc=0,scale=1)
z = binom.rvs(n=10,p=0.8,size=10000)
df = pd.DataFrame(data={'v1':x.flatten(),'target':y.flatten(),'label':z.flatten()})

classes=df.label.unique().tolist()
results = []


for name in classes:
    df_subset=df.loc[df['label']==name]
    
    reg = LinearRegression()
    reg.fit(df_subset['v1'].values.reshape(-1, 1), df_subset["target"].values.reshape(-1, 1))
    predictions = reg.predict(df_subset['v1'].values.reshape(-1, 1))
    
    res=np.mean((predictions - df_subset["target"].values.reshape(-1, 1)) ** 2)
    results.append(res)
    
    msg = "Metric model %s: %f " % (name, res)
    print(msg)
    
    df_subset['pred']=predictions
    sns.scatterplot(data=df_subset, x='pred', y="target")

Solution

Just create a new figure before sns plot. plt.figure() <--- after sns plot do plt.show() so that you can show print statement(model metric) before each plot.

import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from scipy.stats import binom
from scipy.stats import norm
import numpy as np
import seaborn as sns

from scipy.stats import norm
# generate random numbers from N(0,1)
x = norm.rvs(size=10000,loc=0,scale=1)
y = norm.rvs(size=10000,loc=0,scale=1)
z = binom.rvs(n=10,p=0.8,size=10000)
df = pd.DataFrame(data={'v1':x.flatten(),'target':y.flatten(),'label':z.flatten()})

classes=df.label.unique().tolist()
results = []


for name in classes:
    df_subset=df.loc[df['label']==name]
    
    reg = LinearRegression()
    reg.fit(df_subset['v1'].values.reshape(-1, 1), df_subset["target"].values.reshape(-1, 1))
    predictions = reg.predict(df_subset['v1'].values.reshape(-1, 1))
    
    res=np.mean((predictions - df_subset["target"].values.reshape(-1, 1)) ** 2)
    results.append(res)
    
    msg = "Metric model %s: %f " % (name, res)
    print(msg)
    plt.figure() #<-----------here
    df_subset['pred']=predictions
    sns.scatterplot(data=df_subset, x='pred', y="target")
    plt.show() #<------------ here

Answered By - Pygirl