In [1]:

    

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt


    

In [2]:

    

columns = ["instances", "features", "seconds"]


    

In [3]:

    

lr_times = pd.read_csv("lr_times.txt", header=None, names=columns)
lr_time_grid = lr_times.pivot(index='instances', columns='features', values='seconds')
fig, ax = plt.subplots(figsize=(20,10))  
ax.set_title('Logistic Regression')
sns.heatmap(lr_time_grid, linewidths=.5, cmap="bone_r", annot=True, fmt='.2g', ax=ax) # set vmin=0, vmax=9,


    

    
Out[3]:





<matplotlib.axes._subplots.AxesSubplot at 0x1258f5f28>






    

In [4]:

    

mlp_times = pd.read_csv("mlp_times.txt", header=None, names=columns)
mlp_time_grid = mlp_times.pivot(index='instances', columns='features', values='seconds')
fig, ax = plt.subplots(figsize=(20,10))  
ax.set_title('Multilayer Perceptron')
sns.heatmap(mlp_time_grid, linewidths=.5, cmap="bone_r", annot=True, fmt='.2g', ax=ax) # set vmin=0, vmax=9,


    

    
Out[4]:





<matplotlib.axes._subplots.AxesSubplot at 0x127f40978>






    

In [5]:

    

svc_times = pd.read_csv("svc_times.txt", header=None, names=columns)
svc_time_grid = svc_times.pivot(index='instances', columns='features', values='seconds')
fig, ax = plt.subplots(figsize=(20,10))  
ax.set_title('Support Vector Machine')
sns.heatmap(svc_time_grid, linewidths=.5, cmap="bone_r", annot=True, fmt='.2g', ax=ax) # set vmin=0, vmax=9,


    

    
Out[5]:





<matplotlib.axes._subplots.AxesSubplot at 0x128306d30>






    

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