# heatmap
``` python
df = sns.load_dataset('titanic').dropna(subset=['age', 'fare', 'class', 'sex', 'survived']).reset_index(drop=True)
df2 = df[['age', 'fare', 'sibsp', 'parch']].head(8).copy()
df2.index = [f'row_{i}' for i in range(len(df2))]
print(df.shape)
print(df2.shape)
```
(714, 15)
(8, 4)
``` python
df.head()
```
|
survived |
pclass |
sex |
age |
sibsp |
parch |
fare |
embarked |
class |
who |
adult_male |
deck |
embark_town |
alive |
alone |
| 0 |
0 |
3 |
male |
22.0 |
1 |
0 |
7.2500 |
S |
Third |
man |
True |
NaN |
Southampton |
no |
False |
| 1 |
1 |
1 |
female |
38.0 |
1 |
0 |
71.2833 |
C |
First |
woman |
False |
C |
Cherbourg |
yes |
False |
| 2 |
1 |
3 |
female |
26.0 |
0 |
0 |
7.9250 |
S |
Third |
woman |
False |
NaN |
Southampton |
yes |
True |
| 3 |
1 |
1 |
female |
35.0 |
1 |
0 |
53.1000 |
S |
First |
woman |
False |
C |
Southampton |
yes |
False |
| 4 |
0 |
3 |
male |
35.0 |
0 |
0 |
8.0500 |
S |
Third |
man |
True |
NaN |
Southampton |
no |
True |
## Matrix Helpers
------------------------------------------------------------------------
### get_similarity
``` python
def get_similarity(
df:DataFrame, # numeric feature matrix indexed by sample name
metric:str='euclidean', # pairwise_distances metric name
)->tuple:
```
*Calculate both distance and similarity matrices for a dataframe.*
``` python
get_similarity(df2)[0]
```
|
row_0 |
row_1 |
row_2 |
row_3 |
row_4 |
row_5 |
row_6 |
row_7 |
| row_0 |
0.000000 |
66.001996 |
4.177993 |
47.657345 |
13.062925 |
54.911521 |
24.415786 |
6.714166 |
| row_1 |
66.001996 |
0.000000 |
64.492435 |
18.429118 |
63.312323 |
25.182682 |
61.821302 |
61.188418 |
| row_2 |
4.177993 |
64.492435 |
0.000000 |
46.073643 |
9.000868 |
52.100901 |
27.548548 |
3.910651 |
| row_3 |
47.657345 |
18.429118 |
46.073643 |
0.000000 |
45.061097 |
19.066500 |
46.039121 |
42.780883 |
| row_4 |
13.062925 |
63.312323 |
9.000868 |
45.061097 |
0.000000 |
47.754949 |
35.618122 |
8.803791 |
| row_5 |
54.911521 |
25.182682 |
52.100901 |
19.066500 |
47.754949 |
0.000000 |
60.513388 |
48.906725 |
| row_6 |
24.415786 |
61.821302 |
27.548548 |
46.039121 |
35.618122 |
60.513388 |
0.000000 |
27.089433 |
| row_7 |
6.714166 |
61.188418 |
3.910651 |
42.780883 |
8.803791 |
48.906725 |
27.089433 |
0.000000 |
------------------------------------------------------------------------
### plot_corr
``` python
def plot_corr(
df_corr:DataFrame, # correlation, distance, or similarity matrix
mask_method:str | None='upper', # upper, lower, or None
inverse_color:bool=False, # reverse the colormap when True
figsize:tuple=(15, 10), # figure size in inches
annot:bool=True, # whether to annotate the matrix values
linewidths:float=0.1, # cell border width
kwargs:VAR_KEYWORD
):
```
*Plot a square matrix with an optional triangular mask.*
``` python
plot_corr(df[['age', 'fare', 'sibsp', 'parch']].corr(numeric_only=True))
```
## Classification And Composition
------------------------------------------------------------------------
### plot_confusion_matrix
``` python
def plot_confusion_matrix(
target, # true labels
pred, # predicted labels
class_names:list[str] | None=None, # labels shown on the axes
normalize:bool=False, # normalize rows when True
title:str='Confusion matrix', # plot title
cmap:LinearSegmentedColormap=, # matplotlib colormap
figsize:tuple=(6, 6), # figure size in inches
kwargs:VAR_KEYWORD
):
```
*Plot a confusion matrix from target and prediction arrays.*
``` python
plot_confusion_matrix(df['survived'], df['adult_male'], class_names=['False', 'True'], normalize=True)
```
Normalized confusion matrix
