# onehot
## Example Data
``` python
df = pd.DataFrame(
{
"site_seq": [
"AASTYGVAKR",
"GGSTYGVAKR",
"AASTFGVAKR",
"PPSTYGVAKK",
"VVSTYGIARR",
]
}
)
df.shape
```
``` python
df.head()
```
## Encoding
------------------------------------------------------------------------
source
### onehot_encode
``` python
def onehot_encode(
sequences:Sequence, # aligned protein sequence windows
transform_colname:bool=True, # shift feature names around the center residue
n:int=20, # center position used for transformed column labels
)->DataFrame:
```
*One-hot encode aligned protein sequence windows.*
``` python
onehot_encode(df["site_seq"]).head()
```
------------------------------------------------------------------------
source
### onehot_encode_df
``` python
def onehot_encode_df(
df:DataFrame, # dataframe containing aligned sequences
seq_col:str='site_seq', # column containing the sequence strings
kwargs:object
)->DataFrame: # forwarded to onehot_encode
```
*One-hot encode a sequence column from a dataframe.*
``` python
onehot = onehot_encode_df(df, seq_col="site_seq")
onehot.head()
```
## Clustering
------------------------------------------------------------------------
source
### run_kmeans
``` python
def run_kmeans(
onehot:DataFrame, # one-hot encoded sequence matrix
n:int=2, # number of clusters to fit
seed:int=42, # random seed for KMeans
)->object:
```
*Fit KMeans to one-hot encoded features and return the assigned labels.*
``` python
run_kmeans(onehot, n=2)
```
------------------------------------------------------------------------
source
### filter_range_columns
``` python
def filter_range_columns(
df:DataFrame, # one-hot encoded dataframe with position-prefixed column names
low:int=-10, # lower bound for retained positions
high:int=10, # upper bound for retained positions
)->DataFrame:
```
*Filter one-hot columns to a position window around the center residue.*
``` python
onehot_10 = filter_range_columns(onehot, low=-10, high=10)
onehot_10.head()
```
## Elbow Method
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source
### get_clusters_elbow
``` python
def get_clusters_elbow(
encoded_data:DataFrame, # one-hot encoded feature matrix
max_cluster:int=400, # largest cluster count to evaluate
interval:int=50, # step size between tested cluster counts
)->None:
```
*Plot the elbow curve for a range of KMeans cluster counts.*
``` python
get_clusters_elbow(onehot, max_cluster=5, interval=2)
```