mpa.EvaluateModel

Overview

EvaluateModel can be used to predict the activity of arbitrary sequences.

Usage

>>> import mpathic as mpa
>>> model = mpa.io.load_model("./mpathic/data/sortseq/full-0/crp_model.txt")
>>> dataset = mpa.io.load_dataset("./mpathic/data/sortseq/full-0/data.txt")
>>> mpa.EvaluateModel(dataset_df = dataset, model_df = model)

Example Input and Output

Example Input Table:

pos      val_A      val_C      val_G      val_T
3  -0.070101  -0.056502   0.184170  -0.057568
4  -0.045146  -0.042017   0.172377  -0.085214
5  -0.035447   0.006974   0.059453  -0.030979
6  -0.037837  -0.000299   0.079747  -0.041611
7  -0.110627  -0.054740   0.066257   0.099110
...

Example Output Table:

output:

 0        0.348108
 1       -0.248134
 2        0.009507
 3        0.238852
 4       -0.112121
 5       -0.048588
...

Class Details

class evaluate_model.EvaluateModel(**kwargs)
Parameters:
dataset_df: (pandas dataframe)

Input dataset data frame

model_df: (pandas dataframe)

Model dataframe

left: (int)

Seq position at which to align the left-side of the model.

Defaults to position determined by model dataframe.

right: (int)

Seq position at which to align the right-side of the model.

Defaults to position determined by model dataframe.