Skip to content

Latest commit

 

History

History
109 lines (71 loc) · 3.63 KB

LSTMpredict.rst

File metadata and controls

109 lines (71 loc) · 3.63 KB
Raw

Prediction using LSTM

This module contains the code necessary to make a recommendation based on a LSTM.

Functions

get_latest_file_with_path(path, *paths):

Method to get the full path name for the latest file for the input parameter in paths. This method uses the os.path.getctime function to get the most recently created file that matches the filename pattern in the provided path.

Parameters:

path : string
Root pathname for the files.
*paths : string list
These are the var args field, the optional set of strings to denote the full path to the file names.

Returns:

latest_file : string
Full path name for the latest file provided in the paths parameter.

Class

LSTMpredict

Functions

predict(self, device, net, words, vocab_to_int, int_to_vocab, names_set):

Returns the list of top 5 predictions for the provided list of words using the model stored in net. The device is initialized to CPU for the purpose of the predictions.

Parameters:

device : torch.device
Device type to signify 'cpu' or 'gpu'.
net : torch.module
Instance of LSTM created using RNN Module.
words : list
List of strings used for predicting the next string in the list of words.
vocab_to_int : dict
Mapping of strings to int used to embed the input strings.
int_to_vocab : dict
Mapping of int to string used in the process of running the model predictions.
names_set : dict
Mapping of fieldType(example proxyObsType, interpretation/variable) and list of all the possible values the field can take.

Returns:

list
Top 5 recommendations for the next string in the sequence of words.

predictForSentence(self, sentence, isInferred = False):

This method is used from the Flask Server Code API. This method handles the initialization of the lstm model for the two different prediction chains we are using in our system.

archiveType -> proxyObservationType -> units archiveType -> proxyObservationType -> interpretation/variable -> interpretation/variableDetial -> inferredVariable -> inferredVarUnits

Depending on the length of the input sentence and the variableType, it chooses the output that will be returned to the server.

If the variableType == measured then we will be considering the complete chain for prediction example: If sentence length = 1, it contains the archiveType and output = prediction for proxyObservationType example: If sentence length = 2, it contains the archiveType and proxyObservationType and output = units and interpretation/variable so on..

If the variableType == inferred then we will be considering the top attributes in the chain from proxyObservationType to interpretation/variableDetail to predict the top 5 inferredVariable as the output .

Parameters:

sentence : string
Input sentence to predict the next field.
isInferred : boolean, optional
True if variableType == 'inferred'. The default is False.

Returns:

dict
Contains the result list of predictions as the value. Depending on the length of the input sentence and the variableType, the dict can contain one item corresponding to key '0' or two items corresponding to the two keys '0' and '1'.

Usage

LSTMpredict.py module is used for accuracy calculation in the /accuracy_calc/lstm directory. For more information check out the documentation for Metrics Calculation.

.. toctree::
    :maxdepth: 1

    /../accuracy_calculation/fang_metrics.rst