slearn 0.0.3

A package linking symbolic representation with sklearn for time series prediction

slearn

A package linking symbolic representation with sklearn for time series prediction

Install the slearn package simply by

$ pip install slearn

Support Classifiers	Parameter call
Multi-layer Perceptron	'MLPClassifier'
K-Nearest Neighbors	'KNeighborsClassifier'
Gaussian Naive Bayes	'GaussianNB'
Decision Tree	'DecisionTreeClassifier'
Support Vector Classification	'SVC'
Radial-basis Function Kernel	'RBF'
Logistic Regression	'LogisticRegression'
Quadratic Discriminant Analysis	'QuadraticDiscriminantAnalysis'
AdaBoost classifier	'AdaBoostClassifier'
Random Forest	'RandomForestClassifier'
LightGBM	'LGBM'

Symbolic machine learning prediction

Import the package

>>> from slearn import symbolicML

We can predict any symbolic sequence by choosing the classifiers available in scikit-learn.

>>> string = 'aaaabbbccd'
>>> sbml = symbolicML(classifier_name="MLPClassifier", gap=3, random_seed=0, verbose=0)
>>> x, y = sbml._encoding(string)
>>> pred = sbml.forecasting(x, y, step=5, hidden_layer_sizes=(10,10), learning_rate_init=0.1)
>>> print(pred)
['d', 'b', 'a', 'b', 'b'] # the prediction

Also, you can use it by passing into parameters of dictionary form

>>> string = 'aaaabbbccd'
>>> sbml = symbolicML(classifier_name="MLPClassifier", gap=3, random_seed=0, verbose=0)
>>> x, y = sbml._encoding(string)
>>> params = {'hidden_layer_sizes':(10,10), 'activation':'relu', 'learning_rate_init':0.1}
>>> pred = sbml.forecasting(x, y, step=5, **params)
>>> print(pred)
['d', 'b', 'a', 'b', 'b'] # the prediction

The parameters for the chosen classifier follow the same as the scikit-learn library, so just ensure that parameters are existing in the scikit-learn classifiers.

Prediction with symbolic representation

Load libraries.

>>> import pandas as pd
>>> import numpy as np
>>> import seaborn as sns
>>> import matplotlib.pyplot as plt
>>> from slearn import *

>>> time_series = pd.read_csv("Amazon.csv") # load the required dataset, here we use Amazon stock daily close price.
>>> ts = time_series.Close.values

Set the number of symbols you would like to predict.

>>> step = 50

You can select the available classifiers and symbolic representation method (currently we support SAX and ABBA) for prediction. Similarly, the parameters of the chosen classifier follow the same as the scikit-learn library. We usually deploy ABBA symbolic representation, since it achieves better forecasting against SAX.

Use Gaussian Naive Bayes method:

>>> sl = slearn(series=ts, method='ABBA', 
            gap=3, step=step,
            tol=0.01, alpha=0.2, 
            form='numeric', classifier_name="GaussianNB",
            random_seed=1, verbose=1)
>>> sklearn_params = {'var_smoothing':0.001}
>>> abba_nb_pred = sl.predict(**sklearn_params)

Use neural network models method:

>>> sl = slearn(series=ts, method='ABBA',
            gap=3, step=step,
            tol=0.01, alpha=0.2, 
            form='numeric', classifier_name="MLPClassifier",
            random_seed=1, verbose=1)
>>> sklearn_params = {'hidden_layer_sizes':(20,80), 'learning_rate_init':0.1}
>>> abba_nn_pred = sl.predict(**sklearn_params)

We can plot the prediction,

>>> sns.set_theme(style="whitegrid")
>>> plt.figure(figsize=(25, 9))
>>> sns.set(font_scale=2, style="whitegrid")
>>> sns.lineplot(x=np.arange(0, len(ts)), y= ts, color='c', linewidth=6, label='Time series')
>>> sns.lineplot(x=np.arange(len(ts), len(ts)+min_len), y=abba_nb_pred[:min_len], color='tomato', linewidth=6, label='Prediction (ABBA - GaussianNB)')
>>> sns.lineplot(x=np.arange(len(ts), len(ts)+min_len), y=abba_nn_pred[:min_len], color='darkgreen', linewidth=6, label='Prediction (ABBA - MLPClassifier)')
>>> plt.tight_layout()
>>> plt.tick_params(axis='both', labelsize=25)
>>> plt.show()