timeseries

import pandas as pd

window_length = 5
stride = None
horizon=1
df = pd.read_csv("trainbins.csv")
print(df)
X, y = SlidingWindow(window_length, stride=stride, horizon=horizon)(df)
X = X.astype('float')
splits = get_splits(y, valid_size=.2, stratify=True, random_state=23, shuffle=False)
print(splits)

tfms = [None, [Categorize()]]
dsets = TSDatasets(X, y, tfms=tfms, splits=splits)
dls = TSDataLoaders.from_dsets(dsets.train, dsets.valid, bs=50)
model = TST(dls.vars, dls.c, dls.len, fc_dropout=0.9)
learn = Learner(dls, model, loss_func=CrossEntropyLossFlat(),
                metrics=[accuracy], cbs=ShowGraphCallback2())
learn.fit_one_cycle(250, 1e-4)
learn.export("models/clf.pkl") # make sure you set the path to a folder that already exists

from tsai.inference import load_learner
learn = load_learner("models/clf.pkl")
probas, target, preds = learn.get_X_preds(X[splits[0]], y[splits[0]])

probas, target, preds