def __init__(self,
feature_exprs,
model,
encoder=None,
window_sizes={
'5s': 50,
'50s': 500
},
raw_features={
'bid': online_features.best_bid,
'offer': online_features.best_offer
},
min_frames_before_prediction=2):
self.raw_features = raw_features
self.window_sizes = window_sizes
self.agg = mk_agg(raw_features, window_sizes)
self.feature_exprs = feature_exprs
# a list of expr_lang functions which map a symbol environment to the
# evaluated feature expression
self.compiled_features = [
expr_lang.compile_expr(expr) for expr in feature_exprs
]
# the set of symbols that need to be present in an environment so that
# all the feature expressions can be evaluated
self.feature_symbols = expr_lang.symbol_set(feature_exprs)
self.encoder = encoder
self.model = model
self.min_frames_before_prediction = min_frames_before_prediction
self.longest_window = np.max(window_sizes.values())
def __init__(self,
feature_exprs,
model,
encoder=None,
window_sizes={'5s': 50, '50s': 500},
raw_features={'bid': online_features.best_bid, 'offer': online_features.best_offer},
min_frames_before_prediction=2):
self.raw_features = raw_features
self.window_sizes = window_sizes
self.agg = mk_agg(raw_features, window_sizes)
self.feature_exprs = feature_exprs
# a list of expr_lang functions which map a symbol environment to the
# evaluated feature expression
self.compiled_features = [expr_lang.compile_expr(expr) for expr in feature_exprs]
# the set of symbols that need to be present in an environment so that
# all the feature expressions can be evaluated
self.feature_symbols = expr_lang.symbol_set(feature_exprs)
self.encoder = encoder
self.model = model
self.min_frames_before_prediction = min_frames_before_prediction
self.longest_window = np.max(window_sizes.values())
def test_symbol_set():
s = '(log2 x) + (log2 y) + 0.5 * 3 - x/y/z'
symbols = expr_lang.symbol_set(s)
expected = set(['x', 'y', 'x/y/z'])
print "Expected:", expected, "Result:", symbols
assert symbols == expected
