def main():
df = stock_data()
df = ta.utils.dropna(df)
df = format_timeseries_dataframe(df, "Timestamp")
df = format_look_ahead(df, "Close", size=-4)
df.dropna()
df['log_returns'] = 0
df['log_returns'] = np.where(df["Close_future"] > df["Close"], 1, 1)
df['log_returns'] = np.where(df["Close_future"] < df["Close"], -1,
df['log_returns'])
df = fibonacci(df)
df = fibonacci_rsi(df)
# df = super_hyper_mega_average_true_range(df)
df = df.drop(columns=[
'Open', 'High', 'Low', 'Volume_Currency', 'Weighted_Price',
'Volume_BTC', 'Close', 'above_below_close', 'Close_future'
])
df = df.rename(columns={"log_returns": "y"})
model = (preprocessing.MinMaxScaler()
| linear_model.PAClassifier(C=0.01, mode=1))
report = metrics.ClassificationReport()
roll_dataframe_stats(df, model=model, metric=report)
else:
inst = obj()
yield inst
@pytest.mark.parametrize('estimator, check', [
pytest.param(
copy.deepcopy(estimator), check, id=f'{estimator}:{check.__name__}')
for estimator in list(get_all_estimators()) + [
feature_extraction.TFIDF(),
linear_model.LogisticRegression(),
preprocessing.StandardScaler() | linear_model.LinearRegression(),
preprocessing.StandardScaler() | linear_model.PAClassifier(),
preprocessing.StandardScaler()
| multiclass.OneVsRestClassifier(linear_model.LogisticRegression()),
preprocessing.StandardScaler()
| multiclass.OneVsRestClassifier(linear_model.PAClassifier()),
naive_bayes.GaussianNB(),
preprocessing.StandardScaler(),
cluster.KMeans(n_clusters=5, seed=42),
preprocessing.MinMaxScaler(),
preprocessing.MinMaxScaler() + preprocessing.StandardScaler(),
preprocessing.PolynomialExtender(),
feature_selection.VarianceThreshold(),
feature_selection.SelectKBest(similarity=stats.PearsonCorrelation())
] for check in utils.estimator_checks.yield_checks(estimator)
])
def test_check_estimator(estimator, check):
check(estimator)
})]
}, 2 + 3 * 4),
(preprocessing.StandardScaler() | linear_model.LinearRegression(), {
'LinearRegression': {
'optimizer': [(optim.SGD, {
'lr': [1, 2]
}),
(optim.Adam, {
'beta_1': [.1, .01, .001],
'lr': [.1, .01, .001, .0001]
})]
}
}, 2 + 3 * 4),
(compose.Pipeline(('Scaler', None), linear_model.LinearRegression()), {
'Scaler': [
preprocessing.MinMaxScaler(),
preprocessing.MaxAbsScaler(),
preprocessing.StandardScaler()
],
'LinearRegression': {
'optimizer': {
'lr': [1e-1, 1e-2, 1e-3]
}
}
}, 3 * 3)
])
def test_expand_param_grid_count(model, param_grid, count):
assert len(utils.expand_param_grid(model, param_grid)) == count
def test_decision_tree_max_depth():
