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Tests : Stock > Prediction Techniques + Doc (#1060)

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* Updating tests : conftest + pytest.ini

* Updating tests : stocks/prediction_techniques

* Updating tests : doc
This commit is contained in:
Chavithra
2021-12-16 15:11:55 +01:00
committed by GitHub
parent 73624dcc18
commit 0c205cbeae
10 changed files with 593 additions and 1 deletions
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2
pytest.ini vendored
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@@ -1,2 +1,4 @@
[pytest]
addopts = -p no:warnings
markers =
prediction: mark tests requiring prediction dependencies, like tensorflow

2
tests/README.md vendored
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@@ -96,7 +96,7 @@ def test_another_function(mocker):
```python
import pytest
pytest.skip(reaason="Some optional comment.", allow_module_level=True)
pytest.skip(msg="Some optional comment.", allow_module_level=True)
def test_some_function(mocker):
pass

9
tests/conftest.py
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@@ -7,6 +7,7 @@ from typing import Any, Dict, List, Optional
# IMPORTATION THIRDPARTY
import pandas as pd
import pytest
from _pytest.config.argparsing import Parser
# from pytest_recording._vcr import merge_kwargs
from _pytest.capture import MultiCapture, SysCapture
@@ -258,6 +259,14 @@ def build_path_by_extension(
return path
def pytest_addoption(parser: Parser):
parser.addoption(
"--prediction",
action="store_true",
help="To run tests with the marker : @pytest.mark.prediction",
)
def pytest_configure(config: Config) -> None:
config.addinivalue_line("markers", "record_stdout: Mark the test as text record.")

0
tests/gamestonk_terminal/stocks/prediction_techniques/__init__.py Normal file
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12
tests/gamestonk_terminal/stocks/prediction_techniques/conftest.py Normal file
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@@ -0,0 +1,12 @@
# IMPORTATION STANDARD
# IMPORTATION THIRDPARTY
import pytest
from _pytest.nodes import Node
# IMPORTATION INTERNAL
def pytest_runtest_setup(item: Node):
if not item.config.getoption("--prediction"):
pytest.skip(msg="Runs only with option : --prediction")

473
tests/gamestonk_terminal/stocks/prediction_techniques/test_pred_controller.py Normal file
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@@ -0,0 +1,473 @@
# IMPORTATION STANDARD
import os
from datetime import datetime
# IMPORTATION THIRDPARTY
import pandas as pd
import pytest
# IMPORTATION INTERNAL
pred_controller = pytest.importorskip(
modname="gamestonk_terminal.stocks.prediction_techniques.pred_controller",
reason="Requires prediction dependencies, like tensorflow",
)
# pylint: disable=E1101
# pylint: disable=W0603
# pytest.skip(allow_module_level=True)
DF_STOCK = pd.DataFrame.from_dict(
data={
pd.Timestamp("2020-11-30 00:00:00"): {
"Open": 75.69999694824219,
"High": 76.08999633789062,
"Low": 75.41999816894531,
"Close": 75.75,
"Adj Close": 71.90919494628906,
"Volume": 5539100,
"date_id": 1,
"OC_High": 75.75,
"OC_Low": 75.69999694824219,
},
pd.Timestamp("2020-12-01 00:00:00"): {
"Open": 76.0199966430664,
"High": 77.12999725341797,
"Low": 75.69000244140625,
"Close": 77.02999877929688,
"Adj Close": 73.1242904663086,
"Volume": 6791700,
"date_id": 2,
"OC_High": 77.02999877929688,
"OC_Low": 76.0199966430664,
},
},
orient="index",
)
EMPTY_DF = pd.DataFrame()
PRED_CONTROLLER = pred_controller.PredictionTechniquesController(
ticker="MOCK_TICKER",
start=datetime.strptime("2020-12-15", "%Y-%m-%d"),
interval="1440min",
stock=DF_STOCK,
)
@pytest.mark.vcr(record_mode="none")
@pytest.mark.record_stdout
def test_menu_quick_exit(mocker):
mocker.patch("builtins.input", return_value="quit")
mocker.patch(
"gamestonk_terminal.stocks.prediction_techniques.pred_controller.session"
)
mocker.patch(
"gamestonk_terminal.stocks.prediction_techniques.pred_controller.session.prompt",
return_value="quit",
)
result_menu = pred_controller.menu(
ticker="TSLA",
start=datetime.strptime("2020-12-01", "%Y-%m-%d"),
interval="1440min",
stock=DF_STOCK,
)
assert result_menu
@pytest.mark.vcr(record_mode="none")
@pytest.mark.record_stdout
def test_menu_system_exit(mocker):
class SystemExitSideEffect:
def __init__(self):
self.first_call = True
def __call__(self, *args, **kwargs):
if self.first_call:
self.first_call = False
raise SystemExit()
return True
mock_switch = mocker.Mock(side_effect=SystemExitSideEffect())
mocker.patch("builtins.input", return_value="quit")
mocker.patch(
"gamestonk_terminal.stocks.prediction_techniques.pred_controller.session"
)
mocker.patch(
"gamestonk_terminal.stocks.prediction_techniques.pred_controller.session.prompt",
return_value="quit",
)
mocker.patch(
"gamestonk_terminal.stocks.prediction_techniques.pred_controller.PredictionTechniquesController.switch",
new=mock_switch,
)
pred_controller.menu(
ticker="TSLA",
start=datetime.strptime("2020-12-01", "%Y-%m-%d"),
interval="1440min",
stock=DF_STOCK,
)
@pytest.mark.vcr(record_mode="none")
@pytest.mark.record_stdout
def test_print_help():
controller = pred_controller.PredictionTechniquesController(
ticker="TSLA",
start=datetime.strptime("2020-12-01", "%Y-%m-%d"),
interval="1440min",
stock=DF_STOCK,
)
controller.print_help()
@pytest.mark.vcr(record_mode="none")
def test_switch_empty():
controller = pred_controller.PredictionTechniquesController(
ticker="TSLA",
start=datetime.strptime("2020-12-01", "%Y-%m-%d"),
interval="1440min",
stock=DF_STOCK,
)
result = controller.switch(an_input="")
assert result is None
@pytest.mark.vcr(record_mode="none")
@pytest.mark.record_stdout
def test_switch_help():
controller = pred_controller.PredictionTechniquesController(
ticker="TSLA",
start=datetime.strptime("2020-12-01", "%Y-%m-%d"),
interval="1440min",
stock=DF_STOCK,
)
result = controller.switch(an_input="?")
assert result is None
@pytest.mark.vcr(record_mode="none")
def test_switch_cls(mocker):
mocker.patch("os.system")
controller = pred_controller.PredictionTechniquesController(
ticker="TSLA",
start=datetime.strptime("2020-12-01", "%Y-%m-%d"),
interval="1440min",
stock=DF_STOCK,
)
result = controller.switch(an_input="cls")
assert result is None
os.system.assert_called_once_with("cls||clear")
@pytest.mark.vcr(record_mode="none")
def test_call_q():
controller = pred_controller.PredictionTechniquesController(
ticker="TSLA",
start=datetime.strptime("2020-12-01", "%Y-%m-%d"),
interval="1440min",
stock=DF_STOCK,
)
other_args = list()
result = controller.call_q(other_args)
assert result is False
@pytest.mark.vcr(record_mode="none")
def test_call_quit():
controller = pred_controller.PredictionTechniquesController(
ticker="TSLA",
start=datetime.strptime("2020-12-01", "%Y-%m-%d"),
interval="1440min",
stock=DF_STOCK,
)
other_args = list()
result = controller.call_quit(other_args)
assert result is True
@pytest.mark.vcr(record_mode="none")
@pytest.mark.parametrize(
"tested_func, mocked_func, other_args, called_with",
[
(
"call_ets",
"ets_view.display_exponential_smoothing",
[
"--day=1",
"--trend=A",
"--seasonal=M",
"--period=2",
"--end=2020-12-15",
"--export=png",
],
dict(
ticker="MOCK_TICKER",
values=PRED_CONTROLLER.stock["AdjClose"],
n_predict=1,
trend="A",
seasonal="M",
seasonal_periods=2,
s_end_date=datetime.strptime("2020-12-15", "%Y-%m-%d"),
export="png",
),
),
(
"call_knn",
"knn_view.display_k_nearest_neighbors",
[
"--input=1",
"--days=2",
"--jumps=3",
"--neighbors=4",
"--end=2020-12-01",
"--test_size=0.1",
"--no_shuffle",
],
dict(
ticker="MOCK_TICKER",
data=PRED_CONTROLLER.stock["AdjClose"],
n_neighbors=4,
n_input_days=1,
n_predict_days=2,
test_size=0.1,
end_date=datetime.strptime("2020-12-01", "%Y-%m-%d"),
no_shuffle=False,
),
),
(
"call_regression",
"regression_view.display_regression",
[
"4",
"--input=1",
"--days=2",
"--jumps=3",
"--end=2020-12-15",
"--export=png",
],
dict(
dataset="MOCK_TICKER",
values=PRED_CONTROLLER.stock["AdjClose"],
poly_order=4,
n_input=1,
n_predict=2,
n_jumps=3,
s_end_date=datetime.strptime("2020-12-15", "%Y-%m-%d"),
export="png",
),
),
(
"call_arima",
"arima_view.display_arima",
[
"--days=1",
"--ic=bic",
"--seasonal",
"--order=p,q,d",
"--results",
"--end=2020-12-15",
"--export=png",
],
dict(
dataset="MOCK_TICKER",
values=PRED_CONTROLLER.stock["AdjClose"],
arima_order="p,q,d",
n_predict=1,
seasonal=True,
ic="bic",
results=True,
s_end_date=datetime.strptime("2020-12-15", "%Y-%m-%d"),
export="png",
),
),
(
"call_mlp",
"neural_networks_view.display_mlp",
[
"--days=1",
"--input=2",
"--epochs=3",
"--end=2020-12-15",
"--batch_size=4",
"--loops=5",
# "--valid", "0.1",
"--lr=0.01",
"--no_shuffle",
],
dict(
dataset="MOCK_TICKER",
data=PRED_CONTROLLER.stock["AdjClose"],
n_input_days=2,
n_predict_days=1,
learning_rate=0.01,
epochs=3,
batch_size=4,
test_size=0.1,
n_loops=5,
no_shuffle=False,
),
),
(
"call_rnn",
"neural_networks_view.display_rnn",
[
"--days=1",
"--input=2",
"--epochs=3",
"--end=2020-12-15",
"--batch_size=4",
"--loops=5",
# "--valid", "0.1",
"--lr=0.01",
"--no_shuffle",
],
dict(
dataset="MOCK_TICKER",
data=PRED_CONTROLLER.stock["AdjClose"],
n_input_days=2,
n_predict_days=1,
learning_rate=0.01,
epochs=3,
batch_size=4,
test_size=0.1,
n_loops=5,
no_shuffle=False,
),
),
(
"call_lstm",
"neural_networks_view.display_lstm",
[
"--days=1",
"--input=2",
"--epochs=3",
"--end=2020-12-15",
"--batch_size=4",
"--loops=5",
# "--valid", "0.1",
"--lr=0.01",
"--no_shuffle",
],
dict(
dataset="MOCK_TICKER",
data=PRED_CONTROLLER.stock["AdjClose"],
n_input_days=2,
n_predict_days=1,
learning_rate=0.01,
epochs=3,
batch_size=4,
test_size=0.1,
n_loops=5,
no_shuffle=False,
),
),
(
"call_conv1d",
"neural_networks_view.display_conv1d",
[
"--days=1",
"--input=2",
"--epochs=3",
"--end=2020-12-15",
"--batch_size=4",
"--loops=5",
# "--valid", "0.1",
"--lr=0.01",
"--no_shuffle",
],
dict(
dataset="MOCK_TICKER",
data=PRED_CONTROLLER.stock["AdjClose"],
n_input_days=2,
n_predict_days=1,
learning_rate=0.01,
epochs=3,
batch_size=4,
test_size=0.1,
n_loops=5,
no_shuffle=False,
),
),
(
"call_mc",
"mc_view.display_mc_forecast",
[
"--days=1",
# "--num=2",
"--dist=normal",
"--export=png",
],
dict(
data=PRED_CONTROLLER.stock["AdjClose"],
n_future=1,
n_sims=100,
use_log=False,
export="png",
),
),
],
)
def test_call_func(tested_func, mocked_func, other_args, called_with, mocker):
mock = mocker.Mock()
mocker.patch(
"gamestonk_terminal.stocks.prediction_techniques.pred_controller."
+ mocked_func,
new=mock,
)
getattr(PRED_CONTROLLER, tested_func)(other_args=other_args)
if isinstance(called_with, dict):
mock.assert_called_once_with(**called_with)
elif isinstance(called_with, list):
mock.assert_called_once_with(*called_with)
else:
mock.assert_called_once()
@pytest.mark.vcr(record_mode="none")
@pytest.mark.parametrize(
"func",
[
"call_load",
"call_ets",
"call_knn",
"call_regression",
"call_arima",
"call_mlp",
"call_rnn",
"call_lstm",
"call_conv1d",
"call_mc",
],
)
def test_call_func_no_parser(func, mocker):
mock = mocker.Mock(return_value=None)
mocker.patch.object(
target=pred_controller,
attribute="parse_known_args_and_warn",
new=mock,
)
mocker.patch.object(
target=pred_controller.pred_helper,
attribute="parse_known_args_and_warn",
new=mock,
)
controller = pred_controller.PredictionTechniquesController(
ticker="MOCK_TICKER",
start=datetime.strptime("2020-12-01", "%Y-%m-%d"),
interval="1440min",
stock=DF_STOCK.copy(),
)
func_result = getattr(controller, func)(other_args=["PM"])
assert func_result is None
getattr(pred_controller, "parse_known_args_and_warn").assert_called_once()

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tests/gamestonk_terminal/stocks/prediction_techniques/txt/test_pred_controller/test_menu_quick_exit.txt vendored Normal file
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@@ -0,0 +1,23 @@
Prediction Techniques:
cls clear screen
?/help show this menu again
q quit this menu, and shows back to main menu
quit quit to abandon program
load load new ticker
pick pick new target variable
Daily Stock: TSLA (from 2020-12-01)
Target Column: AdjClose
Models:
ets exponential smoothing (e.g. Holt-Winters)
knn k-Nearest Neighbors
regression polynomial regression
arima autoregressive integrated moving average
mlp MultiLayer Perceptron
rnn Recurrent Neural Network
lstm Long-Short Term Memory
conv1d 1D Convolutional Neural Network
mc Monte-Carlo simulations

27
tests/gamestonk_terminal/stocks/prediction_techniques/txt/test_pred_controller/test_menu_system_exit.txt vendored Normal file
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@@ -0,0 +1,27 @@
Prediction Techniques:
cls clear screen
?/help show this menu again
q quit this menu, and shows back to main menu
quit quit to abandon program
load load new ticker
pick pick new target variable
Daily Stock: TSLA (from 2020-12-01)
Target Column: AdjClose
Models:
ets exponential smoothing (e.g. Holt-Winters)
knn k-Nearest Neighbors
regression polynomial regression
arima autoregressive integrated moving average
mlp MultiLayer Perceptron
rnn Recurrent Neural Network
lstm Long-Short Term Memory
conv1d 1D Convolutional Neural Network
mc Monte-Carlo simulations
The command selected doesn't exist
Did you mean 'quit'?

23
tests/gamestonk_terminal/stocks/prediction_techniques/txt/test_pred_controller/test_print_help.txt vendored Normal file
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@@ -0,0 +1,23 @@
Prediction Techniques:
cls clear screen
?/help show this menu again
q quit this menu, and shows back to main menu
quit quit to abandon program
load load new ticker
pick pick new target variable
Daily Stock: TSLA (from 2020-12-01)
Target Column: AdjClose
Models:
ets exponential smoothing (e.g. Holt-Winters)
knn k-Nearest Neighbors
regression polynomial regression
arima autoregressive integrated moving average
mlp MultiLayer Perceptron
rnn Recurrent Neural Network
lstm Long-Short Term Memory
conv1d 1D Convolutional Neural Network
mc Monte-Carlo simulations

23
tests/gamestonk_terminal/stocks/prediction_techniques/txt/test_pred_controller/test_switch_help.txt vendored Normal file
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@@ -0,0 +1,23 @@
Prediction Techniques:
cls clear screen
?/help show this menu again
q quit this menu, and shows back to main menu
quit quit to abandon program
load load new ticker
pick pick new target variable
Daily Stock: TSLA (from 2020-12-01)
Target Column: AdjClose
Models:
ets exponential smoothing (e.g. Holt-Winters)
knn k-Nearest Neighbors
regression polynomial regression
arima autoregressive integrated moving average
mlp MultiLayer Perceptron
rnn Recurrent Neural Network
lstm Long-Short Term Memory
conv1d 1D Convolutional Neural Network
mc Monte-Carlo simulations