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[tests] Remove unneeded logs #1207

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Apr 7, 2023
Merged

[tests] Remove unneeded logs #1207

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6300ab7
initial cleanup
karl-richter Mar 8, 2023
80241f2
Merge branch 'main' into cleanup/tests
LeonieFreisinger Apr 7, 2023
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3 changes: 1 addition & 2 deletions tests/pytest.ini
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Original file line number Diff line number Diff line change
@@ -1,9 +1,8 @@
[pytest]
log_cli = true
log_cli_level = DEBUG
log_cli_format = %(asctime)s [%(levelname)s, %(name)s.%(funcName)s]: %(message)s (%(filename)s:%(lineno)s)
log_cli_format = %(asctime)s [%(levelname)s]: %(message)s (%(filename)s:%(lineno)s)
log_cli_date_format = %Y-%m-%d %H:%M:%S
capture = no
filterwarnings =
ignore::DeprecationWarning:tensorboard.*
ignore::pytorch_lightning.utilities.warnings.PossibleUserWarning
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22 changes: 2 additions & 20 deletions tests/test_integration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -96,10 +96,6 @@ def test_df_utils_func():
global_data_params = df_utils.init_data_params(df_global, normalize="soft1")
global_data_params = df_utils.init_data_params(df_global, normalize="standardize")

log.debug(f"Time Threshold: \n {time_threshold}")
log.debug(f"Df_train: \n {df_train}")
log.debug(f"Df_val: \n {df_val}")


def test_trend():
log.info("testing: Trend")
Expand Down Expand Up @@ -134,9 +130,6 @@ def test_custom_changepoints():
dates = df["ds"][range(1, len(df) - 1, int(len(df) / 5.0))]
dates_list = [str(d) for d in dates]
dates_array = pd.to_datetime(dates_list).values
log.debug(f"dates: {dates}")
log.debug(f"dates_list: {dates_list}")
log.debug(f"dates_array: {dates_array.dtype} {dates_array}")
for cp in [dates_list, dates_array]:
m = NeuralProphet(
changepoints=cp,
Expand Down Expand Up @@ -209,6 +202,7 @@ def test_no_trend():
def test_seasons():
log.info("testing: Seasonality: additive")
df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
# Additive
m = NeuralProphet(
yearly_seasonality=8,
weekly_seasonality=4,
Expand All @@ -221,22 +215,14 @@ def test_seasons():
metrics_df = m.fit(df, freq="D")
future = m.make_future_dataframe(df, n_historic_predictions=365, periods=365)
forecast = m.predict(df=future)
log.debug(
"SUM of yearly season params: {}".format(sum(abs(m.model.seasonality.season_params["yearly"].data.numpy())))
)
log.debug(
"SUM of weekly season params: {}".format(sum(abs(m.model.seasonality.season_params["weekly"].data.numpy())))
)
log.debug(f"season params: {m.model.seasonality.season_params.items()}")
if PLOT:
m.plot(forecast)
# m.plot_components(forecast)
m.plot_parameters()
plt.show()
log.info("testing: Seasonality: multiplicative")
df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
# m = NeuralProphet(n_lags=60, n_changepoints=10, n_forecasts=30, verbose=True,
# epochs=EPOCHS, batch_size=BATCH_SIZE, learning_rate=LR,)
# Multiplicative
m = NeuralProphet(
yearly_seasonality=8,
weekly_seasonality=4,
Expand Down Expand Up @@ -276,13 +262,11 @@ def test_custom_seasons():
m.add_seasonality(name="weekend", period=1, fourier_order=3, condition_name="weekend")
m.add_seasonality(name="weekday", period=1, fourier_order=3, condition_name="weekday")

log.debug(f"seasonalities: {m.config_seasonality.periods}")
metrics_df = m.fit(df, freq="D")
future = m.make_future_dataframe(df, n_historic_predictions=365, periods=365)
future = df_utils.add_quarter_condition(future)
future = df_utils.add_weekday_condition(future)
forecast = m.predict(df=future)
log.debug(f"season params: {m.model.seasonality.season_params.items()}")
if PLOT:
m.plot(forecast)
# m.plot_components(forecast)
Expand Down Expand Up @@ -565,8 +549,6 @@ def test_random_seed():
future = m.make_future_dataframe(df, periods=10, n_historic_predictions=10)
forecast = m.predict(future)
checksum3 = sum(forecast["yhat1"].values)
log.debug(f"should be same: {checksum1} and {checksum2}")
log.debug(f"should not be same: {checksum1} and {checksum3}")
assert math.isclose(checksum1, checksum2)
assert not math.isclose(checksum1, checksum3)

Expand Down
39 changes: 3 additions & 36 deletions tests/test_unit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -193,7 +193,6 @@ def test_auto_batch_epoch():
"10000000": (512, 18),
}

observe = {}
for n_data, (batch_size, epochs) in check.items():
n_data = int(n_data)
c = configure.Train(
Expand All @@ -204,14 +203,8 @@ def test_auto_batch_epoch():
optimizer="SGD",
)
c.set_auto_batch_epoch(n_data=n_data)
observe[f"{n_data}"] = (c.batch_size, c.epochs)
log.debug(f"[config] n_data: {n_data}, batch: {c.batch_size}, epoch: {c.epochs}")
log.debug(f"[should] n_data: {n_data}, batch: {batch_size}, epoch: {epochs}")
assert c.batch_size == batch_size
assert c.epochs == epochs
# print("\n")
# print(check)
# print(observe)


def test_split_impute():
Expand Down Expand Up @@ -278,14 +271,6 @@ def check_folds(df, n_lags, n_forecasts, valid_fold_num, valid_fold_pct, fold_ov
for i in range(valid_fold_num)
]
assert all([x == y for (x, y) in zip(train_folds_samples, train_folds_should)])
log.debug(f"total_samples: {total_samples}")
log.debug(f"val_fold_each: {val_fold_each}")
log.debug(f"overlap_each: {overlap_each}")
log.debug(f"val_folds_len: {val_folds_len}")
log.debug(f"val_folds_samples: {val_folds_samples}")
log.debug(f"train_folds_len: {train_folds_len}")
log.debug(f"train_folds_samples: {train_folds_samples}")
log.debug(f"train_folds_should: {train_folds_should}")

len_df = 100
check_folds(
Expand Down Expand Up @@ -346,16 +331,6 @@ def check_folds_dict(
for i in range(valid_fold_num)
]
assert all([x == y for (x, y) in zip(train_folds_samples, train_folds_should)])
log.debug(f"global_model_cv_type: {global_model_cv_type}")
log.debug(f"df_name: {df_name}")
log.debug(f"total_samples: {total_samples}")
log.debug(f"val_fold_each: {val_fold_each}")
log.debug(f"overlap_each: {overlap_each}")
log.debug(f"val_folds_len: {val_folds_len}")
log.debug(f"val_folds_samples: {val_folds_samples}")
log.debug(f"train_folds_len: {train_folds_len}")
log.debug(f"train_folds_samples: {train_folds_samples}")
log.debug(f"train_folds_should: {train_folds_should}")
return folds

# Test cv for dict with time series with similar time range
Expand Down Expand Up @@ -474,7 +449,6 @@ def test_reg_delay():
(1, 8, 0),
]:
weight = c.get_reg_delay_weight(e, i, reg_start_pct=0.5, reg_full_pct=0.8)
log.debug(f"e {e}, i {i}, expected w {w}, got w {weight}")
assert weight == w


Expand Down Expand Up @@ -502,11 +476,7 @@ def test_double_crossvalidation():
assert train_folds_len2[0] == 85
assert val_folds_len1[0] == 10
assert val_folds_len2[0] == 5
log.debug(f"train_folds_len1: {train_folds_len1}")
log.debug(f"val_folds_len1: {val_folds_len1}")
log.debug(f"train_folds_len2: {train_folds_len2}")
log.debug(f"val_folds_len2: {val_folds_len2} ")
log.info(f"Test m.double_crossvalidation_split_df")

m = NeuralProphet(
epochs=EPOCHS,
batch_size=BATCH_SIZE,
Expand All @@ -533,11 +503,8 @@ def test_double_crossvalidation():
assert train_folds_len2[0] == 88
assert val_folds_len1[0] == 12
assert val_folds_len2[0] == 6
log.debug(f"train_folds_len1: {train_folds_len1}")
log.debug(f"val_folds_len1: {val_folds_len1}")
log.debug(f"train_folds_len2: {train_folds_len2}")
log.debug(f"val_folds_len2: {val_folds_len2}")
log.info("Raise not implemented error as double_crossvalidation is not compatible with many time series")

# Raise not implemented error as double_crossvalidation is not compatible with many time series
with pytest.raises(NotImplementedError):
df = pd.DataFrame({"ds": pd.date_range(start="2017-01-01", periods=len_df), "y": np.arange(len_df)})
df1 = df.copy(deep=True)
Expand Down