def test_keras(c, s, a, b):
# Mirror the mnist dataset
X, y = make_classification(n_classes=10, n_features=784, n_informative=100)
X = X.astype("float32")
assert y.dtype == np.dtype("int64")
model = KerasClassifier(build_fn=_keras_build_fn, lr=0.01, verbose=False)
params = {"lr": loguniform(1e-3, 1e-1)}
search = IncrementalSearchCV(model,
params,
max_iter=3,
n_initial_parameters=5,
decay_rate=None)
yield search.fit(X, y)
# search.fit(X, y)
assert search.best_score_ >= 0
# Make sure the model trains, and scores aren't constant
scores = {
ident: [h["score"] for h in hist]
for ident, hist in search.model_history_.items()
}
assert all(len(hist) == 3 for hist in scores.values())
nuniq_scores = [pd.Series(v).nunique() for v in scores.values()]
assert max(nuniq_scores) > 1
def _test_verbosity(c, s, a, b):
X, y = make_classification(n_samples=10, n_features=4, chunks=10)
model = ConstantFunction()
params = {"value": scipy.stats.uniform(0, 1)}
search = IncrementalSearchCV(model, params, max_iter=max_iter, verbose=verbose)
yield search.fit(X, y)
return search
def test_pytorch(c, s, a, b):
n_features = 10
defaults = {
"callbacks": False,
"warm_start": False,
"train_split": None,
"max_epochs": 1,
}
model = NeuralNetRegressor(
module=ShallowNet,
module__n_features=n_features,
criterion=nn.MSELoss,
optimizer=optim.SGD,
optimizer__lr=0.1,
batch_size=64,
**defaults,
)
model2 = clone(model)
assert model.callbacks is False
assert model.warm_start is False
assert model.train_split is None
assert model.max_epochs == 1
params = {"optimizer__lr": loguniform(1e-3, 1e0)}
X, y = make_regression(n_samples=100, n_features=n_features)
X = X.astype("float32")
y = y.astype("float32").reshape(-1, 1)
search = IncrementalSearchCV(model2, params, max_iter=5, decay_rate=None)
yield search.fit(X, y)
assert search.best_score_ >= 0
def test_min_max_iter(c, s, a, b):
X, y = make_classification(n_samples=100, n_features=5, chunks=(10, 5))
est = SGDClassifier()
params = {"alpha": np.logspace(-3, 0)}
search = IncrementalSearchCV(est, params, max_iter=0)
with pytest.raises(ValueError, match="max_iter < 1 is not supported"):
yield search.fit(X, y, classes=[0, 1])
def test_numpy_array(c, s, a, b):
X, y = make_classification(n_samples=100, n_features=5, chunks=(10, 5))
X, y = yield c.compute([X, y])
model = SGDClassifier(tol=1e-3, penalty="elasticnet")
params = {"alpha": np.logspace(-2, 10, 10), "l1_ratio": np.linspace(0.01, 1, 20)}
search = IncrementalSearchCV(model, params, n_initial_parameters=10)
yield search.fit(X, y, classes=[0, 1])
def test_small(c, s, a, b):
X, y = make_classification(n_samples=100, n_features=5, chunks=(10, 5))
model = SGDClassifier(tol=1e-3, penalty="elasticnet")
params = {"alpha": [0.1, 0.5, 0.75, 1.0]}
search = IncrementalSearchCV(model, params, n_initial_parameters="grid")
yield search.fit(X, y, classes=[0, 1])
(X_,) = yield c.compute([X])
search.predict(X_)
def test_search_max_iter(c, s, a, b):
X, y = make_classification(n_samples=100, n_features=5, chunks=(10, 5))
model = SGDClassifier(tol=1e-3, penalty="elasticnet")
params = {"alpha": np.logspace(-2, 10, 10), "l1_ratio": np.linspace(0.01, 1, 20)}
search = IncrementalSearchCV(model, params, n_initial_parameters=10, max_iter=1)
yield search.fit(X, y, classes=[0, 1])
for d in search.history_:
assert d["partial_fit_calls"] <= 1
def test_warns_scores_per_fit(c, s, a, b):
X, y = make_classification(n_samples=100, n_features=5, chunks=10)
params = {"value": np.random.RandomState(42).rand(1000)}
model = ConstantFunction()
search = IncrementalSearchCV(model, params, scores_per_fit=2)
with pytest.warns(UserWarning, match="deprecated since Dask-ML v1.4.0"):
yield search.fit(X, y)
def test_transform(c, s, a, b):
X, y = make_classification(n_samples=100, n_features=5, chunks=(10, 5))
model = MiniBatchKMeans(random_state=0)
params = {"n_clusters": [3, 4, 5], "n_init": [1, 2]}
search = IncrementalSearchCV(model, params, n_initial_parameters="grid")
yield search.fit(X, y)
X_, = yield c.compute([X])
result = search.transform(X_)
assert result.shape == (100, search.best_estimator_.n_clusters)
async def test_smaller(c, s, a, b):
# infinite loop
X, y = make_classification(n_samples=100, n_features=5, chunks=(10, 5))
model = SGDClassifier(tol=1e-3, penalty="elasticnet")
params = {"alpha": [0.1, 0.5]}
search = IncrementalSearchCV(model, params, n_initial_parameters="grid")
await search.fit(X, y, classes=[0, 1])
X_ = await c.compute(X)
search.predict(X_)
def test_gridsearch_func(c, s, a, b):
X, y = make_classification(n_samples=100, n_features=5, chunks=(10, 5))
model = SGDClassifier(tol=1e-3)
params = {"alpha": np.logspace(-2, 10, 3), "l1_ratio": np.linspace(0.01, 1, 2)}
search = IncrementalSearchCV(model, params, n_initial_parameters="grid")
yield search.fit(X, y, classes=[0, 1])
assert {frozenset(d["params"].items()) for d in search.history_} == {
frozenset(d.items()) for d in ParameterGrid(params)
}
def test_numpy_array(c, s, a, b):
X, y = make_classification(n_samples=100, n_features=5, chunks=(10, 5))
X, y = yield c.compute([X, y])
model = SGDClassifier(tol=1e-3, penalty="elasticnet")
params = {
"alpha": np.logspace(-5, -3, 10),
"l1_ratio": np.linspace(0, 1, 20),
}
search = IncrementalSearchCV(model, params, n_initial_parameters=10, max_iter=10)
yield search.fit(X, y, classes=[0, 1])
# smoke test to ensure search completed successfully
assert search.best_score_ > 0
async def test_search_plateau_tol(c, s, a, b):
model = LinearFunction(slope=1)
params = {"foo": np.linspace(0, 1)}
# every 3 calls, score will increase by 3. tol=1: model did improved enough
search = IncrementalSearchCV(model, params, patience=3, tol=1, max_iter=10)
X, y = make_classification(n_samples=100, n_features=5, chunks=(10, 5))
await search.fit(X, y)
assert set(search.cv_results_["partial_fit_calls"]) == {10}
# Every 3 calls, score increases by 3. tol=4: model didn't improve enough
search = IncrementalSearchCV(model, params, patience=3, tol=4, max_iter=10)
X, y = make_classification(n_samples=100, n_features=5, chunks=(10, 5))
await search.fit(X, y)
assert set(search.cv_results_["partial_fit_calls"]) == {3}
async def test_warns_decay_rate(c, s, a, b):
X, y = make_classification(n_samples=100, n_features=5, chunks=10)
params = {"value": np.random.RandomState(42).rand(1000)}
model = ConstantFunction()
kwargs = dict(max_iter=5, n_initial_parameters=5)
search = IncrementalSearchCV(model, params, **kwargs)
match = r"deprecated since Dask-ML v1.4.0."
with pytest.warns(FutureWarning, match=match):
await search.fit(X, y)
# Make sure the printed warning message works
search = IncrementalSearchCV(model, params, decay_rate=None, **kwargs)
await search.fit(X, y)
def test_warns_decay_rate_wanted(c, s, a, b):
X, y = make_classification(n_samples=100, n_features=5, chunks=10)
params = {"value": np.random.RandomState(42).rand(1000)}
model = ConstantFunction()
search = IncrementalSearchCV(
model, params, max_iter=5, n_initial_parameters=5, decay_rate=1
)
match = "decay_rate is deprecated .* Use InverseDecaySearchCV"
with pytest.warns(FutureWarning, match=match):
yield search.fit(X, y)
# Make sure old behavior is retained w/o warning
search = InverseDecaySearchCV(model, params, decay_rate=1)
yield search.fit(X, y)
def test_search_patience_infeasible_tol(c, s, a, b):
X, y = make_classification(n_samples=100, n_features=5, chunks=(10, 5))
rng = check_random_state(42)
params = {"value": rng.rand(1000)}
model = ConstantFunction()
max_iter = 10
score_increase = -10
search = IncrementalSearchCV(
model, params, max_iter=max_iter, patience=3, tol=score_increase,
)
yield search.fit(X, y, classes=[0, 1])
hist = pd.DataFrame(search.history_)
assert hist.partial_fit_calls.max() == max_iter
def test_search_plateau_patience(c, s, a, b):
X, y = make_classification(n_samples=100, n_features=5, chunks=(10, 5))
class ConstantClassifier(SGDClassifier):
def __init__(self, value=0):
self.value = value
super(ConstantClassifier, self).__init__(tol=1e-3)
def score(self, *args, **kwargs):
return self.value
params = {"value": np.random.rand(10)}
model = ConstantClassifier()
search = IncrementalSearchCV(model,
params,
n_initial_parameters=10,
patience=5,
tol=0,
max_iter=10)
yield search.fit(X, y, classes=[0, 1])
assert search.history_
for h in search.history_:
assert h["partial_fit_calls"] <= 5
assert isinstance(search.best_estimator_, SGDClassifier)
assert search.best_score_ == params["value"].max(
) == search.best_estimator_.value
assert "visualize" not in search.__dict__
assert search.best_score_ > 0
X_test, y_test = yield c.compute([X, y])
search.predict(X_test)
search.score(X_test, y_test)
async def test_search_invalid_patience(c, s, a, b):
X, y = make_classification(n_samples=100, n_features=5, chunks=10)
params = {"value": np.random.RandomState(42).rand(1000)}
model = ConstantFunction()
search = IncrementalSearchCV(model, params, patience=1, max_iter=10)
with pytest.raises(ValueError, match="patience >= 2"):
await search.fit(X, y, classes=[0, 1])
search = IncrementalSearchCV(model, params, patience=2.0, max_iter=10)
with pytest.raises(ValueError, match="patience must be an integer"):
await search.fit(X, y, classes=[0, 1])
# Make sure this passes
search = IncrementalSearchCV(model, params, patience=False, max_iter=10)
await search.fit(X, y, classes=[0, 1])
assert search.history_
async def test_verbosity_types(c, s, a, b):
X, y = make_classification(n_samples=10, n_features=4, chunks=10)
model = ConstantFunction()
params = {"value": scipy.stats.uniform(0, 1)}
for verbose in [-1.0, 1.2]:
search = IncrementalSearchCV(model,
params,
verbose=verbose,
max_iter=3)
with pytest.raises(ValueError, match="0 <= verbose <= 1"):
await search.fit(X, y)
for verbose in [0.0, 0, 1, 1.0, True, False]:
search = IncrementalSearchCV(model,
params,
verbose=verbose,
max_iter=3)
await search.fit(X, y)
async def test_search_basic_patience(c, s, a, b):
X, y = make_classification(n_samples=100, n_features=5, chunks=(10, 5))
rng = check_random_state(42)
params = {"slope": 2 + rng.rand(1000)}
model = LinearFunction()
# Test the case where tol to small (all models finish)
max_iter = 15
patience = 5
increase_after_patience = patience
search = IncrementalSearchCV(
model,
params,
max_iter=max_iter,
tol=increase_after_patience,
patience=patience,
fits_per_score=3,
)
await search.fit(X, y, classes=[0, 1])
hist = pd.DataFrame(search.history_)
# +1 (and +2 below) because scores_per_fit isn't exact
assert hist.partial_fit_calls.max() == max_iter + 1
# Test the case where tol to large (no models finish)
patience = 5
increase_after_patience = patience
params = {"slope": 0 + 0.9 * rng.rand(1000)}
search = IncrementalSearchCV(
model,
params,
max_iter=max_iter,
tol=increase_after_patience,
patience=patience,
fits_per_score=3,
)
await search.fit(X, y, classes=[0, 1])
hist = pd.DataFrame(search.history_)
assert hist.partial_fit_calls.max() == patience + 2
async def test_model_future(c, s, a, b):
X, y = make_classification(n_samples=100, n_features=5, chunks=10)
params = {"value": np.random.RandomState(42).rand(1000)}
model = ConstantFunction()
model_future = await c.scatter(model)
search = IncrementalSearchCV(model_future, params, max_iter=10)
await search.fit(X, y, classes=[0, 1])
assert search.history_
assert search.best_score_ > 0
def test_same_models_with_random_state(c, s, a, b):
X, y = make_classification(n_samples=100,
n_features=2,
chunks=(10, 5),
random_state=0)
model = Incremental(
SGDClassifier(tol=-np.inf,
penalty="elasticnet",
random_state=42,
eta0=0.1))
params = {
"loss":
["hinge", "log", "modified_huber", "squared_hinge", "perceptron"],
"average": [True, False],
"learning_rate": ["constant", "invscaling", "optimal"],
"eta0": np.logspace(-2, 0, num=1000),
}
params = {"estimator__" + k: v for k, v in params.items()}
search1 = IncrementalSearchCV(clone(model),
params,
n_initial_parameters=10,
random_state=0)
search2 = IncrementalSearchCV(clone(model),
params,
n_initial_parameters=10,
random_state=0)
yield search1.fit(X, y, classes=[0, 1])
yield search2.fit(X, y, classes=[0, 1])
assert search1.best_score_ == search2.best_score_
assert search1.best_params_ == search2.best_params_
assert np.allclose(search1.best_estimator_.coef_,
search2.best_estimator_.coef_)
def test_high_performing_models_are_retained_with_patience(c, s, a, b):
"""
This tests covers a case when high performing models plateau before the
search is finished.
This covers the use case when one poor-performing model takes a long time
to converge, but all other high-performing models have finished (and
plateaued).
Details
-------
This test defines
* low performing models that continue to improve
* high performing models that are constant
It uses a small tolerance to stop the constant (and high-performing) models.
This test is only concerned with making sure the high-performing model is
retained after it has reached a plateau. It is not concerned with making
sure models are killed off at correct times.
"""
X, y = make_classification(n_samples=100, n_features=5, chunks=(10, 5))
params = {"final_score": [1, 2, 3, 4, 5]}
search = IncrementalSearchCV(
_MaybeLinearFunction(),
params,
patience=2,
tol=1e-3, # only stop the constant functions
decay_rate=0,
n_initial_parameters="grid",
max_iter=20,
)
search._adapt = _remove_worst_performing_model
yield search.fit(X, y)
assert search.best_params_ == {"final_score": 5}
def test_same_random_state_same_params(c, s, a, b):
# This makes sure parameters are sampled correctly when random state is
# specified.
# This test makes sure random state is *correctly* passed to successive
# halvings from Hyperband
seed = 0
values = scipy.stats.uniform(0, 1)
h = HyperbandSearchCV(ConstantFunction(), {"value": values},
random_state=seed,
max_iter=9)
# Make a class for passive random sampling
passive = IncrementalSearchCV(
ConstantFunction(),
{"value": values},
random_state=seed,
max_iter=2,
n_initial_parameters=h.metadata["n_models"],
)
X, y = make_classification(n_samples=10, n_features=4, chunks=10)
yield h.fit(X, y)
yield passive.fit(X, y)
# Check to make sure the Hyperbands found the same params
v_h = h.cv_results_["param_value"]
# Check to make sure the random passive search had *some* of the same params
v_passive = passive.cv_results_["param_value"]
# Sanity checks to make sure all unique floats
assert len(set(v_passive)) == len(v_passive)
assert len(set(v_h)) == len(v_h)
# Getting the `value`s that are the same for both searches
same = set(v_passive).intersection(set(v_h))
passive_models = h.metadata["brackets"][0]["n_models"]
assert len(same) == passive_models
def test_same_params_with_random_state(c, s, a, b):
X, y = make_classification(n_samples=100, n_features=5, chunks=(10, 5))
model = SGDClassifier(tol=1e-3, penalty="elasticnet")
params = {"alpha": scipy.stats.uniform(1e-4, 1)}
search1 = IncrementalSearchCV(
model, params, n_initial_parameters=10, random_state=0
)
yield search1.fit(X, y, classes=[0, 1])
params1 = search1.cv_results_["param_alpha"]
search2 = IncrementalSearchCV(
model, params, n_initial_parameters=10, random_state=0
)
yield search2.fit(X, y, classes=[0, 1])
params2 = search2.cv_results_["param_alpha"]
assert np.allclose(params1, params2)
def test_search_plateau_tol(c, s, a, b):
class LinearFunction(BaseEstimator):
def __init__(self, intercept=0, slope=1, foo=0):
self._num_calls = 0
self.intercept = intercept
self.slope = slope
super(LinearFunction, self).__init__()
def fit(self, *args):
return self
def partial_fit(self, *args, **kwargs):
self._num_calls += 1
return self
def score(self, *args, **kwargs):
return self.intercept + self.slope * self._num_calls
model = LinearFunction(slope=1)
params = {"foo": np.linspace(0, 1)}
# every 3 calls, score will increase by 3. tol=1: model did improved enough
search = IncrementalSearchCV(model,
params,
patience=3,
tol=1,
max_iter=10,
decay_rate=0)
X, y = make_classification(n_samples=100, n_features=5, chunks=(10, 5))
yield search.fit(X, y)
assert set(search.cv_results_["partial_fit_calls"]) == {10}
# Every 3 calls, score increases by 3. tol=4: model didn't improve enough
search = IncrementalSearchCV(model,
params,
patience=3,
tol=4,
decay_rate=0,
max_iter=10)
X, y = make_classification(n_samples=100, n_features=5, chunks=(10, 5))
yield search.fit(X, y)
assert set(search.cv_results_["partial_fit_calls"]) == {3}
def test_history(c, s, a, b):
X, y = make_classification(n_samples=10, n_features=4, chunks=10)
model = ConstantFunction()
params = {"value": scipy.stats.uniform(0, 1)}
alg = IncrementalSearchCV(model, params, max_iter=9, random_state=42)
yield alg.fit(X, y)
gt_zero = lambda x: x >= 0
gt_one = lambda x: x >= 1
key_types_and_checks = [
("mean_partial_fit_time", float, gt_zero),
("mean_score_time", float, gt_zero),
("std_partial_fit_time", float, gt_zero),
("std_score_time", float, gt_zero),
("test_score", float, gt_zero),
("rank_test_score", int, gt_one),
("model_id", int, None),
("partial_fit_calls", int, gt_zero),
("params", dict, lambda d: set(d.keys()) == {"value"}),
("param_value", float, gt_zero),
]
assert set(alg.cv_results_) == {v[0] for v in key_types_and_checks}
for column, dtype, condition in key_types_and_checks:
if dtype:
assert alg.cv_results_[column].dtype == dtype
if condition:
assert all(condition(x) for x in alg.cv_results_[column])
alg.best_estimator_.fit(X, y)
alg.best_estimator_.score(X, y)
alg.score(X, y)
# Test types/format of all parameters we set after fitting
assert isinstance(alg.best_index_, int)
assert isinstance(alg.best_estimator_, ConstantFunction)
assert isinstance(alg.best_score_, float)
assert isinstance(alg.best_params_, dict)
assert isinstance(alg.history_, list)
assert all(isinstance(h, dict) for h in alg.history_)
assert isinstance(alg.model_history_, dict)
assert all(vi in alg.history_ for v in alg.model_history_.values()
for vi in v)
assert all(isinstance(v, np.ndarray) for v in alg.cv_results_.values())
assert isinstance(alg.multimetric_, bool)
keys = {
"score",
"score_time",
"partial_fit_calls",
"partial_fit_time",
"model_id",
"elapsed_wall_time",
"params",
}
assert all(set(h.keys()) == keys for h in alg.history_)
times = [v["elapsed_wall_time"] for v in alg.history_]
assert (np.diff(times) >= 0).all()
# Test to make sure history_ ordered with wall time
assert (np.diff([v["elapsed_wall_time"] for v in alg.history_]) >= 0).all()
for model_hist in alg.model_history_.values():
calls = [h["partial_fit_calls"] for h in model_hist]
assert (np.diff(calls) >= 1).all() or len(calls) == 1
def test_high_performing_models_are_retained_with_patience(c, s, a, b):
"""
This tests covers a case when high performing models plateau before the
search is finished.
This covers the use case when one poor-performing model takes a long time
to converge, but all other high-performing models have finished (and
plateaued).
Details
-------
This test defines
* low performing models that continue to improve
* high performing models that are constant
It uses a small tolerance to stop the constant (and high-performing) models.
This test is only concerned with making sure the high-performing model is
retained after it has reached a plateau. It is not concerned with making
sure models are killed off at correct times.
"""
class MaybeLinearFunction(BaseEstimator):
def __init__(self, final_score=1):
self.final_score = final_score
self._calls = 0
def fit(self, X, y):
return self
def partial_fit(self, X, y):
self._calls += 1
def score(self, X, y):
if self.final_score <= 3:
return self.final_score * (1 - 1 / (self._calls + 2))
return self.final_score
X, y = make_classification(n_samples=100, n_features=5, chunks=(10, 5))
params = {"final_score": [1, 2, 3, 4, 5]}
search = IncrementalSearchCV(
MaybeLinearFunction(),
params,
patience=2,
tol=1e-3, # only stop the constant functions
decay_rate=0,
n_initial_parameters="grid",
max_iter=20,
)
def remove_worst_performing_model(info):
calls = {v[-1]["partial_fit_calls"] for v in info.values()}
ests = {v[-1]["params"]["final_score"] for v in info.values()}
if max(calls) == 1:
assert all(x in ests for x in [1, 2, 3, 4, 5])
elif max(calls) == 2:
assert all(x in ests for x in [2, 3, 4, 5])
assert all(x not in ests for x in [1])
elif max(calls) == 3:
assert all(x in ests for x in [3, 4, 5])
assert all(x not in ests for x in [1, 2])
elif max(calls) == 4:
assert all(x in ests for x in [4, 5])
assert all(x not in ests for x in [1, 2, 3])
elif max(calls) == 5:
assert all(x in ests for x in [5])
assert all(x not in ests for x in [1, 2, 3, 4])
return {k: 0 for k in info.keys()}
recent_scores = {
k: v[-1]["score"]
for k, v in info.items()
if v[-1]["partial_fit_calls"] == max(calls)
}
return {
k: 1
for k, v in recent_scores.items()
if v > min(recent_scores.values())
}
search._adapt = remove_worst_performing_model
yield search.fit(X, y)
assert search.best_params_ == {"final_score": 5}
def _test_search_basic(decay_rate, c, s, a, b):
X, y = make_classification(n_samples=1000, n_features=5, chunks=(100, 5))
model = SGDClassifier(tol=1e-3, loss="log", penalty="elasticnet")
params = {
"alpha": np.logspace(-2, 2, 100),
"l1_ratio": np.linspace(0.01, 1, 200)
}
search = IncrementalSearchCV(model,
params,
n_initial_parameters=20,
max_iter=10,
decay_rate=decay_rate)
yield search.fit(X, y, classes=[0, 1])
assert search.history_
for d in search.history_:
assert d["partial_fit_calls"] <= search.max_iter + 1
assert isinstance(search.best_estimator_, SGDClassifier)
assert search.best_score_ > 0
assert "visualize" not in search.__dict__
assert search.best_params_
assert search.cv_results_ and isinstance(search.cv_results_, dict)
assert {
"mean_partial_fit_time",
"mean_score_time",
"std_partial_fit_time",
"std_score_time",
"test_score",
"rank_test_score",
"model_id",
"params",
"partial_fit_calls",
"param_alpha",
"param_l1_ratio",
}.issubset(set(search.cv_results_.keys()))
assert len(search.cv_results_["param_alpha"]) == 20
assert all(isinstance(v, np.ndarray) for v in search.cv_results_.values())
if decay_rate == 0:
assert (search.cv_results_["test_score"][search.best_index_] >=
search.cv_results_["test_score"]).all()
assert search.cv_results_["rank_test_score"][search.best_index_] == 1
else:
assert all(search.cv_results_["test_score"] >= 0)
assert all(search.cv_results_["rank_test_score"] >= 1)
assert all(search.cv_results_["partial_fit_calls"] >= 1)
assert len(np.unique(search.cv_results_["model_id"])) == len(
search.cv_results_["model_id"])
assert sorted(search.model_history_.keys()) == list(range(20))
assert set(search.model_history_[0][0].keys()) == {
"model_id",
"params",
"partial_fit_calls",
"partial_fit_time",
"score",
"score_time",
"elapsed_wall_time",
}
X_, = yield c.compute([X])
# Dask Objects are lazy
proba = search.predict_proba(X)
log_proba = search.predict_log_proba(X)
assert proba.shape == (1000, 2)
assert log_proba.shape == (1000, 2)
assert isinstance(proba, da.Array)
assert isinstance(log_proba, da.Array)
proba_ = search.predict_proba(X_)
log_proba_ = search.predict_log_proba(X_)
da.utils.assert_eq(proba, proba_)
da.utils.assert_eq(log_proba, log_proba_)
decision = search.decision_function(X_)
assert decision.shape == (1000, )
def run():
client = Client()
from dask_ml.datasets import make_classification
df = dd.read_csv("isHealth.csv",
assume_missing=True,
sample=640000000,
blocksize="10MB")
df = df.fillna(0).fillna(0)
for column in df.columns:
if '.' in column:
df = df.drop(column, axis=1)
# for column in droppedColumns:
# df = df.drop(column, axis=1)
y = df['acquired']
X = df.drop('acquired', axis=1)
from dask_ml.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.1)
# X_train,X_train2,y_train,y_train2 = train_test_split(X_train,y_train)
x_test_tickers = X_test['ticker'].values.compute()
x_test_dates = X_test['date'].values.compute()
print(x_test_tickers[0])
np.savetxt("x_test_tickers.csv", [x_test_tickers, x_test_dates],
delimiter=",",
fmt='%s')
np.savetxt("x_test_dates.csv", x_test_dates, delimiter=",", fmt='%s')
print("GOOD")
for column in X_train.columns:
if 'ticker' in column or 'date' in column:
X_train = X_train.drop(column, axis=1)
X_test = X_test.drop(column, axis=1)
X_train = X_train.to_dask_array()
X_test = X_test.values.compute()
y_train = y_train.to_dask_array()
y_test = y_test.values.compute()
np.savetxt("y_test.csv", y_test, delimiter=",")
from dask_ml.wrappers import Incremental
from sklearn.linear_model import SGDClassifier
from sklearn.neural_network import MLPClassifier
from dask_ml.wrappers import ParallelPostFit
est = MLPClassifier(solver='adam', activation='relu', random_state=0)
inc = Incremental(est, scoring='neg_log_loss')
print("WORKING")
for _ in range(10):
inc.partial_fit(X_train, y_train, classes=[0, 1])
print("FITTED")
np.savetxt("predictions.csv", inc.predict_proba(X_test))
print('Score:', inc.score(X_test, y_test))
# model = MLPClassifier(solver='sgd', hidden_layer_sizes=(10,2),random_state=1)
params = {'alpha': np.logspace(-2, 1, num=1000)}
from dask_ml.model_selection import IncrementalSearchCV
search = IncrementalSearchCV(est,
params,
n_initial_parameters=100,
patience=20,
max_iter=100)
search.fit(X_train, y_train, classes=[0, 1])
print(search)
print("SCORE")
print("FITTED")
np.savetxt("predictions.csv", inc.predict_proba(X_test))
print('Score:', inc.score(X_test, y_test))
