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added support for sparse matrices #424

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added support for sparse matrices #424

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130 changes: 67 additions & 63 deletions lazypredict/Supervised.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,9 +40,9 @@
"GaussianProcessClassifier",
"HistGradientBoostingClassifier",
"MLPClassifier",
"LogisticRegressionCV",
"MultiOutputClassifier",
"MultinomialNB",
"LogisticRegressionCV",
"MultiOutputClassifier",
"MultinomialNB",
"OneVsOneClassifier",
"OneVsRestClassifier",
"OutputCodeClassifier",
Expand All @@ -52,20 +52,20 @@

removed_regressors = [
"TheilSenRegressor",
"ARDRegression",
"CCA",
"IsotonicRegression",
"ARDRegression",
"CCA",
"IsotonicRegression",
"StackingRegressor",
"MultiOutputRegressor",
"MultiTaskElasticNet",
"MultiTaskElasticNetCV",
"MultiTaskLasso",
"MultiTaskLassoCV",
"PLSCanonical",
"PLSRegression",
"RadiusNeighborsRegressor",
"RegressorChain",
"VotingRegressor",
"MultiOutputRegressor",
"MultiTaskElasticNet",
"MultiTaskElasticNetCV",
"MultiTaskLasso",
"MultiTaskLassoCV",
"PLSCanonical",
"PLSRegression",
"RadiusNeighborsRegressor",
"RegressorChain",
"VotingRegressor",
]

CLASSIFIERS = [
Expand Down Expand Up @@ -219,7 +219,7 @@ def __init__(
self.random_state = random_state
self.classifiers = classifiers

def fit(self, X_train, X_test, y_train, y_test):
def fit(self, X_train, X_test, y_train, y_test, should_preprocess: bool = True):
"""Fit Classification algorithms to X_train and y_train, predict and score on X_test, y_test.
Parameters
----------
Expand All @@ -235,6 +235,9 @@ def fit(self, X_train, X_test, y_train, y_test):
y_test : array-like,
Testing vectors, where rows is the number of samples
and columns is the number of features.
should_preprocess : bool,
Indicates if preprocessing columns is needed.
Turn this off if your matrix is sparse.
Returns
-------
scores : Pandas DataFrame
Expand All @@ -257,20 +260,22 @@ def fit(self, X_train, X_test, y_train, y_test):
X_train = pd.DataFrame(X_train)
X_test = pd.DataFrame(X_test)

numeric_features = X_train.select_dtypes(include=[np.number]).columns
categorical_features = X_train.select_dtypes(include=["object"]).columns
preprocessor = None
if should_preprocess:
numeric_features = X_train.select_dtypes(include=[np.number]).columns
categorical_features = X_train.select_dtypes(include=["object"]).columns

categorical_low, categorical_high = get_card_split(
X_train, categorical_features
)
categorical_low, categorical_high = get_card_split(
X_train, categorical_features
)

preprocessor = ColumnTransformer(
transformers=[
("numeric", numeric_transformer, numeric_features),
("categorical_low", categorical_transformer_low, categorical_low),
("categorical_high", categorical_transformer_high, categorical_high),
]
)
preprocessor = ColumnTransformer(
transformers=[
("numeric", numeric_transformer, numeric_features),
("categorical_low", categorical_transformer_low, categorical_low),
("categorical_high", categorical_transformer_high, categorical_high),
]
)

if self.classifiers == "all":
self.classifiers = CLASSIFIERS
Expand All @@ -288,17 +293,14 @@ def fit(self, X_train, X_test, y_train, y_test):
for name, model in tqdm(self.classifiers):
start = time.time()
try:
steps = []
if should_preprocess:
steps.append(("preprocessor", preprocessor))
if "random_state" in model().get_params().keys():
pipe = Pipeline(
steps=[
("preprocessor", preprocessor),
("classifier", model(random_state=self.random_state)),
]
)
steps.append(("classifier", model(random_state=self.random_state)))
else:
pipe = Pipeline(
steps=[("preprocessor", preprocessor), ("classifier", model())]
)
steps.append(("classifier", model()))
pipe = Pipeline(steps=steps)

pipe.fit(X_train, y_train)
self.models[name] = pipe
Expand Down Expand Up @@ -349,6 +351,7 @@ def fit(self, X_train, X_test, y_train, y_test):
if self.predictions:
predictions[name] = y_pred
except Exception as exception:
print(f"{name} got error: {exception}")
if self.ignore_warnings is False:
print(name + " model failed to execute")
print(exception)
Expand Down Expand Up @@ -404,7 +407,7 @@ def provide_models(self, X_train, X_test, y_train, y_test):
Returns
-------
models: dict-object,
Returns a dictionary with each model pipeline as value
Returns a dictionary with each model pipeline as value
with key as name of models.
"""
if len(self.models.keys()) == 0:
Expand Down Expand Up @@ -519,7 +522,7 @@ def __init__(
self.random_state = random_state
self.regressors = regressors

def fit(self, X_train, X_test, y_train, y_test):
def fit(self, X_train, X_test, y_train, y_test, should_preprocess: bool = True):
"""Fit Regression algorithms to X_train and y_train, predict and score on X_test, y_test.
Parameters
----------
Expand All @@ -535,6 +538,9 @@ def fit(self, X_train, X_test, y_train, y_test):
y_test : array-like,
Testing vectors, where rows is the number of samples
and columns is the number of features.
should_preprocess : bool,
If preprocessing of columns should be done.
Turn this off if your matrix is sparse.
Returns
-------
scores : Pandas DataFrame
Expand All @@ -557,20 +563,22 @@ def fit(self, X_train, X_test, y_train, y_test):
X_train = pd.DataFrame(X_train)
X_test = pd.DataFrame(X_test)

numeric_features = X_train.select_dtypes(include=[np.number]).columns
categorical_features = X_train.select_dtypes(include=["object"]).columns
preprocessor = None
if should_preprocess:
numeric_features = X_train.select_dtypes(include=[np.number]).columns
categorical_features = X_train.select_dtypes(include=["object"]).columns

categorical_low, categorical_high = get_card_split(
X_train, categorical_features
)
categorical_low, categorical_high = get_card_split(
X_train, categorical_features
)

preprocessor = ColumnTransformer(
transformers=[
("numeric", numeric_transformer, numeric_features),
("categorical_low", categorical_transformer_low, categorical_low),
("categorical_high", categorical_transformer_high, categorical_high),
]
)
preprocessor = ColumnTransformer(
transformers=[
("numeric", numeric_transformer, numeric_features),
("categorical_low", categorical_transformer_low, categorical_low),
("categorical_high", categorical_transformer_high, categorical_high),
]
)

if self.regressors == "all":
self.regressors = REGRESSORS
Expand All @@ -588,18 +596,14 @@ def fit(self, X_train, X_test, y_train, y_test):
for name, model in tqdm(self.regressors):
start = time.time()
try:
steps = []
if should_preprocess:
steps.append(("preprocessor", preprocessor))
if "random_state" in model().get_params().keys():
pipe = Pipeline(
steps=[
("preprocessor", preprocessor),
("regressor", model(random_state=self.random_state)),
]
)
steps.append(("regressor", model(random_state=self.random_state)))
else:
pipe = Pipeline(
steps=[("preprocessor", preprocessor), ("regressor", model())]
)

steps.append(("regressor", model()))
pipe = Pipeline(steps=steps)
pipe.fit(X_train, y_train)
self.models[name] = pipe
y_pred = pipe.predict(X_test)
Expand Down Expand Up @@ -681,7 +685,7 @@ def provide_models(self, X_train, X_test, y_train, y_test):
Returns
-------
models: dict-object,
Returns a dictionary with each model pipeline as value
Returns a dictionary with each model pipeline as value
with key as name of models.
"""
if len(self.models.keys()) == 0:
Expand Down