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Don't run prior and posterior predictive when calling fit #365

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merged 9 commits into from
Sep 1, 2023
Merged

Don't run prior and posterior predictive when calling fit #365

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38c1e91
passing **kwargs to all sampling methods, making prior and posterior …
michaelraczycki Aug 19, 2023
b1afedb
passing kwargs from fit
michaelraczycki Aug 19, 2023
10064b1
testing sample_model without previous build
michaelraczycki Aug 20, 2023
381daee
marking tests using fit as slow, adding additional tests for new feature
michaelraczycki Aug 20, 2023
ee23a8c
adjusting plotting fit
michaelraczycki Aug 20, 2023
6eaf896
moving prior and posterior kwargs out of sample kwargs
michaelraczycki Aug 22, 2023
700377f
reducing sampling size for tests
michaelraczycki Aug 22, 2023
e24652d
updating test_plotting.py to use new fit() parameters
michaelraczycki Aug 22, 2023
fcd30c6
removing sample_model
michaelraczycki Aug 29, 2023
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2 changes: 1 addition & 1 deletion pymc_marketing/clv/models/basic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -293,7 +293,7 @@ def fit_summary(self, **kwargs):
def output_var(self):
pass

def generate_and_preprocess_model_data(
def _generate_and_preprocess_model_data(
self,
X: Union[pd.DataFrame, pd.Series],
y: Union[pd.Series, np.ndarray[Any, Any]],
Expand Down
28 changes: 15 additions & 13 deletions pymc_marketing/mmm/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,7 +42,7 @@ def __init__(
**kwargs,
) -> None:
self.X: Optional[pd.DataFrame] = None
self.y: Optional[pd.Series] = None
self.y: Optional[Union[pd.Series, np.ndarray]] = None
self.date_column: str = date_column
self.channel_columns: Union[List[str], Tuple[str]] = channel_columns
self.n_channel: int = len(channel_columns)
Expand All @@ -69,8 +69,8 @@ def methods(self) -> List[Any]:
def validation_methods(
self,
) -> Tuple[
List[Callable[["BaseMMM", Union[pd.DataFrame, pd.Series]], None]],
List[Callable[["BaseMMM", Union[pd.DataFrame, pd.Series]], None]],
List[Callable[["BaseMMM", Union[pd.DataFrame, pd.Series, np.ndarray]], None]],
List[Callable[["BaseMMM", Union[pd.DataFrame, pd.Series, np.ndarray]], None]],
]:
"""
A property that provides validation methods for features ("X") and the target variable ("y").
Expand Down Expand Up @@ -98,7 +98,9 @@ def validation_methods(
],
)

def validate(self, target: str, data: Union[pd.DataFrame, pd.Series]) -> None:
def validate(
self, target: str, data: Union[pd.DataFrame, pd.Series, np.ndarray]
) -> None:
"""
Validates the input data based on the specified target type.

Expand All @@ -110,7 +112,7 @@ def validate(self, target: str, data: Union[pd.DataFrame, pd.Series]) -> None:
target : str
The type of target to be validated.
Expected values are "X" for features and "y" for the target variable.
data : Union[pd.DataFrame, pd.Series]
data : Union[pd.DataFrame, pd.Series, np.ndarray]
The input data to be validated.

Raises
Expand All @@ -134,14 +136,14 @@ def preprocessing_methods(
) -> Tuple[
List[
Callable[
["BaseMMM", Union[pd.DataFrame, pd.Series]],
Union[pd.DataFrame, pd.Series],
["BaseMMM", Union[pd.DataFrame, pd.Series, np.ndarray]],
Union[pd.DataFrame, pd.Series, np.ndarray],
]
],
List[
Callable[
["BaseMMM", Union[pd.DataFrame, pd.Series]],
Union[pd.DataFrame, pd.Series],
["BaseMMM", Union[pd.DataFrame, pd.Series, np.ndarray]],
Union[pd.DataFrame, pd.Series, np.ndarray],
]
],
]:
Expand Down Expand Up @@ -171,8 +173,8 @@ def preprocessing_methods(
)

def preprocess(
self, target: str, data: Union[pd.DataFrame, pd.Series]
) -> Union[pd.DataFrame, pd.Series]:
self, target: str, data: Union[pd.DataFrame, pd.Series, np.ndarray]
) -> Union[pd.DataFrame, pd.Series, np.ndarray]:
"""
Preprocess the provided data according to the specified target.

Expand All @@ -184,12 +186,12 @@ def preprocess(
target : str
Indicates whether the data represents features ("X") or the target variable ("y").

data : pd.DataFrame
data : Union[pd.DataFrame, pd.Series, np.ndarray]
The data to be preprocessed.

Returns
-------
Union[pd.DataFrame, pd.Series]
Union[pd.DataFrame, pd.Series, np.ndarray]
The preprocessed data.

Raises
Expand Down
43 changes: 34 additions & 9 deletions pymc_marketing/mmm/delayed_saturated_mmm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -81,10 +81,11 @@ def default_sampler_config(self) -> Dict:

@property
def output_var(self):
"""Defines target variable for the model"""
return "y"

def generate_and_preprocess_model_data( # type: ignore
self, X: Union[pd.DataFrame, pd.Series], y: pd.Series
def _generate_and_preprocess_model_data( # type: ignore
self, X: Union[pd.DataFrame, pd.Series], y: Union[pd.Series, np.ndarray]
) -> None:
"""
Applies preprocessing to the data before fitting the model.
Expand All @@ -93,8 +94,8 @@ def generate_and_preprocess_model_data( # type: ignore

Parameters
----------
X : array, shape (n_obs, n_features)
y : array, shape (n_obs,)
X : Union[pd.DataFrame, pd.Series], shape (n_obs, n_features)
y : Union[pd.Series, np.ndarray], shape (n_obs,)
"""
date_data = X[self.date_column]
channel_data = X[self.channel_columns]
Expand Down Expand Up @@ -126,11 +127,11 @@ def generate_and_preprocess_model_data( # type: ignore
self.validate("X", X_data)
self.validate("y", y)
self.preprocessed_data: Dict[str, Union[pd.DataFrame, pd.Series]] = {
"X": self.preprocess("X", X_data),
"y": self.preprocess("y", y),
"X": self.preprocess("X", X_data), # type: ignore
"y": self.preprocess("y", y), # type: ignore
}
self.X: pd.DataFrame = X_data
self.y: pd.Series = y
self.y: Union[pd.Series, np.ndarray] = y

def _save_input_params(self, idata) -> None:
"""Saves input parameters to the attrs of idata."""
Expand All @@ -144,11 +145,35 @@ def _save_input_params(self, idata) -> None:
def build_model(
self,
X: pd.DataFrame,
y: pd.Series,
y: Union[pd.Series, np.ndarray],
**kwargs,
) -> None:
"""
Builds a probabilistic model using PyMC for marketing mix modeling.

The model incorporates channels, control variables, and Fourier components, applying
adstock and saturation transformations to the channel data. The final model is
constructed with multiple factors contributing to the response variable.

Parameters
----------
X : pd.DataFrame
The input data for the model, which should include columns for channels,
control variables (if applicable), and Fourier components (if applicable).

y : Union[pd.Series, np.ndarray]
The target/response variable for the modeling.

**kwargs : dict
Additional keyword arguments that might be required by underlying methods or utilities.

Attributes Set
---------------
model : pm.Model
The PyMC model object containing all the defined stochastic and deterministic variables.
"""
model_config = self.model_config
self.generate_and_preprocess_model_data(X, y)
self._generate_and_preprocess_model_data(X, y)
with pm.Model(coords=self.model_coords) as self.model:
channel_data_ = pm.MutableData(
name="channel_data",
Expand Down
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