dowhy.gcm.util package
Submodules
dowhy.gcm.util.general module
Functions in this module should be considered experimental, meaning there might be breaking API changes in the future.
- dowhy.gcm.util.general.apply_one_hot_encoding(X: ndarray, one_hot_encoder_map: Dict[int, OneHotEncoder]) ndarray[source]
- dowhy.gcm.util.general.fit_one_hot_encoders(X: ndarray) Dict[int, OneHotEncoder][source]
Fits one-hot encoders to each categorical column in X. A categorical input needs to be a string, i.e. a categorical column consists only of strings.
- Parameters
X – Input data matrix.
- Returns
Dictionary that maps a column index to a scikit OneHotEncoder.
- dowhy.gcm.util.general.has_categorical(X: ndarray) bool[source]
Checks if any of the given columns are categorical, i.e. either a string or a boolean. If any of the columns is categorical, this method will return True. Alternatively, consider is_categorical for checking if all columns are categorical.
Note: A np matrix with mixed data types might internally convert numeric columns to strings and vice versa. To ensure that the given given data keeps the original data type, consider converting/initializing it with the dtype ‘object’. For instance: np.array([[1, ‘True’, ‘0’, 0.2], [3, ‘False’, ‘1’, 2.3]], dtype=object)
- Parameters
X – Input array to check if all columns are categorical.
- Returns
True if all columns of the input are categorical, False otherwise.
- dowhy.gcm.util.general.is_categorical(X: ndarray) bool[source]
Checks if all of the given columns are categorical, i.e. either a string or a boolean. Only if all of the columns are categorical, this method will return True. Alternatively, consider has_categorical for checking if any of the columns is categorical.
Note: A np matrix with mixed data types might internally convert numeric columns to strings and vice versa. To ensure that the given given data keeps the original data type, consider converting/initializing it with the dtype ‘object’. For instance: np.array([[1, ‘True’, ‘0’, 0.2], [3, ‘False’, ‘1’, 2.3]], dtype=object)
- Parameters
X – Input array to check if all columns are categorical.
- Returns
True if all columns of the input are categorical, False otherwise.
- dowhy.gcm.util.general.means_difference(randomized_predictions: ndarray, baseline_values: ndarray) ndarray[source]
- dowhy.gcm.util.general.set_random_seed(random_seed: int) None[source]
Sets random seed in numpy and the random module.
- Parameters
random_seed – Random see for the numpy and random module.
- Returns
None
- dowhy.gcm.util.general.shape_into_2d(*args)[source]
If necessary, shapes the numpy inputs into 2D matrices.
- Example:
array([1, 2, 3]) -> array([[1], [2], [3]]) 2 -> array([[2]])
- Parameters
args – The function expects numpy arrays as inputs and returns a reshaped (2D) version of them (if necessary).
- Returns
Reshaped versions of the input numpy arrays. For instance, given 1D inputs X, Y and Z, then shape_into_2d(X, Y, Z) reshapes them into 2D and returns them. If an input is already 2D, it will not be modified and returned as it is.
dowhy.gcm.util.plotting module
- dowhy.gcm.util.plotting.plot(causal_graph: Graph, causal_strengths: Optional[Dict[Tuple[Any, Any], float]] = None, filename: Optional[str] = None, display_plot: bool = True, **kwargs) None[source]
Convenience function to plot causal graphs. This function uses different backends based on what’s available on the system. The best result is achieved when using Graphviz as the backend. This requires both the Python pygraphviz package (
pip install pygraphviz) and the shared system library (e.g.brew install graphvizorapt-get install graphviz). When graphviz is not available, it will fall back to the networkx backend.- Parameters
causal_graph – The graph to be plotted
causal_strengths – An optional dictionary with Edge -> float entries.
filename – An optional filename if the output should be plotted into a file.
display_plot – Optionally specify if the plot should be displayed or not (default to True).
kwargs – Remaining parameters will be passed through to the backend verbatim.
Example usage:
>>> plot(nx.DiGraph([('X', 'Y')])) # plots X -> Y >>> plot(nx.DiGraph([('X', 'Y')]), causal_strengths={('X', 'Y'): 0.43}) # annotates arrow with 0.43