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Expand data validation to also cover the scikit-learn experiment classes #290

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Feb 23, 2024
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Expand data validation to also cover the scikit-learn experiment classes #290

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34ac423
extract data validation into mixin classes
drbenvincent Feb 5, 2024
cdbbd03
update UML diagrams
drbenvincent Feb 5, 2024
6574481
add data validation checks for scikit-learn based experiments
drbenvincent Feb 23, 2024
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135 changes: 135 additions & 0 deletions causalpy/data_validation.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,135 @@
import warnings # noqa: I001

import pandas as pd
import numpy as np
from causalpy.custom_exceptions import (
BadIndexException, # NOQA
DataException,
FormulaException,
)
from causalpy.utils import _is_variable_dummy_coded


class PrePostFitDataValidator:
"""Mixin class for validating the input data and model formula for PrePostFit"""

def _input_validation(self, data, treatment_time):
"""Validate the input data and model formula for correctness"""
if isinstance(data.index, pd.DatetimeIndex) and not isinstance(
treatment_time, pd.Timestamp
):
raise BadIndexException(
"If data.index is DatetimeIndex, treatment_time must be pd.Timestamp."
)
if not isinstance(data.index, pd.DatetimeIndex) and isinstance(
treatment_time, pd.Timestamp
):
raise BadIndexException(
"If data.index is not DatetimeIndex, treatment_time must be pd.Timestamp." # noqa: E501
)


class DiDDataValidator:
"""Mixin class for validating the input data and model formula for Difference in Differences experiments."""

def _input_validation(self):
"""Validate the input data and model formula for correctness"""
if "post_treatment" not in self.formula:
raise FormulaException(
"A predictor called `post_treatment` should be in the formula"
)

if "post_treatment" not in self.data.columns:
raise DataException(
"Require a boolean column labelling observations which are `treated`"
)

if "unit" not in self.data.columns:
raise DataException(
"Require a `unit` column to label unique units. This is used for plotting purposes" # noqa: E501
)

if _is_variable_dummy_coded(self.data[self.group_variable_name]) is False:
raise DataException(
f"""The grouping variable {self.group_variable_name} should be dummy
coded. Consisting of 0's and 1's only."""
)


class RDDataValidator:
"""Mixin class for validating the input data and model formula for Regression Discontinuity experiments."""

def _input_validation(self):
"""Validate the input data and model formula for correctness"""
if "treated" not in self.formula:
raise FormulaException(
"A predictor called `treated` should be in the formula"
)

if _is_variable_dummy_coded(self.data["treated"]) is False:
raise DataException(
"""The treated variable should be dummy coded. Consisting of 0's and 1's only.""" # noqa: E501
)


class RegressionKinkDataValidator:
"""Mixin class for validating the input data and model formula for Regression Kink experiments."""

def _input_validation(self):
"""Validate the input data and model formula for correctness"""
if "treated" not in self.formula:
raise FormulaException(

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"A predictor called `treated` should be in the formula"
)

if _is_variable_dummy_coded(self.data["treated"]) is False:
raise DataException(

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causalpy/data_validation.py#L86 

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"""The treated variable should be dummy coded. Consisting of 0's and 1's only.""" # noqa: E501
)

if self.bandwidth <= 0:
raise ValueError("The bandwidth must be greater than zero.")

if self.epsilon <= 0:
raise ValueError("Epsilon must be greater than zero.")


class PrePostNEGDDataValidator:
"""Mixin class for validating the input data and model formula for PrePostNEGD experiments."""

def _input_validation(self) -> None:
"""Validate the input data and model formula for correctness"""
if not _is_variable_dummy_coded(self.data[self.group_variable_name]):
raise DataException(
f"""
There must be 2 levels of the grouping variable
{self.group_variable_name}. I.e. the treated and untreated.
"""
)


class IVDataValidator:
"""Mixin class for validating the input data and model formula for IV experiments."""

def _input_validation(self):
"""Validate the input data and model formula for correctness"""
treatment = self.instruments_formula.split("~")[0]
test = treatment.strip() in self.instruments_data.columns
test = test & (treatment.strip() in self.data.columns)
if not test:
raise DataException(
f"""
The treatment variable:
{treatment} must appear in the instrument_data to be used
as an outcome variable and in the data object to be used as a covariate.
"""
)
Z = self.data[treatment.strip()]
check_binary = len(np.unique(Z)) > 2
if check_binary:
warnings.warn(
"""Warning. The treatment variable is not Binary.
This is not necessarily a problem but it violates
the assumption of a simple IV experiment.
The coefficients should be interpreted appropriately."""
)
129 changes: 14 additions & 115 deletions causalpy/pymc_experiments.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,13 +23,16 @@
from patsy import build_design_matrices, dmatrices
from sklearn.linear_model import LinearRegression as sk_lin_reg

from causalpy.custom_exceptions import (
BadIndexException, # NOQA
DataException,
FormulaException,
from causalpy.data_validation import (
PrePostFitDataValidator,
DiDDataValidator,
RDDataValidator,
RegressionKinkDataValidator,
PrePostNEGDDataValidator,
IVDataValidator,
)
from causalpy.plot_utils import plot_xY
from causalpy.utils import _is_variable_dummy_coded, round_num
from causalpy.utils import round_num

LEGEND_FONT_SIZE = 12
az.style.use("arviz-darkgrid")
Expand Down Expand Up @@ -108,7 +111,7 @@ def print_coefficients(self, round_to=None) -> None:
)


class PrePostFit(ExperimentalDesign):
class PrePostFit(ExperimentalDesign, PrePostFitDataValidator):
"""
A class to analyse quasi-experiments where parameter estimation is based on just
the pre-intervention data.
Expand Down Expand Up @@ -160,7 +163,6 @@ def __init__(
) -> None:
super().__init__(model=model, **kwargs)
self._input_validation(data, treatment_time)

self.treatment_time = treatment_time
# set experiment type - usually done in subclasses
self.expt_type = "Pre-Post Fit"
Expand Down Expand Up @@ -214,21 +216,6 @@ def __init__(
# cumulative impact post
self.post_impact_cumulative = self.post_impact.cumsum(dim="obs_ind")

def _input_validation(self, data, treatment_time):
"""Validate the input data and model formula for correctness"""
if isinstance(data.index, pd.DatetimeIndex) and not isinstance(
treatment_time, pd.Timestamp
):
raise BadIndexException(
"If data.index is DatetimeIndex, treatment_time must be pd.Timestamp."
)
if not isinstance(data.index, pd.DatetimeIndex) and isinstance(
treatment_time, pd.Timestamp
):
raise BadIndexException(
"If data.index is not DatetimeIndex, treatment_time must be pd.Timestamp." # noqa: E501
)

def plot(self, counterfactual_label="Counterfactual", round_to=None, **kwargs):
"""
Plot the results
Expand Down Expand Up @@ -438,7 +425,7 @@ def plot(self, plot_predictors=False, **kwargs):
return fig, ax


class DifferenceInDifferences(ExperimentalDesign):
class DifferenceInDifferences(ExperimentalDesign, DiDDataValidator):
"""A class to analyse data from Difference in Difference settings.

.. note::
Expand Down Expand Up @@ -568,29 +555,6 @@ def __init__(
if "post_treatment" in label and self.group_variable_name in label:
self.causal_impact = self.idata.posterior["beta"].isel({"coeffs": i})

def _input_validation(self):
"""Validate the input data and model formula for correctness"""
if "post_treatment" not in self.formula:
raise FormulaException(
"A predictor called `post_treatment` should be in the formula"
)

if "post_treatment" not in self.data.columns:
raise DataException(
"Require a boolean column labelling observations which are `treated`"
)

if "unit" not in self.data.columns:
raise DataException(
"Require a `unit` column to label unique units. This is used for plotting purposes" # noqa: E501
)

if _is_variable_dummy_coded(self.data[self.group_variable_name]) is False:
raise DataException(
f"""The grouping variable {self.group_variable_name} should be dummy
coded. Consisting of 0's and 1's only."""
)

def plot(self, round_to=None):
"""Plot the results.

Expand Down Expand Up @@ -749,7 +713,7 @@ def summary(self, round_to=None) -> None:
self.print_coefficients(round_to)


class RegressionDiscontinuity(ExperimentalDesign):
class RegressionDiscontinuity(ExperimentalDesign, RDDataValidator):
"""
A class to analyse sharp regression discontinuity experiments.

Expand Down Expand Up @@ -876,18 +840,6 @@ def __init__(
- self.pred_discon["posterior_predictive"].sel(obs_ind=0)["mu"]
)

def _input_validation(self):
"""Validate the input data and model formula for correctness"""
if "treated" not in self.formula:
raise FormulaException(
"A predictor called `treated` should be in the formula"
)

if _is_variable_dummy_coded(self.data["treated"]) is False:
raise DataException(
"""The treated variable should be dummy coded. Consisting of 0's and 1's only.""" # noqa: E501
)

def _is_treated(self, x):
"""Returns ``True`` if `x` is greater than or equal to the treatment threshold.

Expand Down Expand Up @@ -970,7 +922,7 @@ def summary(self, round_to: None) -> None:
self.print_coefficients(round_to)


class RegressionKink(ExperimentalDesign):
class RegressionKink(ExperimentalDesign, RegressionKinkDataValidator):
"""
A class to analyse sharp regression kink experiments.

Expand Down Expand Up @@ -1095,24 +1047,6 @@ def _probe_kink_point(self):
mu_kink_right = predicted["posterior_predictive"].sel(obs_ind=2)["mu"]
return mu_kink_left, mu_kink, mu_kink_right

def _input_validation(self):
"""Validate the input data and model formula for correctness"""
if "treated" not in self.formula:
raise FormulaException(
"A predictor called `treated` should be in the formula"
)

if _is_variable_dummy_coded(self.data["treated"]) is False:
raise DataException(
"""The treated variable should be dummy coded. Consisting of 0's and 1's only.""" # noqa: E501
)

if self.bandwidth <= 0:
raise ValueError("The bandwidth must be greater than zero.")

if self.epsilon <= 0:
raise ValueError("Epsilon must be greater than zero.")

def _is_treated(self, x):
"""Returns ``True`` if `x` is greater than or equal to the treatment threshold.""" # noqa: E501
return np.greater_equal(x, self.kink_point)
Expand Down Expand Up @@ -1193,7 +1127,7 @@ def summary(self, round_to=None) -> None:
self.print_coefficients(round_to)


class PrePostNEGD(ExperimentalDesign):
class PrePostNEGD(ExperimentalDesign, PrePostNEGDDataValidator):
"""
A class to analyse data from pretest/posttest designs

Expand Down Expand Up @@ -1302,18 +1236,6 @@ def __init__(
{"coeffs": self._get_treatment_effect_coeff()}
)

# ================================================================

def _input_validation(self) -> None:
"""Validate the input data and model formula for correctness"""
if not _is_variable_dummy_coded(self.data[self.group_variable_name]):
raise DataException(
f"""
There must be 2 levels of the grouping variable
{self.group_variable_name}. I.e. the treated and untreated.
"""
)

def plot(self, round_to=None):
"""Plot the results

Expand Down Expand Up @@ -1408,7 +1330,7 @@ def _get_treatment_effect_coeff(self) -> str:
raise NameError("Unable to find coefficient name for the treatment effect")


class InstrumentalVariable(ExperimentalDesign):
class InstrumentalVariable(ExperimentalDesign, IVDataValidator):
"""
A class to analyse instrumental variable style experiments.

Expand Down Expand Up @@ -1555,26 +1477,3 @@ def get_naive_OLS_fit(self):
beta_params.insert(0, ols_reg.intercept_[0])
self.ols_beta_params = dict(zip(self._x_design_info.column_names, beta_params))
self.ols_reg = ols_reg

def _input_validation(self):
"""Validate the input data and model formula for correctness"""
treatment = self.instruments_formula.split("~")[0]
test = treatment.strip() in self.instruments_data.columns
test = test & (treatment.strip() in self.data.columns)
if not test:
raise DataException(
f"""
The treatment variable:
{treatment} must appear in the instrument_data to be used
as an outcome variable and in the data object to be used as a covariate.
"""
)
Z = self.data[treatment.strip()]
check_binary = len(np.unique(Z)) > 2
if check_binary:
warnings.warn(
"""Warning. The treatment variable is not Binary.
This is not necessarily a problem but it violates
the assumption of a simple IV experiment.
The coefficients should be interpreted appropriately."""
)
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