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Determine presence of intercept only by fit_intercept argument #747

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Jan 15, 2024
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Determine presence of intercept only by fit_intercept argument #747

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daf84b1
always use self.fit_intercept; raise if formula conflicts with it
MatthiasSchmidtblaicherQC Jan 9, 2024
20747d3
wording [skip ci]
MatthiasSchmidtblaicherQC Jan 9, 2024
c6354b5
adjust other tests, cosmetics
MatthiasSchmidtblaicherQC Jan 9, 2024
ab3d86f
don't compare specs with singular matrix to smf
MatthiasSchmidtblaicherQC Jan 9, 2024
80316c1
fix smf test formula
MatthiasSchmidtblaicherQC Jan 9, 2024
0f31e23
fix intercept in context test
MatthiasSchmidtblaicherQC Jan 9, 2024
9f4adf8
remove outdated sentence; clean up
MatthiasSchmidtblaicherQC Jan 9, 2024
b569862
fix
MatthiasSchmidtblaicherQC Jan 9, 2024
d17f805
adjust tutorial
MatthiasSchmidtblaicherQC Jan 10, 2024
2359590
adjust tutorial
MatthiasSchmidtblaicherQC Jan 10, 2024
4db62f4
Merge branch 'glum-v3' into use-fit-intercept
MatthiasSchmidtblaicherQC Jan 11, 2024
1edd591
Merge branch 'glum-v3' into use-fit-intercept
MatthiasSchmidtblaicherQC Jan 15, 2024
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147 changes: 15 additions & 132 deletions docs/tutorials/formula_interface/formula_interface.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@
"Formulas can provide a concise and convenient way to specify many of the usual pre-processing steps, such as converting to categorical types, creating interactions, applying transformations, or even spline interpolation. As an example, consider the following formula:\n",
"\n",
"```\n",
"{ClaimAmountCut / Exposure} ~ C(DrivAge, missing_method='convert') * C(VehPower, missing_method=\"zero\") + bs(BonusMalus, 3) + 1\n",
"{ClaimAmountCut / Exposure} ~ C(DrivAge, missing_method='convert') * C(VehPower, missing_method=\"zero\") + bs(BonusMalus, 3)\n",
"```\n",
"\n",
"Despite its brevity, it describes all of the following:\n",
Expand All @@ -32,7 +32,6 @@
" - If there are missing values in `DrivAge`, they should be treated as a separate category.\n",
" - On the other hand, missing values in `VehPower` should be treated as all-zero indicators.\n",
" - The predictors should also include a third degree B-spline interpolation of `BonusMalus`.\n",
" - The model should include an intercept.\n",
"\n",
"The following chapters demonstrate each of these features in some detail, as well as some additional advantages of using the formula interface."
]
Expand All @@ -59,6 +58,7 @@
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import pytest\n",
"import scipy.optimize as optimize\n",
"import scipy.stats\n",
"from dask_ml.preprocessing import Categorizer\n",
Expand Down Expand Up @@ -1261,144 +1261,27 @@
"source": [
"### Intercept Term\n",
"\n",
"Just like in the case of the non-formula interface, an intercept term is added by default. This can be disabled by either setting the `fit_intercept` parameter to `False`, or adding `+0` or `-1` to the end of the formula. In the case of conflict, a warning is emitted, and the latter takes precedence."
"Just like in the case of the non-formula interface, the presence of an intercept is determined by the `fit_intercept` argument. In case that the formula specifies a different behavior (e.g., adding `+0` or `-1` while `fit_intercept=True`), an error will be raised."
]
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/stanmart/work/glum/src/glum/_glm.py:2354: UserWarning: The formula explicitly sets the intercept to False, overriding fit_intercept=True.\n",
" warnings.warn(\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>intercept</th>\n",
" <th>DrivAge__0</th>\n",
" <th>DrivAge__1</th>\n",
" <th>DrivAge__2</th>\n",
" <th>DrivAge__3</th>\n",
" <th>DrivAge__4</th>\n",
" <th>DrivAge__5</th>\n",
" <th>DrivAge__6</th>\n",
" <th>VehPower__4</th>\n",
" <th>VehPower__5</th>\n",
" <th>...</th>\n",
" <th>DrivAge__4__x__VehPower__8</th>\n",
" <th>DrivAge__5__x__VehPower__8</th>\n",
" <th>DrivAge__6__x__VehPower__8</th>\n",
" <th>DrivAge__0__x__VehPower__9</th>\n",
" <th>DrivAge__1__x__VehPower__9</th>\n",
" <th>DrivAge__2__x__VehPower__9</th>\n",
" <th>DrivAge__3__x__VehPower__9</th>\n",
" <th>DrivAge__4__x__VehPower__9</th>\n",
" <th>DrivAge__5__x__VehPower__9</th>\n",
" <th>DrivAge__6__x__VehPower__9</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>coefficient</th>\n",
" <td>0.0</td>\n",
" <td>1.713298</td>\n",
" <td>0.783505</td>\n",
" <td>0.205914</td>\n",
" <td>0.016085</td>\n",
" <td>0.0</td>\n",
" <td>0.000094</td>\n",
" <td>0.223685</td>\n",
" <td>4.66123</td>\n",
" <td>4.736272</td>\n",
" <td>...</td>\n",
" <td>-0.144927</td>\n",
" <td>0.001657</td>\n",
" <td>0.515373</td>\n",
" <td>0.714834</td>\n",
" <td>-0.325666</td>\n",
" <td>-0.370935</td>\n",
" <td>0.20417</td>\n",
" <td>0.013222</td>\n",
" <td>-0.273913</td>\n",
" <td>0.115693</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1 rows × 56 columns</p>\n",
"</div>"
],
"text/plain": [
" intercept DrivAge__0 DrivAge__1 DrivAge__2 DrivAge__3 \\\n",
"coefficient 0.0 1.713298 0.783505 0.205914 0.016085 \n",
"\n",
" DrivAge__4 DrivAge__5 DrivAge__6 VehPower__4 VehPower__5 \\\n",
"coefficient 0.0 0.000094 0.223685 4.66123 4.736272 \n",
"\n",
" ... DrivAge__4__x__VehPower__8 DrivAge__5__x__VehPower__8 \\\n",
"coefficient ... -0.144927 0.001657 \n",
"\n",
" DrivAge__6__x__VehPower__8 DrivAge__0__x__VehPower__9 \\\n",
"coefficient 0.515373 0.714834 \n",
"\n",
" DrivAge__1__x__VehPower__9 DrivAge__2__x__VehPower__9 \\\n",
"coefficient -0.325666 -0.370935 \n",
"\n",
" DrivAge__3__x__VehPower__9 DrivAge__4__x__VehPower__9 \\\n",
"coefficient 0.20417 0.013222 \n",
"\n",
" DrivAge__5__x__VehPower__9 DrivAge__6__x__VehPower__9 \n",
"coefficient -0.273913 0.115693 \n",
"\n",
"[1 rows x 56 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"formula_noint = \"PurePremium ~ DrivAge * VehPower - 1\"\n",
"\n",
"t_glm6 = GeneralizedLinearRegressor(\n",
" family=TweedieDist,\n",
" alpha_search=True,\n",
" l1_ratio=1,\n",
" fit_intercept=True,\n",
" formula=formula_noint,\n",
" interaction_separator=\"__x__\",\n",
" categorical_format=\"{name}__{category}\",\n",
")\n",
"t_glm6.fit(df_train, sample_weight=df[\"Exposure\"].values[train])\n",
"\n",
"pd.DataFrame(\n",
" {\"coefficient\": np.concatenate(([t_glm6.intercept_], t_glm6.coef_))},\n",
" index=[\"intercept\"] + t_glm6.feature_names_,\n",
").T"
"with pytest.raises(ValueError, match=\"The formula sets the intercept to False\"):\n",
" t_glm6 = GeneralizedLinearRegressor(\n",
" family=TweedieDist,\n",
" alpha_search=True,\n",
" l1_ratio=1,\n",
" fit_intercept=True,\n",
" formula=formula_noint,\n",
" interaction_separator=\"__x__\",\n",
" categorical_format=\"{name}__{category}\",\n",
" )"
]
},
{
Expand Down
13 changes: 7 additions & 6 deletions src/glum/_glm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -244,8 +244,7 @@ def _parse_formula(
formula : FormulaSpec
The formula to parse.
include_intercept: bool, default True
Whether to include an intercept column if the formula does not
include (``+ 1``) or exclude (``+ 0`` or ``- 1``) it explicitly.
Whether to include an intercept column.

Returns
-------
Expand Down Expand Up @@ -2673,11 +2672,11 @@ def _set_up_and_check_fit_args(

intercept = "1" in X.model_spec.terms
if intercept != self.fit_intercept:
warnings.warn(
f"The formula explicitly sets the intercept to {intercept}, "
f"overriding fit_intercept={self.fit_intercept}."
raise ValueError(
f"The formula sets the intercept to {intercept}, "
f"contradicting fit_intercept={self.fit_intercept}. "
"You should use fit_intercept to specify the intercept."
)
self.fit_intercept = intercept

self.X_model_spec_ = X.model_spec

Expand Down Expand Up @@ -3104,6 +3103,7 @@ class GeneralizedLinearRegressor(GeneralizedLinearRegressorBase):
expected_information : bool, optional (default = False)
If true, then the expected information matrix is computed by default.
Only relevant when computing robust standard errors.

formula : FormulaSpec
A formula accepted by formulaic. It can either be a one-sided formula, in
which case ``y`` must be specified in ``fit``, or a two-sided formula, in
Expand All @@ -3130,6 +3130,7 @@ class GeneralizedLinearRegressor(GeneralizedLinearRegressorBase):
- if 'zero', missing values will represent all-zero indicator columns.
- if 'convert', missing values will be converted to the ``cat_missing_name``
category.

cat_missing_name: str, default='(MISSING)'
Name of the category to which missing values will be converted if
``cat_missing_method='convert'``. Only used if ``X`` is a pandas data frame.
Expand Down
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