From c08d44710048a16ed1e985f931c2b84f1cc40254 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Fri, 22 Nov 2024 21:17:35 +0100 Subject: [PATCH] polars -> narwhals (#714) * polars -> narwhals * remove any polars * trigger Attribute Error in vcov method * add pyarrow to dev dependencies * delete files * finalise narwhals migration * lit does not support numpy, but supports lists * Update changelog * minor cleanup * run through polars when available --- coverage.xml | 1975 +++++++++++----------- docs/changelog.qmd | 3 +- pixi.lock | 1330 ++++++++------- pyfixest/estimation/feols_.py | 12 +- pyfixest/estimation/feols_compressed_.py | 103 +- pyproject.toml | 4 +- 6 files changed, 1718 insertions(+), 1709 deletions(-) diff --git a/coverage.xml b/coverage.xml index acf48075..2b6fb819 100644 --- a/coverage.xml +++ b/coverage.xml @@ -1,12 +1,12 @@ - + C:\Users\alexa\Documents\pyfixest\pyfixest - + @@ -29,9 +29,8 @@ - + - @@ -550,7 +549,7 @@ - + @@ -1199,7 +1198,7 @@ - + @@ -1278,14 +1277,14 @@ - 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- vc14_runtime >=14.42.34433 + - vc14_runtime >=14.40.33810 license: BSD-3-Clause license_family: BSD purls: [] - size: 17572 - timestamp: 1731710685291 + size: 17453 + timestamp: 1728400827536 - kind: pypi name: watchdog version: 6.0.0 @@ -15479,10 +15462,18 @@ packages: - backports-functools-lru-cache>=1.2.1 ; python_full_version < '3.2' - kind: pypi name: webcolors - version: 24.11.1 - url: https://files.pythonhosted.org/packages/60/e8/c0e05e4684d13459f93d312077a9a2efbe04d59c393bc2b8802248c908d4/webcolors-24.11.1-py3-none-any.whl - sha256: 515291393b4cdf0eb19c155749a096f779f7d909f7cceea072791cb9095b92e9 - requires_python: '>=3.9' + version: 24.8.0 + url: https://files.pythonhosted.org/packages/f0/33/12020ba99beaff91682b28dc0bbf0345bbc3244a4afbae7644e4fa348f23/webcolors-24.8.0-py3-none-any.whl + sha256: fc4c3b59358ada164552084a8ebee637c221e4059267d0f8325b3b560f6c7f0a + requires_dist: + - furo ; extra == 'docs' + - sphinx ; extra == 'docs' + - sphinx-copybutton ; extra == 'docs' + - sphinx-inline-tabs ; extra == 'docs' + - sphinx-notfound-page ; extra == 'docs' + - sphinxext-opengraph ; extra == 'docs' + - coverage[toml] ; extra == 'tests' + requires_python: '>=3.8' - kind: pypi name: webencodings version: 0.5.1 @@ -15662,12 +15653,13 @@ packages: timestamp: 1727529943015 - kind: conda name: xorg-libxt - version: 1.3.1 - build: hb9d3cd8_0 + version: 1.3.0 + build: hb9d3cd8_2 + build_number: 2 subdir: linux-64 - url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxt-1.3.1-hb9d3cd8_0.conda - sha256: a8afba4a55b7b530eb5c8ad89737d60d60bc151a03fbef7a2182461256953f0e - md5: 279b0de5f6ba95457190a1c459a64e31 + url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxt-1.3.0-hb9d3cd8_2.conda + sha256: da032ee35fa329af7b551917764d2e161f8af2f7fdbb4c3e4a0fab47f3d968a0 + md5: d8602724ac0d276c380b97e9eb0f814b depends: - __glibc >=2.17,<3.0.a0 - libgcc >=13 @@ -15677,8 +15669,8 @@ packages: license: MIT license_family: MIT purls: [] - size: 379686 - timestamp: 1731860547604 + size: 378681 + timestamp: 1727663260353 - kind: conda name: xorg-xorgproto version: '2024.1' @@ -15751,21 +15743,21 @@ packages: timestamp: 1660346976440 - kind: conda name: zipp - version: 3.21.0 + version: 3.20.2 build: pyhd8ed1ab_0 subdir: noarch noarch: python - url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_0.conda - sha256: 232a30e4b0045c9de5e168dda0328dc0e28df9439cdecdfb97dd79c1c82c4cec - md5: fee389bf8a4843bd7a2248ce11b7f188 + url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.20.2-pyhd8ed1ab_0.conda + sha256: 1e84fcfa41e0afdd87ff41e6fbb719c96a0e098c1f79be342293ab0bd8dea322 + md5: 4daaed111c05672ae669f7036ee5bba3 depends: - python >=3.8 license: MIT license_family: MIT purls: - pkg:pypi/zipp?source=hash-mapping - size: 21702 - timestamp: 1731262194278 + size: 21409 + timestamp: 1726248679175 - kind: conda name: zlib version: 1.3.1 diff --git a/pyfixest/estimation/feols_.py b/pyfixest/estimation/feols_.py index a2f3f8bc..7bfd04e2 100644 --- a/pyfixest/estimation/feols_.py +++ b/pyfixest/estimation/feols_.py @@ -7,7 +7,6 @@ import numba as nb import numpy as np import pandas as pd -import polars as pl from formulaic import Formula from scipy.sparse import diags from scipy.sparse.linalg import lsqr @@ -517,8 +516,13 @@ def vcov( """ # Assuming `data` is the DataFrame in question - if isinstance(data, pl.DataFrame): - data = _narwhals_to_pandas(data) + _data = data if data is not None else self._data + try: + _data = _narwhals_to_pandas(_data) + except TypeError as e: + raise TypeError( + f"The data set must be a DataFrame type. Received: {type(data)}" + ) from e _data = self._data _has_fixef = self._has_fixef @@ -1579,7 +1583,7 @@ def predict( Parameters ---------- newdata : Optional[DataFrameType], optional - A pd.DataFrame or pl.DataFrame with the data to be used for prediction. + A narwhals compatible DataFrame (polars, pandas, duckdb, etc). If None (default), the data used for fitting the model is used. type : str, optional The type of prediction to be computed. diff --git a/pyfixest/estimation/feols_compressed_.py b/pyfixest/estimation/feols_compressed_.py index 2eb01689..6c20514e 100644 --- a/pyfixest/estimation/feols_compressed_.py +++ b/pyfixest/estimation/feols_compressed_.py @@ -1,15 +1,25 @@ +import logging from dataclasses import dataclass from typing import Optional, Union +import narwhals as nw import numpy as np import pandas as pd -import polars as pl from tqdm import tqdm from pyfixest.estimation.feols_ import Feols, PredictionType from pyfixest.estimation.FormulaParser import FixestFormula from pyfixest.utils.dev_utils import DataFrameType +logging.basicConfig(level=logging.INFO) + +try: + import polars as pl + + polars_installed = True +except ImportError: + polars_installed = False + class FeolsCompressed(Feols): """ @@ -129,19 +139,28 @@ def prepare_model_matrix(self): depvars = self._Y.columns.tolist() covars = self._X.columns.tolist() - Y_polars = pl.DataFrame(pd.DataFrame(self._Y)) - X_polars = pl.DataFrame(pd.DataFrame(self._X)) + if polars_installed: + Y_nw = nw.from_native(pl.from_pandas(self._Y)) + X_nw = nw.from_native(pl.from_pandas(self._X)) + else: + logging.info( + "Polars is not installed. Falling back to pandas. You can likely speed up the compression drastically by installing polars." + ) + Y_nw = nw.from_native(self._Y) + X_nw = nw.from_native(self._X) fevars = [] - data_long_mundlak = pl.DataFrame() - if self._has_fixef: self._use_mundlak = True self._has_fixef = False fevars = self._fe.columns.tolist() - fe_polars = pl.DataFrame(pd.DataFrame(self._fe)) - data_long = pl.concat([Y_polars, X_polars, fe_polars], how="horizontal") + if polars_installed: + fe_nw = nw.from_native(pl.from_pandas(self._fe)) + else: + fe_nw = nw.from_native(self._fe) + + data_long = nw.concat([Y_nw, X_nw, fe_nw], how="horizontal") if self._use_mundlak: if len(fevars) > 2: @@ -157,7 +176,7 @@ def prepare_model_matrix(self): # add intercept data_long_mundlak = data_long_mundlak.with_columns( - pl.lit(1).alias("Intercept") + nw.lit(1).alias("Intercept") ) data_long_mundlak = data_long_mundlak.select( ["Intercept"] @@ -168,7 +187,7 @@ def prepare_model_matrix(self): self._fe = None else: - data_long = pl.concat([Y_polars, X_polars], how="horizontal") + data_long = nw.concat([Y_nw, X_nw], how="horizontal") compressed_dict = _regression_compression( depvars=depvars, @@ -257,26 +276,20 @@ def _vcov_hetero(self): return _bread @ _meat @ _bread def _vcov_crv1(self, clustid: np.ndarray, cluster_col: np.ndarray): - _data_long_pl = self._data_long - - # if "Intercept" in self._coefnames and "Intercept" not in _data_long_pl.columns: - # _data_long_pl = _data_long_pl.with_columns([pl.lit(1).alias("Intercept")]) - # _data_long_pl = _data_long_pl.select( - # ["Intercept"] + _data_long_pl.columns[:-1] - # ) + _data_long_nw = self._data_long - X_long = _data_long_pl.select(self._coefnames).to_numpy() - Y_long = _data_long_pl.select(self._depvar).to_numpy() + X_long = _data_long_nw.select(self._coefnames).to_numpy() + Y_long = _data_long_nw.select(self._depvar).to_numpy() yhat = X_long @ self._beta_hat uhat = Y_long.flatten() - yhat - _data_long_pl = _data_long_pl.with_columns( + _data_long_nw = _data_long_nw.with_columns( [ - pl.lit(yhat).alias("yhat"), - pl.lit(uhat).alias("uhat"), - pl.lit(yhat + uhat).alias("yhat_g_boot_pos"), # rademacher weights = 1 - pl.lit(yhat - uhat).alias("yhat_g_boot_neg"), # rademacher weights = -1 + nw.lit(yhat.tolist()).alias("yhat"), + nw.lit(uhat.tolist()).alias("uhat"), + nw.lit(yhat + uhat).alias("yhat_g_boot_pos"), # rademacher weights = 1 + nw.lit(yhat - uhat).alias("yhat_g_boot_neg"), # rademacher weights = -1 ] ) @@ -285,24 +298,24 @@ def _vcov_crv1(self, clustid: np.ndarray, cluster_col: np.ndarray): beta_boot = np.zeros((boot_iter, self._k)) clustervar = self._clustervar - cluster = _data_long_pl[clustervar] + cluster = _data_long_nw[clustervar] cluster_ids = np.sort(np.unique(cluster).astype(np.int32)) - _data_long_pl = _data_long_pl.with_columns(pl.col(clustervar[0]).cast(pl.Int32)) + _data_long_nw = _data_long_nw.with_columns(nw.col(clustervar[0]).cast(nw.Int32)) for b in tqdm(range(boot_iter)): - boot_df = pl.DataFrame( + boot_df = nw.from_native( { "coin_flip": rng.integers(0, 2, size=len(cluster_ids)), f"{clustervar[0]}": cluster_ids, } ) - df_boot = _data_long_pl.join(boot_df, on=f"{clustervar[0]}", how="left") + df_boot = _data_long_nw.join(boot_df, on=f"{clustervar[0]}", how="left") df_boot = df_boot.with_columns( [ - pl.when(pl.col("coin_flip") == 1) - .then(pl.col("yhat_g_boot_pos")) - .otherwise(pl.col("yhat_g_boot_neg")) + nw.when(nw.col("coin_flip") == 1) + .then(nw.col("yhat_g_boot_pos")) + .otherwise(nw.col("yhat_g_boot_neg")) .alias("yhat_boot") ] ) @@ -359,20 +372,20 @@ def predict( @dataclass class _RegressionCompressionData: - Y: pl.DataFrame - X: pl.DataFrame - fe: Optional[pl.DataFrame] - compression_count: pl.DataFrame - Yprime: Optional[pl.DataFrame] - Yprimeprime: Optional[pl.DataFrame] - df_compressed: pl.DataFrame + Y: nw.DataFrame + X: nw.DataFrame + fe: Optional[nw.DataFrame] + compression_count: nw.DataFrame + Yprime: Optional[nw.DataFrame] + Yprimeprime: Optional[nw.DataFrame] + df_compressed: nw.DataFrame def _regression_compression( depvars: list[str], covars: list[str], fevars: Optional[list[str]], - data_long: pl.DataFrame, + data_long: nw.DataFrame, short: bool = False, ) -> _RegressionCompressionData: "Compress data for regression based on sufficient statistics." @@ -384,19 +397,19 @@ def _regression_compression( data_long = data_long.lazy() - agg_expressions.append(pl.count(depvars[0]).alias("count")) + agg_expressions.append(nw.col(depvars[0]).count().alias("count")) if not short: for var in depvars: - agg_expressions.append(pl.sum(var).alias(f"sum_{var}")) - agg_expressions.append(pl.col(var).pow(2).sum().alias(f"sum_{var}_sq")) + agg_expressions.append(nw.sum(var).alias(f"sum_{var}")) + agg_expressions.append((nw.col(var) ** 2).sum().alias(f"sum_{var}_sq")) df_compressed = data_long.group_by(covars_updated).agg(agg_expressions) mean_expressions = [] for var in depvars: mean_expressions.append( - (pl.col(f"sum_{var}") / pl.col("count")).alias(f"mean_{var}") + (nw.col(f"sum_{var}") / nw.col("count")).alias(f"mean_{var}") ) df_compressed = df_compressed.with_columns(mean_expressions) @@ -414,8 +427,8 @@ def _regression_compression( def _mundlak_transform( - covars: list[str], fevars: list[str], data_long: pl.DataFrame -) -> Union[pl.DataFrame, list[str]]: + covars: list[str], fevars: list[str], data_long: nw.DataFrame +) -> Union[nw.DataFrame, list[str]]: "Compute the Mundlak transformation of the data." covars_updated = covars.copy() # Factorize and prepare group-wise mean calculations in one go @@ -425,7 +438,7 @@ def _mundlak_transform( covars_updated.append(mean_var_name) # Add mean calculation for this var by factorized fevar data_long = data_long.with_columns( - pl.col(var).mean().over(fevar).alias(mean_var_name) + nw.col(var).mean().over(fevar).alias(mean_var_name) ) return data_long, covars_updated diff --git a/pyproject.toml b/pyproject.toml index bf13e232..2f6d0633 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,7 +18,6 @@ dependencies = [ "numba>=0.58.0", "seaborn>=0.13.2", "tabulate>=0.9.0", - "polars>=0.20.1", "tqdm==4.66.4", "great-tables>=0.10.0", "numpy>=1.19.0", @@ -34,12 +33,13 @@ dev = [ "pytest-cov>=4.1.0", "pytest-xdist>=3.5.0", "pyhdfe>=0.2.0", - "pyarrow>=14.0.0", "pre-commit==3.6.0", "doubleml==0.7.1", "wildboottest>=0.3.2", "ipykernel>=6.29.5,<7", "duckdb>=1.1.3", + "polars>=1.0.0", + "pyarrow>=14.0" ] docs = [