From 6bb8cfb9cf9db5a96a64c5919c10541237db33d2 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Fri, 1 Aug 2025 11:37:34 +0200 Subject: [PATCH 01/11] first pass --- .../direct_vs_recursive_forecasting.py | 310 -------- content/python_files/feature_engineering.py | 21 +- .../multiple_horizons_prediction.py | 326 --------- content/python_files/prediction_intervals.py | 681 ------------------ 4 files changed, 12 insertions(+), 1326 deletions(-) delete mode 100644 content/python_files/direct_vs_recursive_forecasting.py delete mode 100644 content/python_files/multiple_horizons_prediction.py delete mode 100644 content/python_files/prediction_intervals.py diff --git a/content/python_files/direct_vs_recursive_forecasting.py b/content/python_files/direct_vs_recursive_forecasting.py deleted file mode 100644 index 297aff1..0000000 --- a/content/python_files/direct_vs_recursive_forecasting.py +++ /dev/null @@ -1,310 +0,0 @@ -# %% [markdown] -# # Direct vs Recursive Forecasting -# -# The purpose of this notebook is to compare the performance of direct -# forecasting and recursive forecasting using the `MLForecast` library. Direct -# forecasting means that we train a family of model to predict the target value -# at various horizons in the future, e.g. 1 hour, 2 hours, ..., 24 hours ahead. -# Recursive forecasting (also known as auto-regressive forecasting) means that -# we train a single model to predict the target value at the next time step, -# and then use the model recursively to predict the next time step using the -# previous predictions as input features. Implementing recursive forecasting is -# a bit cumbersome to do manually, hence we use the `MLForecast` library to -# handle this for us. -# -# The objective is to show that recursive forecasting can be more efficient in -# terms of memory usage and training time. However, it can also lead to a loss -# of accuracy because recursive calls are fed with previous predictions that do -# not necessarily match the training distribution of the model, and can -# therefore lead to degenerate predictions, in particular when the variance of -# the lagged values is informative. -# -# We highlight this issue with a synthetic dataset that has two types of -# segments: -# -# - Segment type "a" has a prefix centered around 0 with low variance and a -# suffix centered around 1. -# - Segment type "b" has a prefix centered around 0 with high variance and a -# suffix centered around -1. -# -# Segment of type "a" and "b" are independently sampled, meaning that is not -# possible to forecast beyond the length of the segments. However, it should be -# quite easy to predict the end of a segment given the prefix of the segment -# with lagged feature engineering. - -# %% -from time import perf_counter -import numpy as np -import matplotlib.pyplot as plt -import pandas as pd -import tzdata # noqa: F401 - -SEGMENT_LENGTH = 30 - - -def generate_synthetic_1( - segment_length=SEGMENT_LENGTH, - n_segments=100, - low_noise_level=0.01, - high_noise_level=0.1, - seed=None, -): - """Generate synthetic time series data with two types of segments - - - segment type "a" has a prefix centered around 0 and a suffix centered - around 1. - - segment type "b" has a prefix centered around 0 with high variance and a - suffix centered around -1. - - The variance of the prefix is therefore predictive of the suffix. - - The suffix values predictive of the next segment prefix's mean (always 0). - """ - rng = np.random.default_rng(seed) - total_length = segment_length * n_segments - segment_types = rng.choice(["a", "b"], n_segments) - prefix_length = segment_length // 2 - suffix_length = segment_length - prefix_length - - segments = [] - for segment_type in segment_types: - if segment_type == "a": - # Prefix is centered around 0 with low variance - segments.append( - rng.normal(loc=0, scale=low_noise_level, size=prefix_length) - ) - # Suffix is centered around 1 with low variance - segments.append( - rng.normal(loc=1, scale=low_noise_level, size=suffix_length) - ) - elif segment_type == "b": - # Prefix is also centered around 0 but with high variance - segments.append( - rng.normal(loc=0, scale=high_noise_level, size=prefix_length) - ) - # Suffix is centered around -1 with low variance - segments.append( - rng.normal(loc=-1, scale=low_noise_level, size=suffix_length) - ) - return pd.DataFrame( - { - "time": np.arange(total_length), - "y": np.concatenate(segments), - "series_id": np.zeros(total_length, dtype=np.int32), - } - ) - - -data = generate_synthetic_1(n_segments=500, seed=1) -cutoff = -SEGMENT_LENGTH * 10 # 10 segments for testing -data_train = data.iloc[:cutoff] -data_test = data.iloc[cutoff:] -_ = data_train.plot(x="time", y="y", title="Synthetic data 1", figsize=(15, 5)) - -# %% -_ = data_train.iloc[: SEGMENT_LENGTH * 10].plot( - x="time", - y="y", - title="Synthetic data 1 - First points of training set", - figsize=(15, 5), -) - -_ = data_test.iloc[: SEGMENT_LENGTH * 10].plot( - x="time", - y="y", - title="Synthetic data 1 - First points of testing set", - figsize=(15, 5), -) - -# %% -from mlforecast import MLForecast -from sklearn.pipeline import make_pipeline -from sklearn.preprocessing import SplineTransformer, PolynomialFeatures -from sklearn.kernel_approximation import Nystroem -from mlforecast.lag_transforms import ( - RollingMax, - RollingMin, - RollingMean, - RollingStd, -) -from mlforecast.target_transforms import Differences -from sklearn.linear_model import Ridge -from sklearn.ensemble import RandomForestRegressor, HistGradientBoostingRegressor -from sklearn.feature_selection import SelectKBest -from sklearn.tree import DecisionTreeRegressor -import warnings -import threadpoolctl - -# Workaround a performance problem with HistGradientBoostingRegressor on small datasets. -threadpoolctl.threadpool_limits(limits=1, user_api="openmp") -warnings.filterwarnings("ignore", category=RuntimeWarning, module="sklearn") - - -# MLForecast can train multiple models in parallel, each model can be a -# pipeline of transformers and a regressor. However we focus on a single -# HistGradientBoostingRegressor model to make sure that this notebook runs -# quickly enough. Feel free to uncomment the other models to compare their -# performance if you have enough time and memory available. -# -# Spoiler alert: the HistGradientBoostingRegressor model is the most accurate. - -mlf = MLForecast( - models=[ - # make_pipeline( - # SplineTransformer(sparse_output=True, n_knots=10), - # PolynomialFeatures(degree=2, include_bias=False, interaction_only=True), - # # Nystroem(kernel="poly", n_components=200, degree=2, random_state=0), - # SelectKBest(k=100), - # Ridge(alpha=1e-6), - # ), - # RandomForestRegressor( - # n_estimators=100, - # max_features=0.8, - # max_depth=8, - # min_samples_leaf=300, - # n_jobs=4, - # ), - # DecisionTreeRegressor(max_depth=8, min_samples_leaf=300), - HistGradientBoostingRegressor(), - ], - freq=1, - lags=range(1, SEGMENT_LENGTH + 1), - lag_transforms={ - 1: [ - RollingMean(SEGMENT_LENGTH // 2), - RollingStd(SEGMENT_LENGTH // 2), - ], - SEGMENT_LENGTH - // 2: [ - RollingMax(SEGMENT_LENGTH // 2), - RollingMin(SEGMENT_LENGTH // 2), - ], - }, - # target_transforms=[Differences([1])], - num_threads=4, -) -schema = dict( - time_col="time", - id_col="series_id", - target_col="y", -) - -# %% -mlf.preprocess(data_train, **schema) - -# %% [markdown] -# -# ## Recursive or auto-regressive forecasting - -# %% -tic = perf_counter() -mlf.fit(data_train, **schema) # recursive forecasting by default in mlforecast -print(f"Recursive forecasting training time: {perf_counter() - tic:.1f} seconds") - -# %% -PREDICTION_HORIZON = SEGMENT_LENGTH * 2 - - -def collect_predictions(mlf, data_test, test_offset=0): - """Collect predictions from the MLForecast object.""" - all_predictions = [] - UPDATE_CHUNK_SIZE = 5 - while test_offset < len(data_test): - - new_predictions = mlf.predict(PREDICTION_HORIZON) - new_predictions["horizon"] = np.arange(new_predictions.shape[0]) + 1 - new_predictions = new_predictions.merge( - data_test, on=["time", "series_id"], how="left" - ) - all_predictions.append(new_predictions) - - # Update the forecaster with the new observations - mlf.update(data_test.iloc[test_offset : test_offset + UPDATE_CHUNK_SIZE]) - test_offset += UPDATE_CHUNK_SIZE - - return all_predictions - - -tic = perf_counter() -all_recursive_predictions = collect_predictions(mlf, data_test) -print(f"Recursive forecasting prediction time: {perf_counter() - tic:.1f} seconds") - -# %% [markdown] -# -# ## Direct forecasting -# -# Let's pass `max_horizon` to force modeling for direct forecasting. - -# %% -tic = perf_counter() -mlf.fit(data_train, max_horizon=PREDICTION_HORIZON, **schema) -print(f"Direct forecasting training time: {perf_counter() - tic:.1f} seconds") - -# %% -tic = perf_counter() -all_direct_predictions = collect_predictions(mlf, data_test) -print(f"Direct forecasting prediction time: {perf_counter() - tic:.1f} seconds") - -# %% [markdown] -# -# ## Quantitative comparison - - -# %% -def score_predictions(all_predictions, model_name): - """Compute the mean absolute error of the predictions.""" - all_predictions = pd.concat(all_predictions) - all_predictions["absolute_error"] = np.abs( - all_predictions["y"] - all_predictions[model_name] - ) - return all_predictions.dropna().groupby("horizon") - - -import matplotlib.pyplot as plt - -fig, ax = plt.subplots() -score_predictions( - all_recursive_predictions, "HistGradientBoostingRegressor" -).mean().reset_index().plot(x="horizon", y="absolute_error", label="recursive", ax=ax) -score_predictions( - all_direct_predictions, "HistGradientBoostingRegressor" -).mean().reset_index().plot(x="horizon", y="absolute_error", label="direct ", ax=ax) -_ = ax.set(ylabel="MAE") - -# %% [markdown] -# -# ## Qualitative comparison - - -# %% -def plot_some_predictions(all_predictions, data_test, model_name, nrows=12, title=None): - - fig, axes = plt.subplots(nrows=nrows, figsize=(15, 5 * nrows)) - for row_idx, predictions in enumerate(all_predictions): - predictions = predictions.drop("y", axis=1) - merged_data = data_test.copy() - merged_data = merged_data.merge( - predictions, on=["time", "series_id"], how="left" - ) - merged_data.drop(["series_id"], axis=1).iloc[: SEGMENT_LENGTH * 3].plot( - x="time", y=["y", model_name], ax=axes[row_idx] - ) - axes[row_idx].set_title(title) - axes[row_idx].set_ylim(-1.2, 1.2) - - if row_idx >= nrows - 1: - break - - -# %% -plot_some_predictions( - all_recursive_predictions, - data_test, - "HistGradientBoostingRegressor", - title="recursive", -) - -# %% -plot_some_predictions( - all_direct_predictions, data_test, "HistGradientBoostingRegressor", title="direct" -) diff --git a/content/python_files/feature_engineering.py b/content/python_files/feature_engineering.py index a9dddad..abeb7e9 100644 --- a/content/python_files/feature_engineering.py +++ b/content/python_files/feature_engineering.py @@ -13,15 +13,14 @@ # All these data sources cover a time range from March 23, 2021 to May 31, # 2025. # -# Since our maximum forecasting horizon is 24 hours, we consider that the +# Since our forecasting horizon is 24 hours, we consider that the # future weather data is known at a chosen prediction time. Similarly, the # holidays and calendar features are known at prediction time for any point in # the future. +# We can also use the load data to engineer some lagged features and rolling +# aggregations. # -# Therefore, exogenous features derived from the weather and calendar data can -# be used to engineer "future covariates". Since the load data is our -# prediction target, we will can also use it to engineer "past covariates" such -# as lagged features and rolling aggregations. The future values of the load +# The future values of the load # data (with respect to the prediction time) are used as targets for the # forecasting model. # @@ -41,8 +40,6 @@ # The following 3 imports are only needed to workaround some limitations when # using polars in a pyodide/jupyterlite notebook. # -# TODO: remove those workarounds once pyodide 0.28 is released with support for -# the latest polars version. # %% import tzdata # noqa: F401 @@ -64,9 +61,9 @@ # range is in UTC timezone to avoid any ambiguity when joining with the weather # data that is also in UTC. # -# We wrap the resulting polars dataframe in a `skrub` expression to benefit +# We wrap the resulting polars dataframe in a `skrub` DataOp to benefit # from the built-in `skrub.TableReport` display in the notebook. Using the -# `skrub` expression system will also be useful for other reasons: all +# `skrub` DataOps will also be useful for other reasons: all # operations in this notebook chain operations chained together in a directed # acyclic graph that is automatically tracked by `skrub`. This allows us to # extract the resulting pipeline and apply it to new data later on, exactly @@ -206,6 +203,12 @@ def prepare_french_calendar_data(time): ) +from skrub import DatetimeEncoder + +datetime_encoder = DatetimeEncoder( + add_weekday=True, add_day_of_year=True, add_total_seconds=False +) + calendar = prepare_french_calendar_data(time) calendar diff --git a/content/python_files/multiple_horizons_prediction.py b/content/python_files/multiple_horizons_prediction.py deleted file mode 100644 index 2e045d7..0000000 --- a/content/python_files/multiple_horizons_prediction.py +++ /dev/null @@ -1,326 +0,0 @@ -# %% [markdown] -# -# # Multiple horizons predictive modeling -# -# ## Environment setup -# -# We need to install some extra dependencies for this notebook if needed (when -# running jupyterlite). We need the development version of skrub to be able to -# use the skrub expressions. - -# %% -# %pip install -q https://pypi.anaconda.org/ogrisel/simple/polars/1.24.0/polars-1.24.0-cp39-abi3-emscripten_3_1_58_wasm32.whl -# %pip install -q https://pypi.anaconda.org/ogrisel/simple/skrub/0.6.dev0/skrub-0.6.dev0-py3-none-any.whl -# %pip install -q altair holidays plotly nbformat - -# %% -import datetime -import warnings - -import altair -import cloudpickle -import pyarrow # noqa: F401 -import tzdata # noqa: F401 - -from tutorial_helpers import plot_horizon_forecast - -# Ignore warnings from pkg_resources triggered by Python 3.13's multiprocessing. -warnings.filterwarnings("ignore", category=UserWarning, module="pkg_resources") - - -# %% -with open("feature_engineering_pipeline.pkl", "rb") as f: - feature_engineering_pipeline = cloudpickle.load(f) - - -features = feature_engineering_pipeline["features"] -targets = feature_engineering_pipeline["targets"] -prediction_time = feature_engineering_pipeline["prediction_time"] -horizons = feature_engineering_pipeline["horizons"] -target_column_name_pattern = feature_engineering_pipeline["target_column_name_pattern"] - - -# %% [markdown] -# -# ## Predicting multiple horizons with a grid of single output models -# -# Usually, it is really common to predict values for multiple horizons at once. The most -# naive approach is to train as many models as there are horizons. To achieve this, -# scikit-learn provides a meta-estimator called `MultiOutputRegressor` that can be used -# to train a single model that predicts multiple horizons at once. -# -# In short, we only need to provide multiple targets where each column corresponds to -# an horizon and this meta-estimator will train an independent model for each column. -# However, we could expect that the quality of the forecast might degrade as the horizon -# increases. -# -# Let's train a gradient boosting regressor for each horizon. - -# %% -from sklearn.multioutput import MultiOutputRegressor -from sklearn.ensemble import HistGradientBoostingRegressor - -multioutput_predictions = features.skb.apply( - MultiOutputRegressor( - estimator=HistGradientBoostingRegressor(random_state=0), n_jobs=-1 - ), - y=targets.skb.drop(cols=["prediction_time", "load_mw"]).skb.mark_as_y(), -) - -# %% [markdown] -# -# Now, let's just rename the columns for the predictions to make it easier to plot -# the horizon forecast. - -# %% -target_column_names = [target_column_name_pattern.format(horizon=h) for h in horizons] -predicted_target_column_names = [ - f"predicted_{target_column_name}" for target_column_name in target_column_names -] -named_predictions = multioutput_predictions.rename( - {k: v for k, v in zip(target_column_names, predicted_target_column_names)} -) - -# %% [markdown] -# -# Let's plot the horizon forecast on a training data to check the validity of the -# output. - -# %% -plot_at_time = datetime.datetime(2021, 4, 19, 0, 0, tzinfo=datetime.timezone.utc) -plot_horizon_forecast( - targets, - named_predictions, - plot_at_time, - target_column_name_pattern, -).skb.preview() - -# %% [markdown] -# -# On this curve, the red line corresponds to the observed values past to the the date -# for which we would like to forecast. The orange line corresponds to the observed -# values for the next 24 hours and the blue line corresponds to the predicted values -# for the next 24 hours. -# -# Since we are using a strong model and very few training data to check the validity -# we observe that our model perfectly fits the training data. -# -# So, we are now ready to assess the performance of this multi-output model and we need -# to cross-validate it. Since we do not want to aggregate the metrics for the different -# horizons, we need to create a scikit-learn scorer in which we set -# `multioutput="raw_values"` to get the scores for each horizon. -# -# Passing this scorer to the `cross_validate` function returns all horizons scores. - -# %% -from sklearn.model_selection import TimeSeriesSplit - - -max_train_size = 2 * 52 * 24 * 7 # max ~2 years of training data -test_size = 24 * 7 * 24 # 24 weeks of test data -gap = 7 * 24 # 1 week gap between train and test sets -ts_cv_5 = TimeSeriesSplit( - n_splits=5, max_train_size=max_train_size, test_size=test_size, gap=gap -) - -# %% -from sklearn.metrics import r2_score, mean_absolute_percentage_error - - -def multioutput_scorer(regressor, X, y, score_func, score_name): - y_pred = regressor.predict(X) - return { - f"{score_name}_horizon_{h}h": score - for h, score in enumerate( - score_func(y, y_pred, multioutput="raw_values"), start=1 - ) - } - - -def scoring(regressor, X, y): - return { - **multioutput_scorer(regressor, X, y, mean_absolute_percentage_error, "mape"), - **multioutput_scorer(regressor, X, y, r2_score, "r2"), - } - - -multioutput_cv_results = multioutput_predictions.skb.cross_validate( - cv=ts_cv_5, - scoring=scoring, - return_train_score=True, - verbose=1, - n_jobs=-1, -) - -# %% [markdown] -# -# One thing that we observe is that training such multi-output model is expensive. It is -# expected since each horizon involves a different model and thus a training. - -# %% -multioutput_cv_results.round(3) - -# %% [markdown] -# -# Instead of reading the results in the table, we can plot the scores depending on the -# type of data and the metric. - -# %% -import itertools -from IPython.display import display - -for metric_name, dataset_type in itertools.product(["mape", "r2"], ["train", "test"]): - columns = multioutput_cv_results.columns[ - multioutput_cv_results.columns.str.startswith(f"{dataset_type}_{metric_name}") - ] - data_to_plot = multioutput_cv_results[columns] - data_to_plot.columns = [ - col.replace(f"{dataset_type}_", "") - .replace(f"{metric_name}_", "") - .replace("_", " ") - for col in columns - ] - - data_long = data_to_plot.melt(var_name="horizon", value_name="score") - chart = ( - altair.Chart( - data_long, - title=f"{dataset_type.title()} {metric_name.upper()} scores by horizon", - ) - .mark_boxplot(extent="min-max") - .encode( - x=altair.X( - "horizon:N", - title="Horizon", - sort=altair.Sort( - [f"horizon {h}h" for h in range(1, data_to_plot.shape[1])] - ), - ), - y=altair.Y("score:Q", title=f"{metric_name.upper()} Score"), - color=altair.Color("horizon:N", legend=None), - ) - ) - - display(chart) - -# %% [markdown] -# -# An interesting and unexpected observation is that the MAPE error on the test -# data is first increases and then decreases once past the horizon 18h. We -# would not necessarily expect this behaviour. -# -# ## Native multi-output handling using `RandomForestRegressor` -# -# In the previous section, we showed how to wrap a `HistGradientBoostingRegressor` -# in a `MultiOutputRegressor` to predict multiple horizons. With such a strategy, it -# means that we trained independent `HistGradientBoostingRegressor`, one for each -# horizon. -# -# `RandomForestRegressor` natively supports multi-output regression: instead of -# independently training a model per horizon, it will train a joint model that -# predicts all horizons at once. -# -# Repeat the previous analysis using a `RandomForestRegressor`. Fix the parameter -# `min_samples_leaf` to 30 to limit the depth. -# -# Once you created the model, plot the horizon forecast for a given date and time. -# In addition, compute the cross-validated predictions and plot the R2 and MAPE -# scores for each horizon. -# -# Does this model perform better or worse than the previous model? - -# %% -from sklearn.ensemble import RandomForestRegressor - -# %% -# Write your code here. -# -# -# -# -# -# -# -# -# -# -# - -# %% -multioutput_predictions_rf = features.skb.apply( - RandomForestRegressor(min_samples_leaf=30, random_state=0, n_jobs=-1), - y=targets.skb.drop(cols=["prediction_time", "load_mw"]).skb.mark_as_y(), -) - -# %% -named_predictions_rf = multioutput_predictions_rf.rename( - {k: v for k, v in zip(target_column_names, predicted_target_column_names)} -) - -# %% -plot_at_time = datetime.datetime(2021, 4, 24, 0, 0, tzinfo=datetime.timezone.utc) -plot_horizon_forecast( - targets, - named_predictions_rf, - plot_at_time, - target_column_name_pattern, -).skb.preview() - -# %% -multioutput_cv_results_rf = multioutput_predictions_rf.skb.cross_validate( - cv=ts_cv_5, - scoring=scoring, - return_train_score=True, - verbose=1, - n_jobs=-1, -) - -# %% -multioutput_cv_results_rf.round(3) - -# %% -import itertools -from IPython.display import display - -for metric_name, dataset_type in itertools.product(["mape", "r2"], ["train", "test"]): - columns = multioutput_cv_results_rf.columns[ - multioutput_cv_results_rf.columns.str.startswith( - f"{dataset_type}_{metric_name}" - ) - ] - data_to_plot = multioutput_cv_results_rf[columns] - data_to_plot.columns = [ - col.replace(f"{dataset_type}_", "") - .replace(f"{metric_name}_", "") - .replace("_", " ") - for col in columns - ] - - data_long = data_to_plot.melt(var_name="horizon", value_name="score") - chart = ( - altair.Chart( - data_long, - title=f"{dataset_type.title()} {metric_name.upper()} Scores by Horizon", - ) - .mark_boxplot(extent="min-max") - .encode( - x=altair.X( - "horizon:N", - title="Horizon", - sort=altair.Sort( - [f"horizon {h}h" for h in range(1, data_to_plot.shape[1])] - ), - ), - y=altair.Y("score:Q", title=f"{metric_name.upper()} Score"), - color=altair.Color("horizon:N", legend=None), - ) - ) - - display(chart) - -# %% [markdown] -# -# We observe that the performance of the `RandomForestRegressor` is not better in terms -# of scores or computational cost. The trend of the scores along the horizon is also -# different from the `HistGradientBoostingRegressor`: the scores worsen as the horizon -# increases. diff --git a/content/python_files/prediction_intervals.py b/content/python_files/prediction_intervals.py deleted file mode 100644 index 5e922fb..0000000 --- a/content/python_files/prediction_intervals.py +++ /dev/null @@ -1,681 +0,0 @@ -# %% [markdown] -# -# # Computing prediction intervals using quantile regression -# -# ## Environment setup -# -# We need to install some extra dependencies for this notebook if needed (when -# running jupyterlite). We need the development version of skrub to be able to -# use the skrub expressions. - -# %% -# %pip install -q https://pypi.anaconda.org/ogrisel/simple/polars/1.24.0/polars-1.24.0-cp39-abi3-emscripten_3_1_58_wasm32.whl -# %pip install -q https://pypi.anaconda.org/ogrisel/simple/skrub/0.6.dev0/skrub-0.6.dev0-py3-none-any.whl -# %pip install -q altair holidays plotly nbformat - -# %% -import warnings - -import altair -import cloudpickle -import numpy as np -import pyarrow # noqa: F401 -import polars as pl -import tzdata # noqa: F401 - -from tutorial_helpers import ( - binned_coverage, - plot_lorenz_curve, - plot_reliability_diagram, - plot_residuals_vs_predicted, - collect_cv_predictions, -) - -# Ignore warnings from pkg_resources triggered by Python 3.13's multiprocessing. -warnings.filterwarnings("ignore", category=UserWarning, module="pkg_resources") - - -# %% -with open("feature_engineering_pipeline.pkl", "rb") as f: - feature_engineering_pipeline = cloudpickle.load(f) - - -features = feature_engineering_pipeline["features"] -targets = feature_engineering_pipeline["targets"] -prediction_time = feature_engineering_pipeline["prediction_time"] -horizons = feature_engineering_pipeline["horizons"] -target_column_name_pattern = feature_engineering_pipeline["target_column_name_pattern"] - - -# %% [markdown] -# ### Define the quantile regressors -# -# In this section, we show how one can use a gradient boosting but modify the loss -# function to predict different quantiles and thus obtain an uncertainty quantification -# of the predictions. -# -# In terms of evaluation, we reuse the R2 and MAPE scores. However, they are not helpful -# to assess the reliability of quantile models. For this purpose, we use a derivate of -# the metric minimize by those models: the pinball loss. We use the D2 score that is -# easier to interpret since the best possible score is bounded by 1 and a score of 0 -# corresponds to constant predictions at the target quantile. - -# %% -horizon_of_interest = horizons[-1] # Focus on the 24-hour horizon -target_column_name = target_column_name_pattern.format(horizon=horizon_of_interest) -predicted_target_column_name = "predicted_" + target_column_name -target = targets[target_column_name].skb.mark_as_y() -target - -# %% -from sklearn.metrics import get_scorer, make_scorer -from sklearn.metrics import mean_absolute_percentage_error, d2_pinball_score - -scoring = { - "r2": get_scorer("r2"), - "mape": make_scorer(mean_absolute_percentage_error), - "d2_pinball_05": make_scorer(d2_pinball_score, alpha=0.05), - "d2_pinball_50": make_scorer(d2_pinball_score, alpha=0.50), - "d2_pinball_95": make_scorer(d2_pinball_score, alpha=0.95), -} - -# %% [markdown] -# -# We know define three different models: -# -# - a model predicting the 5th percentile of the load -# - a model predicting the median of the load -# - a model predicting the 95th percentile of the load - -# %% -from sklearn.ensemble import HistGradientBoostingRegressor - - -common_params = dict( - loss="quantile", learning_rate=0.1, max_leaf_nodes=100, random_state=0 -) -predictions_hgbr_05 = features.skb.apply( - HistGradientBoostingRegressor(**common_params, quantile=0.05), - y=target, -) -predictions_hgbr_50 = features.skb.apply( - HistGradientBoostingRegressor(**common_params, quantile=0.5), - y=target, -) -predictions_hgbr_95 = features.skb.apply( - HistGradientBoostingRegressor(**common_params, quantile=0.95), - y=target, -) - -# %% [markdown] -# -# ### Evaluation via cross-validation -# -# We evaluate the performance of the quantile regressors via cross-validation. - -# %% -from sklearn.model_selection import TimeSeriesSplit - - -max_train_size = 2 * 52 * 24 * 7 # max ~2 years of training data -test_size = 24 * 7 * 24 # 24 weeks of test data -gap = 7 * 24 # 1 week gap between train and test sets -ts_cv_5 = TimeSeriesSplit( - n_splits=5, max_train_size=max_train_size, test_size=test_size, gap=gap -) - -# %% -cv_results_hgbr_05 = predictions_hgbr_05.skb.cross_validate( - cv=ts_cv_5, - scoring=scoring, - return_pipeline=True, - verbose=1, - n_jobs=-1, -) -cv_results_hgbr_50 = predictions_hgbr_50.skb.cross_validate( - cv=ts_cv_5, - scoring=scoring, - return_pipeline=True, - verbose=1, - n_jobs=-1, -) -cv_results_hgbr_95 = predictions_hgbr_95.skb.cross_validate( - cv=ts_cv_5, - scoring=scoring, - return_pipeline=True, - verbose=1, - n_jobs=-1, -) - -# %% [markdown] -# -# Let's first collect all the cross-validated predictions to make further inspection. - -# %% -cv_predictions_hgbr_05 = collect_cv_predictions( - cv_results_hgbr_05["pipeline"], ts_cv_5, predictions_hgbr_05, prediction_time -) -cv_predictions_hgbr_50 = collect_cv_predictions( - cv_results_hgbr_50["pipeline"], ts_cv_5, predictions_hgbr_50, prediction_time -) -cv_predictions_hgbr_95 = collect_cv_predictions( - cv_results_hgbr_95["pipeline"], ts_cv_5, predictions_hgbr_95, prediction_time -) - -# %% [markdown] -# -# Now, let's make a plot of the predictions for each model and thus we need to gather -# all the predictions in a single dataframe. - -# %% -results = pl.concat( - [ - cv_predictions_hgbr_05[0].rename({"predicted_load_mw": "predicted_load_mw_05"}), - cv_predictions_hgbr_50[0].select("predicted_load_mw").rename( - {"predicted_load_mw": "predicted_load_mw_50"} - ), - cv_predictions_hgbr_95[0].select("predicted_load_mw").rename( - {"predicted_load_mw": "predicted_load_mw_95"} - ), - ], - how="horizontal", -).tail(24 * 10) - -# %% [markdown] -# -# Now, we plot the observed values and the predicted median with a line. In addition, -# we plot the 5th and 95th percentiles as a shaded area. It means that between those -# two bounds, we expect to find 90% of the observed values. -# -# We plot this information on a portion of the test data from the first fold of the -# cross-validation. - -# %% -median_chart = ( - altair.Chart(results) - .transform_fold(["load_mw", "predicted_load_mw_50"]) - .mark_line(tooltip=True) - .encode(x="prediction_time:T", y="value:Q", color="key:N") -) - -# Add a column for the band legend -results_with_band = results.with_columns(pl.lit("90% interval").alias("band_type")) - -quantile_band_chart = ( - altair.Chart(results_with_band) - .mark_area(opacity=0.4, tooltip=True) - .encode( - x="prediction_time:T", - y="predicted_load_mw_05:Q", - y2="predicted_load_mw_95:Q", - color=altair.Color("band_type:N", scale=altair.Scale(range=["lightgreen"])), - ) -) - -combined_chart = quantile_band_chart + median_chart -combined_chart.resolve_scale(color="independent").interactive() - -# %% [markdown] -# -# Now, we can inspect the cross-validated metrics for each model. - -# %% -cv_results_hgbr_05[ - [col for col in cv_results_hgbr_05.columns if col.startswith("test_")] -].mean(axis=0).round(3) - -# %% -cv_results_hgbr_50[ - [col for col in cv_results_hgbr_50.columns if col.startswith("test_")] -].mean(axis=0).round(3) - -# %% -cv_results_hgbr_95[ - [col for col in cv_results_hgbr_95.columns if col.startswith("test_")] -].mean(axis=0).round(3) - -# %% [markdown] -# -# Focusing on the different D2 scores, we observe that each model minimize the D2 score -# associated to the target quantile that we set. For instance, the model predicting the -# 5th percentile obtained the highest D2 pinball score with `alpha=0.05`. It is expected -# but a confirmation of what loss each model minimizes. -# -# Now, let's collect the cross-validated predictions and plot the residual vs predicted -# values for the different models. - -# %% -plot_residuals_vs_predicted(cv_predictions_hgbr_05).interactive().properties( - title=( - "Residuals vs Predicted Values from cross-validation predictions" - " for quantile 0.05" - ) -) - -# %% -plot_residuals_vs_predicted(cv_predictions_hgbr_50).interactive().properties( - title=("Residuals vs Predicted Values from cross-validation predictions for median") -) - -# %% -plot_residuals_vs_predicted(cv_predictions_hgbr_95).interactive().properties( - title=( - "Residuals vs Predicted Values from cross-validation predictions" - " for quantile 0.95" - ) -) - -# %% [markdown] -# -# We observe an expected behaviour: the residuals are centered and symmetric around 0 -# for the median model while not centered and biased for the 5th and 95th percentiles -# models. -# -# Note that we could obtain similar plots using scikit-learn's `PredictionErrorDisplay`. -# This display allows to also plot the observed values vs predicted values as well. - -# %% -cv_predictions_hgbr_05_concat = pl.concat(cv_predictions_hgbr_05, how="vertical") -cv_predictions_hgbr_50_concat = pl.concat(cv_predictions_hgbr_50, how="vertical") -cv_predictions_hgbr_95_concat = pl.concat(cv_predictions_hgbr_95, how="vertical") - -# %% -import matplotlib.pyplot as plt -from sklearn.metrics import PredictionErrorDisplay - - -for kind in ["actual_vs_predicted", "residual_vs_predicted"]: - fig, axs = plt.subplots(1, 3, figsize=(15, 5), sharey=True) - - PredictionErrorDisplay.from_predictions( - y_true=cv_predictions_hgbr_05_concat["load_mw"].to_numpy(), - y_pred=cv_predictions_hgbr_05_concat["predicted_load_mw"].to_numpy(), - kind=kind, - ax=axs[0], - ) - axs[0].set_title("0.05 quantile regression") - - PredictionErrorDisplay.from_predictions( - y_true=cv_predictions_hgbr_50_concat["load_mw"].to_numpy(), - y_pred=cv_predictions_hgbr_50_concat["predicted_load_mw"].to_numpy(), - kind=kind, - ax=axs[1], - ) - axs[1].set_title("Median regression") - - PredictionErrorDisplay.from_predictions( - y_true=cv_predictions_hgbr_95_concat["load_mw"].to_numpy(), - y_pred=cv_predictions_hgbr_95_concat["predicted_load_mw"].to_numpy(), - kind=kind, - ax=axs[2], - ) - axs[2].set_title("0.95 quantile regression") - - fig.suptitle(f"{kind} for GBRT minimzing different quantile losses") - -# %% [markdown] -# -# Those plots carry the same information than the previous ones. -# -# Now, we assess if the actual coverage of the models is close to the target coverage of -# 90%. In addition, we compute the average width of the bands. - - -# %% -def coverage(y_true, y_quantile_low, y_quantile_high): - y_true = np.asarray(y_true) - y_quantile_low = np.asarray(y_quantile_low) - y_quantile_high = np.asarray(y_quantile_high) - return float( - np.logical_and(y_true >= y_quantile_low, y_true <= y_quantile_high) - .mean() - .round(4) - ) - - -def mean_width(y_true, y_quantile_low, y_quantile_high): - y_true = np.asarray(y_true) - y_quantile_low = np.asarray(y_quantile_low) - y_quantile_high = np.asarray(y_quantile_high) - return float(np.abs(y_quantile_high - y_quantile_low).mean().round(1)) - - -# %% -coverage( - cv_predictions_hgbr_50_concat["load_mw"].to_numpy(), - cv_predictions_hgbr_05_concat["predicted_load_mw"].to_numpy(), - cv_predictions_hgbr_95_concat["predicted_load_mw"].to_numpy(), -) - -# %% [markdown] -# -# We see that the obtained coverage (~77%) on the cross-validated predictions is much -# lower than the target coverage of 90%. It means that the pair of regressors is not -# jointly calibrated to estimate the 90% interval. - -# %% -mean_width( - cv_predictions_hgbr_50_concat["load_mw"].to_numpy(), - cv_predictions_hgbr_05_concat["predicted_load_mw"].to_numpy(), - cv_predictions_hgbr_95_concat["predicted_load_mw"].to_numpy(), -) - -# %% [markdown] -# -# In terms of interpretable measure, the mean width provides a measure in the original -# unit of the target variable in MW that is ~5,100 MW. -# -# We can go a bit further and bin the cross-validated predictions and check if some -# specific bins show a better or worse coverage. - -# %% -binned_coverage_results = binned_coverage( - [df["load_mw"].to_numpy() for df in cv_predictions_hgbr_50], - [df["predicted_load_mw"].to_numpy() for df in cv_predictions_hgbr_05], - [df["predicted_load_mw"].to_numpy() for df in cv_predictions_hgbr_95], - n_bins=10, -) -binned_coverage_results - -# %% [markdown] -# -# Let's make a plot to check those data visually. - -# %% -coverage_by_bin = binned_coverage_results.copy() -coverage_by_bin["bin_label"] = coverage_by_bin.apply( - lambda row: f"[{row.bin_left:.0f}, {row.bin_right:.0f}]", axis=1 -) - -# %% -ax = coverage_by_bin.boxplot(column="coverage", by="bin_label", whis=1000) -ax.axhline(y=0.9, color="red", linestyle="--", label="Target coverage (0.9)") -ax.set( - xlabel="Load bins (MW)", - ylabel="Coverage", - title="Coverage Distribution by Load Bins", -) -ax.set_title("Coverage Distribution by Load Bins") -ax.legend() -plt.suptitle("") # Remove automatic suptitle from boxplot -_ = plt.xticks(rotation=45) - -# %% [markdown] -# -# We observe that the lower and higher bins, so low and high load, have the worse -# coverage with a high variability. -# -# ### Reliability diagrams and Lorenz curves for quantile regression - -# %% -plot_reliability_diagram( - cv_predictions_hgbr_50, kind="quantile", quantile_level=0.50 -).interactive().properties( - title="Reliability diagram for quantile 0.50 from cross-validation predictions" -) - -# %% -plot_reliability_diagram( - cv_predictions_hgbr_05, kind="quantile", quantile_level=0.05 -).interactive().properties( - title="Reliability diagram for quantile 0.05 from cross-validation predictions" -) - -# %% -plot_reliability_diagram( - cv_predictions_hgbr_95, kind="quantile", quantile_level=0.95 -).interactive().properties( - title="Reliability diagram for quantile 0.95 from cross-validation predictions" -) - -# %% -plot_lorenz_curve(cv_predictions_hgbr_50).interactive().properties( - title="Lorenz curve for quantile 0.50 from cross-validation predictions" -) - -# %% -plot_lorenz_curve(cv_predictions_hgbr_05).interactive().properties( - title="Lorenz curve for quantile 0.05 from cross-validation predictions" -) - -# %% -plot_lorenz_curve(cv_predictions_hgbr_95).interactive().properties( - title="Lorenz curve for quantile 0.95 from cross-validation predictions" -) - - -# %% [markdown] -# -# ## Quantile regression as classification -# -# In the following, we turn a quantile regression problem for all possible -# quantile levels into a multiclass classification problem by discretizing the -# target variable into bins and interpolating the cumulative sum of the bin -# membership probability to estimate the CDF of the distribution of the -# continuous target variable conditioned on the features. -# -# Ideally, the classifier should be efficient when trained on a large number of -# classes (induced by the number of bins). Therefore we use a Random Forest -# classifier as the default base estimator. -# -# There are several advantages to this approach: -# - a single model is trained and can jointly estimate quantiles for all -# quantile levels (assuming a well tuned number of bins); -# - the quantile levels can be chosen at prediction time, which allows for a -# flexible quantile regression model; -# - in practice, the resulting predictions are often reasonably well calibrated -# as we will see in the reliability diagrams below. -# -# One possible drawback is that current implementations of gradient boosting -# models tend to be very slow to train with a large number of classes. Random -# Forests are much more efficient in this case, but they do not always provide -# the best predictive performance. It could be the case that combining this -# approach with tabular neural networks can lead to competitive results. -# -# However, the current scikit-learn API is not expressive enough to to handle -# the output shape of the quantile prediction function. We therefore cannot -# make it fit into a skrub pipeline. - -# %% -from scipy.interpolate import interp1d -from sklearn.base import BaseEstimator, RegressorMixin, clone -from sklearn.utils.validation import check_is_fitted -from sklearn.ensemble import RandomForestClassifier -from sklearn.preprocessing import KBinsDiscretizer -from sklearn.utils.validation import check_consistent_length -from sklearn.utils import check_random_state -import numpy as np - - -class BinnedQuantileRegressor(BaseEstimator, RegressorMixin): - def __init__( - self, - estimator=None, - n_bins=100, - quantile=0.5, - random_state=None, - ): - self.n_bins = n_bins - self.estimator = estimator - self.quantile = quantile - self.random_state = random_state - - def fit(self, X, y): - # Lightweight input validation: most of the input validation will be - # handled by the sub estimators. - random_state = check_random_state(self.random_state) - check_consistent_length(X, y) - self.target_binner_ = KBinsDiscretizer( - n_bins=self.n_bins, - strategy="quantile", - subsample=200_000, - encode="ordinal", - quantile_method="averaged_inverted_cdf", - random_state=random_state, - ) - - y_binned = ( - self.target_binner_.fit_transform(np.asarray(y).reshape(-1, 1)) - .ravel() - .astype(np.int32) - ) - - # Fit the multiclass classifier to predict the binned targets from the - # training set. - if self.estimator is None: - estimator = RandomForestClassifier(random_state=random_state) - else: - estimator = clone(self.estimator) - self.estimator_ = estimator.fit(X, y_binned) - return self - - def predict_quantiles(self, X, quantiles=(0.05, 0.5, 0.95)): - check_is_fitted(self, "estimator_") - edges = self.target_binner_.bin_edges_[0] - n_bins = edges.shape[0] - 1 - expected_shape = (X.shape[0], n_bins) - y_proba_raw = self.estimator_.predict_proba(X) - - # Some might stay empty on the training set. Typically, classifiers do - # not learn to predict an explicit 0 probability for unobserved classes - # so we have to post process their output: - if y_proba_raw.shape != expected_shape: - y_proba = np.zeros(shape=expected_shape) - y_proba[:, self.estimator_.classes_] = y_proba_raw - else: - y_proba = y_proba_raw - - # Build the mapper for inverse CDF mapping, from cumulated - # probabilities to continuous prediction. - y_cdf = np.zeros(shape=(X.shape[0], edges.shape[0])) - y_cdf[:, 1:] = np.cumsum(y_proba, axis=1) - return np.asarray([interp1d(y_cdf_i, edges)(quantiles) for y_cdf_i in y_cdf]) - - def predict(self, X): - return self.predict_quantiles(X, quantiles=(self.quantile,)).ravel() - - -# %% -quantiles = (0.05, 0.5, 0.95) -bqr = BinnedQuantileRegressor( - RandomForestClassifier( - n_estimators=300, - min_samples_leaf=5, - max_features=0.2, - n_jobs=-1, - random_state=0, - ), - n_bins=30, -) -bqr - -# %% -from sklearn.model_selection import cross_validate - -X, y = features.skb.eval(), target.skb.eval() - -cv_results_bqr = cross_validate( - bqr, - X, - y, - cv=ts_cv_5, - scoring={ - "d2_pinball_50": make_scorer(d2_pinball_score, alpha=0.5), - }, - return_estimator=True, - return_indices=True, - verbose=1, - n_jobs=-1, -) - -# %% -cv_predictions_bqr_all = [ - cv_predictions_bqr_05 := [], - cv_predictions_bqr_50 := [], - cv_predictions_bqr_95 := [], -] -for fold_ix, (qreg, test_idx) in enumerate( - zip(cv_results_bqr["estimator"], cv_results_bqr["indices"]["test"]) -): - print(f"CV iteration #{fold_ix}") - print(f"Test set size: {test_idx.shape[0]} rows") - print( - f"Test time range: {prediction_time.skb.eval()[test_idx][0, 0]} to " - f"{prediction_time.skb.eval()[test_idx][-1, 0]} " - ) - y_pred_all_quantiles = qreg.predict_quantiles(X[test_idx], quantiles=quantiles) - - coverage_score = coverage( - y[test_idx], - y_pred_all_quantiles[:, 0], - y_pred_all_quantiles[:, 2], - ) - print(f"Coverage: {coverage_score:.3f}") - - mean_width_score = mean_width( - y[test_idx], - y_pred_all_quantiles[:, 0], - y_pred_all_quantiles[:, 2], - ) - print(f"Mean prediction interval width: " f"{mean_width_score:.1f} MW") - - for q_idx, (quantile, predictions) in enumerate( - zip(quantiles, cv_predictions_bqr_all) - ): - observed = y[test_idx] - predicted = y_pred_all_quantiles[:, q_idx] - predictions.append( - pl.DataFrame( - { - "prediction_time": prediction_time.skb.eval()[test_idx], - "load_mw": observed, - "predicted_load_mw": predicted, - } - ) - ) - print(f"d2_pinball score: {d2_pinball_score(observed, predicted):.3f}") - print() - -# %% [markdown -# Let's assess the calibration of the quantile regression model: - -# %% -plot_reliability_diagram( - cv_predictions_bqr_50, kind="quantile", quantile_level=0.50 -).interactive().properties( - title="Reliability diagram for quantile 0.50 from cross-validation predictions" -) - -# %% -plot_reliability_diagram( - cv_predictions_bqr_05, kind="quantile", quantile_level=0.05 -).interactive().properties( - title="Reliability diagram for quantile 0.05 from cross-validation predictions" -) - -# %% -plot_reliability_diagram( - cv_predictions_bqr_95, kind="quantile", quantile_level=0.95 -).interactive().properties( - title="Reliability diagram for quantile 0.95 from cross-validation predictions" -) - -# %% [markdown] -# -# We can complement this assessment with the Lorenz curves, which only assess -# the ranking power of the predictions, irrespective of their absolute values. - -# %% -plot_lorenz_curve(cv_predictions_bqr_50).interactive().properties( - title="Lorenz curve for quantile 0.50 from cross-validation predictions" -) - -# %% -plot_lorenz_curve(cv_predictions_bqr_05).interactive().properties( - title="Lorenz curve for quantile 0.05 from cross-validation predictions" -) - -# %% -plot_lorenz_curve(cv_predictions_bqr_95).interactive().properties( - title="Lorenz curve for quantile 0.95 from cross-validation predictions" -) From f541c99519e668b3bb3acf0204343afb583081f0 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Fri, 1 Aug 2025 14:50:46 +0200 Subject: [PATCH 02/11] env --- content/python_files/feature_engineering.py | 20 ++++- environment.yml | 4 +- pixi.lock | 86 ++++++++------------- pixi.toml | 2 +- 4 files changed, 55 insertions(+), 57 deletions(-) diff --git a/content/python_files/feature_engineering.py b/content/python_files/feature_engineering.py index abeb7e9..d221fbe 100644 --- a/content/python_files/feature_engineering.py +++ b/content/python_files/feature_engineering.py @@ -209,8 +209,24 @@ def prepare_french_calendar_data(time): add_weekday=True, add_day_of_year=True, add_total_seconds=False ) -calendar = prepare_french_calendar_data(time) -calendar +def prepare_holidays(time): + fr_time = pl.col("time").dt.convert_time_zone("Europe/Paris") + fr_year_min = time.select(fr_time.dt.year().min()).item() + fr_year_max = time.select(fr_time.dt.year().max()).item() + holidays_fr = holidays.country_holidays( + "FR", years=range(fr_year_min, fr_year_max + 1) + ) + return time.with_columns( + fr_time.dt.date().is_in(holidays_fr.keys()).alias("cal_is_holiday"), + + ) + + +time_encoded = time.rename({"time": "cal"}).skb.apply(datetime_encoder) + +calendar = prepare_holidays(time) +# calendar = prepare_french_calendar_data(time) +# calendar # %% [markdown] diff --git a/environment.yml b/environment.yml index 3adb682..301dbeb 100644 --- a/environment.yml +++ b/environment.yml @@ -20,8 +20,8 @@ dependencies: - u8darts >=0.36.0,<0.37 - setuptools <81 - plotly >=6.2.0,<7 + - skrub ==0.6.0 - pip: - openmeteo-requests >=1.5.0, <2 - requests-cache >=1.2.1, <2 - - retry-requests >=2.0.0, <3 - - git+https://github.com/skrub-data/skrub@771f3870a23438211faefb0e54132781256acc0a \ No newline at end of file + - retry-requests >=2.0.0, <3 \ No newline at end of file diff --git a/pixi.lock b/pixi.lock index a2458e0..87c0e9b 100644 --- a/pixi.lock +++ b/pixi.lock @@ -287,6 +287,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-80.9.0-pyhff2d567_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/shap-0.48.0-cpu_py312hc6e6151_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/skrub-0.6.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/slicer-0.0.8-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.1-h8bd8927_1.conda - 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- requests>=2.25.0 +- conda: https://conda.anaconda.org/conda-forge/noarch/skrub-0.6.0-pyhd8ed1ab_0.conda + sha256: 464c25b308c988d416b2b6183470ae68ec326228dfa791a6333369cd18f42d9d + md5: 3c727218efc120d5f7503c426e0cef2b + depends: + - jinja2 >=3.1.2 + - matplotlib-base >=3.4.3 + - numpy >=1.23.5 + - pandas >=1.5.3 - pydot - - ipykernel ; extra == 'dev' - - ipython ; extra == 'dev' - - jupyterlab ; extra == 'dev' - - jupyterlite-sphinx ; extra == 'dev' - - jupyterlite-pyodide-kernel ; extra == 'dev' - - numpydoc ; extra == 'dev' - - pydata-sphinx-theme ; extra == 'dev' - - seaborn ; extra == 'dev' - - sphinx ; extra == 'dev' - - sphinx-copybutton ; extra == 'dev' - - sphinx-gallery ; extra == 'dev' - - sphinxext-opengraph ; extra == 'dev' - - sphinx-autosummary-accessors ; extra == 'dev' - - statsmodels ; extra == 'dev' - - black==23.3.0 ; extra == 'dev' - - ruff==0.4.8 ; extra == 'dev' - - pre-commit ; extra == 'dev' - - pytest ; extra == 'dev' - - pytest-cov ; extra == 'dev' - - pytest-xdist ; extra == 'dev' - - pyarrow ; extra == 'dev' - - polars ; extra == 'dev' - - plotly ; extra == 'dev' - - sentence-transformers ; extra == 'transformers' - requires_python: '>=3.9' + - python >=3.9 + - requests >=2.25.0 + - scikit-learn >=1.2.1 + - scipy >=1.9.3 + license: BSD-3-Clause + license_family: BSD + purls: + - pkg:pypi/skrub?source=hash-mapping + size: 339154 + timestamp: 1753356996648 - conda: https://conda.anaconda.org/conda-forge/noarch/slicer-0.0.8-pyhd8ed1ab_0.conda sha256: 915b33075b7aa0c9f339038e2747394e949c34035f74104530a643f895ace621 md5: f6f75ceccf92ddb21665b03cfbfa7800 diff --git a/pixi.toml b/pixi.toml index 8973612..ab52fb4 100644 --- a/pixi.toml +++ b/pixi.toml @@ -28,9 +28,9 @@ u8darts = ">=0.36.0,<0.37" setuptools = "<81" plotly = ">=6.2.0,<7" cloudpickle = ">=3.0.0,<3.1" +skrub = "==0.6.0" [pypi-dependencies] -skrub = { git = "https://github.com/skrub-data/skrub", rev = "771f3870a23438211faefb0e54132781256acc0a" } openmeteo-requests = ">=1.5.0, <2" requests-cache = ">=1.2.1, <2" retry-requests = ">=2.0.0, <3" From 462a8d102af0568759e57a92a8b1bdbfe7ccc063 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Fri, 1 Aug 2025 15:05:59 +0200 Subject: [PATCH 03/11] cutting out a lot of detail --- content/python_files/feature_engineering.py | 136 +------------------- 1 file changed, 6 insertions(+), 130 deletions(-) diff --git a/content/python_files/feature_engineering.py b/content/python_files/feature_engineering.py index d221fbe..5aa5d4f 100644 --- a/content/python_files/feature_engineering.py +++ b/content/python_files/feature_engineering.py @@ -209,6 +209,7 @@ def prepare_french_calendar_data(time): add_weekday=True, add_day_of_year=True, add_total_seconds=False ) +@skrub.deferred def prepare_holidays(time): fr_time = pl.col("time").dt.convert_time_zone("Europe/Paris") fr_year_min = time.select(fr_time.dt.year().min()).item() @@ -216,7 +217,7 @@ def prepare_holidays(time): holidays_fr = holidays.country_holidays( "FR", years=range(fr_year_min, fr_year_max + 1) ) - return time.with_columns( + return time.select( fr_time.dt.date().is_in(holidays_fr.keys()).alias("cal_is_holiday"), ) @@ -224,9 +225,9 @@ def prepare_holidays(time): time_encoded = time.rename({"time": "cal"}).skb.apply(datetime_encoder) -calendar = prepare_holidays(time) +calendar = time.skb.concat([time_encoded, prepare_holidays(time)], axis=1) # calendar = prepare_french_calendar_data(time) -# calendar +calendar # %% [markdown] @@ -296,18 +297,6 @@ def load_electricity_load_data(time, data_source_folder): & (pl.col("time") < pl.datetime(2021, 10, 31, hour=10, time_zone="UTC")) ).skb.eval().plot.line(x="time:T", y="load_mw:Q") -# %% [markdown] -# -# **Remark**: interpolating missing values in the target column that we will -# use to train and evaluate our models can bias the learning problem and make -# our cross-validation metrics misrepresent the performance of the deployed -# predictive system. -# -# A potentially better approach would be to keep the missing values in the -# dataset and use a sample_weight mask to keep a contiguous dataset while -# ignoring the time periods with missing values when training or evaluating the -# model. - # %% [markdown] # # ## Lagged features @@ -361,115 +350,6 @@ def iqr(col, *, window_size: int): as_=["key", "load_mw"], ).mark_line(tooltip=True).encode(x="time:T", y="load_mw:Q", color="key:N").interactive() -# %% [markdown] -# -# ## Important remark about lagged features engineering and system lag -# -# When working with historical data, we often have access to all the past -# measurements in the dataset. However, when we want to use the lagged features -# in a forecasting model, we need to be careful about the length of the -# **system lag**: the time between a timestamped measurement is made in the -# real world and the time the record is made available to the downstream -# application (in our case, a deployed predictive pipeline). -# -# System lag is rarely explicitly represented in the data sources even if such -# delay can be as large as several hours or even days and can sometimes be -# irregular. For instance, if there is a human intervention in the data -# recording process, holidays and weekends can punctually add significant -# delay. -# -# If the system lag is larger than the maximum feature engineering lag, the -# resulting features be filled with missing values once deployed. More -# importantly, if the system lag is not handled explicitly, those resulting -# missing values will only be present in the features computed for the -# deployed system but not present in the features computed to train and -# backtest the system before deployment. -# -# This structural discrepancy can severely degrade the performance of the -# deployed model compared to the performance estimated from backtesting on the -# historical data. -# -# We will set this problem aside for now but discuss it again in a later -# section of this tutorial. - -# %% [markdown] -# ## Investigating outliers in the lagged features -# -# Let's use the `skrub.TableReport` tool to look at the plots of the marginal -# distribution of the lagged features. - -# %% -from skrub import TableReport - -TableReport(electricity_lagged.skb.eval()) - -# %% [markdown] -# -# Let's extract the dates where the inter-quartile range of the load is -# greater than 15,000 MW. - -# %% -electricity_lagged.filter(pl.col("load_mw_iqr_7d") > 15_000)[ - "time" -].dt.date().unique().sort().to_list().skb.eval() - -# %% [markdown] -# -# We observe 3 date ranges with high inter-quartile range. Let's plot the -# electricity load and the lagged features for the first data range along with -# the weather data for Paris. - -# %% -altair.Chart( - electricity_lagged.filter( - (pl.col("time") > pl.datetime(2021, 12, 1, time_zone="UTC")) - & (pl.col("time") < pl.datetime(2021, 12, 31, time_zone="UTC")) - ).skb.eval() -).transform_fold( - [ - "load_mw", - "load_mw_iqr_7d", - ], -).mark_line( - tooltip=True -).encode( - x="time:T", y="value:Q", color="key:N" -).interactive() - -# %% -altair.Chart( - all_city_weather.filter( - (pl.col("time") > pl.datetime(2021, 12, 1, time_zone="UTC")) - & (pl.col("time") < pl.datetime(2021, 12, 31, time_zone="UTC")) - ).skb.eval() -).transform_fold( - [f"weather_temperature_2m_{city_name}" for city_name in city_names.skb.eval()], -).mark_line( - tooltip=True -).encode( - x="time:T", y="value:Q", color="key:N" -).interactive() - -# %% [markdown] -# -# Based on the plots above, we can see that the electricity load was high just -# before the Christmas holidays due to low temperatures. Then the load suddenly -# dropped because temperatures went higher right at the start of the -# end-of-year holidays. -# -# So those outliers do not seem to be caused to a data quality issue but rather -# due to a real change in the electricity load demand. We could conduct similar -# analysis for the other date ranges with high inter-quartile range but we will -# skip that for now. -# -# If we had observed significant data quality issues over extended periods of -# time could have been addressed by removing the corresponding rows from the -# dataset. However, this would make the lagged and windowing feature -# engineering challenging to reimplement correctly. A better approach would be -# to keep a contiguous dataset assign 0 weights to the affected rows when -# fitting or evaluating the trained models via the use of the `sample_weight` -# parameter. - # %% [markdown] # ## Final dataset # @@ -569,18 +449,14 @@ def build_features( def build_targets(prediction_time, electricity, horizons): return prediction_time.join( electricity.with_columns( - [ pl.col("load_mw") - .shift(-h) - .alias(target_column_name_pattern.format(horizon=h)) - for h in horizons - ] + .shift(-24) + .alias("load_mw_horizon_24h") ), left_on="prediction_time", right_on="time", ) - targets = build_targets(prediction_time, electricity, horizons) targets From bfee63bdf116f576bb0c234d5fb11754f7f205d5 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Sun, 10 Aug 2025 21:00:42 +0200 Subject: [PATCH 04/11] adding slides --- .gitignore | 5 + content/python_files/feature_engineering.py | 12 +- .../python_files/single_horizon_prediction.py | 31 +- content/slides/.gitignore | 1 + content/slides/_quarto.yml | 33 ++ content/slides/images/periodic_features.png | Bin 0 -> 244572 bytes content/slides/images/plot-parallel-coord.png | Bin 0 -> 225611 bytes content/slides/images/qr-code.png | Bin 0 -> 14169 bytes .../slides/images/skrub-table-vectorizer.png | Bin 0 -> 293206 bytes content/slides/images/skrub.svg | 79 ++++ content/slides/slides.qmd | 337 ++++++++++++++++++ content/slides/style.css | 7 + pixi.toml | 9 +- 13 files changed, 486 insertions(+), 28 deletions(-) create mode 100644 content/slides/.gitignore create mode 100644 content/slides/_quarto.yml create mode 100644 content/slides/images/periodic_features.png create mode 100644 content/slides/images/plot-parallel-coord.png create mode 100644 content/slides/images/qr-code.png create mode 100644 content/slides/images/skrub-table-vectorizer.png create mode 100644 content/slides/images/skrub.svg create mode 100644 content/slides/slides.qmd create mode 100644 content/slides/style.css diff --git a/.gitignore b/.gitignore index e757fa6..425f4f9 100644 --- a/.gitignore +++ b/.gitignore @@ -90,3 +90,8 @@ jupyterlite/.jupyterlite.doit.db # requests cache for weather data download .cache.sqlite + +# Slides files +content/slides/_freeze/ +content/slides/_site/ + diff --git a/content/python_files/feature_engineering.py b/content/python_files/feature_engineering.py index 5aa5d4f..299b9e1 100644 --- a/content/python_files/feature_engineering.py +++ b/content/python_files/feature_engineering.py @@ -441,12 +441,8 @@ def build_features( # to 24 hours. # %% -horizons = range(1, 25) -target_column_name_pattern = "load_mw_horizon_{horizon}h" - - @skrub.deferred -def build_targets(prediction_time, electricity, horizons): +def build_targets(prediction_time, electricity): return prediction_time.join( electricity.with_columns( pl.col("load_mw") @@ -457,7 +453,7 @@ def build_targets(prediction_time, electricity, horizons): right_on="time", ) -targets = build_targets(prediction_time, electricity, horizons) +targets = build_targets(prediction_time, electricity) targets # %% [markdown] @@ -474,8 +470,8 @@ def build_targets(prediction_time, electricity, horizons): "features": features, "targets": targets, "prediction_time": prediction_time, - "horizons": horizons, - "target_column_name_pattern": target_column_name_pattern, }, f, ) + +# %% diff --git a/content/python_files/single_horizon_prediction.py b/content/python_files/single_horizon_prediction.py index 4c89c01..910cca4 100644 --- a/content/python_files/single_horizon_prediction.py +++ b/content/python_files/single_horizon_prediction.py @@ -44,16 +44,13 @@ features = feature_engineering_pipeline["features"] targets = feature_engineering_pipeline["targets"] prediction_time = feature_engineering_pipeline["prediction_time"] -horizons = feature_engineering_pipeline["horizons"] -target_column_name_pattern = feature_engineering_pipeline["target_column_name_pattern"] # %% [markdown] # # For now, let's focus on the last horizon (24 hours) to train a model # predicting the electricity load at the next 24 hours. # %% -horizon_of_interest = horizons[-1] # Focus on the 24-hour horizon -target_column_name = target_column_name_pattern.format(horizon=horizon_of_interest) +target_column_name = "load_mw_horizon_24h" predicted_target_column_name = "predicted_" + target_column_name target = targets[target_column_name].skb.mark_as_y() target @@ -79,23 +76,23 @@ cols=skrub.choose_from( { "none": s.glob(""), # No column has an empty name. - "load": s.glob("load_*"), - "rolling_load": s.glob("load_mw_rolling_*"), - "weather": s.glob("weather_*"), - "temperature": s.glob("weather_temperature_*"), - "moisture": s.glob("weather_moisture_*"), - "cloud_cover": s.glob("weather_cloud_cover_*"), - "calendar": s.glob("cal_*"), - "holiday": s.glob("cal_is_holiday*"), - "future_1h": s.glob("*_future_1h"), - "future_24h": s.glob("*_future_24h"), - "non_paris_weather": s.glob("weather_*") & ~s.glob("weather_*_paris_*"), + # "load": s.glob("load_*"), + # "rolling_load": s.glob("load_mw_rolling_*"), + # "weather": s.glob("weather_*"), + # "temperature": s.glob("weather_temperature_*"), + # "moisture": s.glob("weather_moisture_*"), + # "cloud_cover": s.glob("weather_cloud_cover_*"), + # "calendar": s.glob("cal_*"), + # "holiday": s.glob("cal_is_holiday*"), + # "future_1h": s.glob("*_future_1h"), + # "future_24h": s.glob("*_future_24h"), + # "non_paris_weather": s.glob("weather_*") & ~s.glob("weather_*_paris_*"), }, name="dropped_cols", ) ) ) - +# %% hgbr_predictions = features_with_dropped_cols.skb.apply( HistGradientBoostingRegressor( random_state=0, @@ -111,7 +108,7 @@ ) hgbr_predictions -# %% [markdown] +horizon_of_interest = horizons[-1] # Focus on the 24-hour horizon# %% [markdown] # # The `predictions` expression captures the whole expression graph that # includes the feature engineering steps, the target variable, and the model diff --git a/content/slides/.gitignore b/content/slides/.gitignore new file mode 100644 index 0000000..075b254 --- /dev/null +++ b/content/slides/.gitignore @@ -0,0 +1 @@ +/.quarto/ diff --git a/content/slides/_quarto.yml b/content/slides/_quarto.yml new file mode 100644 index 0000000..c086c3e --- /dev/null +++ b/content/slides/_quarto.yml @@ -0,0 +1,33 @@ +project: + type: website + # render: + # - "!src/" + +website: + title: "Skrub learning materials" + favicon: "images/skrub.svg" + navbar: + left: + # - href: index.qmd + # text: Home + - href: pages/slides/index_slides.qmd + text: Slides + - href: pages/notebooks/index_notebooks.qmd + text: Notebooks + - href: https://skrub-data.org/stable/reference/index.html + text: API docs + - href: https://skrub-data.org/stable/auto_examples/index.html + text: Skrub Examples + - href: pages/listings.qmd + text: Latest updates +execute: + freeze: auto + +format: + html: + theme: united + css: styles.css + toc: true + + + diff --git a/content/slides/images/periodic_features.png b/content/slides/images/periodic_features.png new file mode 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--- /dev/null +++ b/content/slides/slides.qmd @@ -0,0 +1,337 @@ +--- +title: "Tutorial: Skrub" +title-block-banner: true +date: 2025-08-18 +subtitle: "Machine Learning with Dataframes" +author: "Riccardo Cappuzzo, Guillaume Lemaitre" +format: + revealjs: + slide-number: c/t + show-slide-number: all + preview-links: auto + embed-resources: false + transition: slide + theme: [simple] + logo: images/skrub.svg + css: style.css +incremental: true + +--- +# Program for the tutorial + +1. Short introduction to `skrub` +2. Simple `skrub` transformers +3. Starting with the `skrub` DataOps +4. The tutorial! **Forecasting with `skrub` DataOps and Polars** + +# Boost your productivity with `skrub`! {auto-animate="true"} + +`skrub` simplifies many tedious data preparation operations + +## An example pipeline +1. Gather some data +2. Explore the data +3. Pre-process the data +4. Perform feature engineering +5. Build a scikit-learn pipeline +6. ??? +7. Profit? + +## Exploring the data with `skrub` {.smaller auto-animate="true"} + +```{.python} +from skrub import TableReport +TableReport(employee_salaries) +``` +[Preview](https://skrub-data.org/skrub-reports/examples/employee_salaries.html){preview-link="true"} + + +::: {.fragment} +::: {.nonincremental} +Main features: + +- Obtain high-level statistics about the data +- Explore the distribution of values and find outliers +- Discover highly correlated columns +- Export and share the report as an `html` file +::: +::: + +## Lightweight data cleaning: `Cleaner` {.smaller auto-animate="true"} + +```{python} +#| echo: true +from skrub.datasets import fetch_employee_salaries +from pprint import pprint +import pandas as pd + +dataset = fetch_employee_salaries() +employees, salaries = dataset.X, dataset.y + +df = pd.DataFrame(employees) +``` + +## Lightweight data cleaning: `Cleaner` {.smaller auto-animate="true"} + +```{python} +#| echo: true +from skrub import Cleaner +cleaner = Cleaner(drop_if_constant=True, datetime_format='%d/%m/%Y') +df_cleaned = cleaner.fit_transform(df) +display(df_cleaned) +``` + +## Encoding datetime features `skrub.DatetimeEncoder` {auto-animate="true" .smaller} +```{python} +#| echo: true +from skrub import DatetimeEncoder, ToDatetime +import pandas as pd +import numpy as np + +data = { + 'Constant int': [1, 1, 1], # Single unique value + 'B': [2, 3, 2], # Multiple unique values + 'Constant str': ['x', 'x', 'x'], # Single unique value + 'D': [4, 5, 6], # Multiple unique values + 'All nan': [np.nan, np.nan, np.nan], # All missing values + 'All empty': ['', '', ''], # All empty strings + 'Date': ['01/01/2023', '02/01/2023', '03/01/2023'], +} + +df = pd.DataFrame(data) +de = DatetimeEncoder(periodic_encoding="circular") +X_date = ToDatetime().fit_transform(df["Date"]) +X_enc = de.fit_transform(X_date) +print(X_enc) +``` + +## What periodic features look like +![](images/periodic_features.png){fig-align="center"} + + +## Encoding _all the features_ with `skrub.TableVectorizer` {.smaller auto-animate="true"} + +![](images/skrub-table-vectorizer.png) + +## Build a predictive pipeline {auto-animate="true" visibility="uncounted"} +```{.python } +from sklearn.linear_model import Ridge +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler, OneHotEncoder +from sklearn.impute import SimpleImputer +from sklearn.compose import make_column_selector as selector +from sklearn.compose import make_column_transformer + +categorical_columns = selector(dtype_include=object)(employees) +numerical_columns = selector(dtype_exclude=object)(employees) + +ct = make_column_transformer( + (StandardScaler(), + numerical_columns), + (OneHotEncoder(handle_unknown="ignore"), + categorical_columns)) + +model = make_pipeline(ct, SimpleImputer(), Ridge()) +``` +## Build a predictive pipeline with `tabular_pipeline` {auto-animate="true" .smaller} +```{python} +#| echo: true +import skrub +from sklearn.linear_model import Ridge +model = skrub.tabular_pipeline(Ridge()) +model +``` + +## We now have a pipeline! {.smaller} + +1. Gather some data + - `skrub.datasets`, or user data +2. Explore the data + - `skrub.TableReport` +3. Pre-process the data + - `skrub.TableVectorizer`, `Cleaner`, ... +4. Perform feature engineering + - `DatetimeEncoder`, `TextEncoder`, `StringEncoder `... +5. Build a scikit-learn pipeline + - `tabular_pipeline`, `sklearn.pipeline.make_pipeline` ... +6. ??? +7. Profit 📈 + + +# What if we had a *better* pipeline? + +## A realistic scenario +A data scientist needs to train a ML model, but features are spread across +multiple tables. + +::: {.fragment} +::: {.callout-warning} +Many issues with this! +::: + +::: + +::: {.incremental} +- `scikit-learn` pipelines support only a single feature matrix `X` +- Dataframe operations cannot be tuned +- Data leakage must be accounted for +- Persisting and reproducing operations is complex +::: + +## `skrub` DataOps +When a normal pipe is not enough... + +::: {.fragment style="font-size:2em;"} +... the `skrub` DataOps come to the rescue 🚒 +::: + +## DataOps... +- Extend the `scikit-learn` machinery to complex multi-table operations +- Track all operations with a computational graph +- Can tune any operation in the data plan +- Can be persisted and shared easily by generating a `learner` + +## DataOps, DataOps plans, `learner`s: oh my! +- A `DataOp` (singular) wraps a single operation, and can be combined and concatenated with other `DataOps`. + +- We refer to a sequence and combination of `DataOps` as a `DataOps` plan. + +- The `DataOps` plan can be exported as a standalone object called `learner`. The `learner` takes a dictionary of values rather than just `X` and `y`. + + +## How do DataOps work, though? +DataOps **wrap** around *user operations*, where user operations are: + +- any dataframe operation (e.g., merge, group by, aggregate etc.) +- scikit-learn estimators (a Random Forest, RidgeCV etc.) +- custom user code (load data from a path, fetch from an URL etc.) + +::: {.fragment} + +::: {.callout-important} +DataOps _record_ user operations, so that they can later be _replayed_ in the same +order and with the same arguments on unseen data. +::: +::: + + +## Starting with the `DataOps` + +```{python} +#| echo: true +data = skrub.datasets.fetch_credit_fraud() + +baskets = skrub.var("baskets", data.baskets) +X = baskets[["ID"]].skb.mark_as_X() +y = baskets["fraud_flag"].skb.mark_as_y() + +products = skrub.var("products", data.products) # add a new variable +``` + +:::{.incremental} +- `X`, `y`, `products` represent inputs to the pipeline. +- `skrub` splits `X` and `y` when training. +::: + +## Building a full data plan +```{.python} +from skrub import selectors as s +from sklearn.ensemble import ExtraTreesClassifier + +vectorizer = skrub.TableVectorizer(high_cardinality=skrub.StringEncoder(), n_jobs=-1) + +vectorized_products = products.skb.apply(vectorizer, cols=s.all() - "basket_ID") +aggregated_products = vectorized_products.groupby("basket_ID").agg("mean").reset_index() + +features = X.merge(aggregated_products, left_on="ID", right_on="basket_ID") +features = features.drop(columns=["ID", "basket_ID"]) + +predictions = features.skb.apply(ExtraTreesClassifier(n_jobs=-1), y=y) +``` + +## Inspecting the data plan +```{.python} +predictions.skb.full_report() +``` + +
+ +report + +## Exporting the plan in a `learner` {.smaller} +The data plan can be exported as a `learner`: +```{.python} +# anywhere +learner = predictions.skb.make_learner() +# search is a HPO object +best_learner = search.skb.best_learner_ +``` +::: {.fragment} +Then, the `learner` can be pickled ... +```{.python} +import pickle + +with open("learner.bin", "wb") as fp: + pickle.dump(learner, fp) +``` +::: + +::: {.fragment} + +... and loaded + +```{.python} +with open("learner.bin", "rb") as fp: + learner = pickle.load(fp) + +learner.predict({"baskets": new_baskets, "products": new_products}) +``` +::: + + +## Hyperparameter tuning in a Data Plan +`skrub` implements four `choose_*` functions (and `optional`): + +- `choose_from`: select from the given list of options +- `choose_int`: select an integer within a range +- `choose_float`: select a float within a range +- `choose_bool`: select a bool +- `optional`: chooses between a value or DataOp and no op + + +## Hyperparameter tuning in a Data Plan {auto-animate="true"} +It's possible to nest these functions to create complex grids: +```python +X.skb.apply( + skrub.choose_from( + { + "PCA": PCA(n_components=skrub.choose_int(10, 30)), + "SelectKBest": SelectKBest(k=skrub.choose_int(10, 30)) + }, name="dim_reduction" + ) +) +``` + +## Observe the impact of the hyperparameters {auto-animate="true"} + +```{.python} +search = pred.skb.get_randomized_search(scoring="roc_auc", fitted=True) + +search.plot_parallel_coord() +``` + +![](images/plot-parallel-coord.png){fig-align="center"} + + +## Time for the tutorial ... + +## Getting involved +::: {.nonincremental} +- [Skrub website](https://skrub-data.org/stable/) (QR code below!) +- [Skrub materials website](https://skrub-data.org/skrub-materials/index.html) +- [Git repository](https://github.com/skrub-data/skrub/) +- [Discord server](https://discord.gg/ABaPnm7fDC) +- [Bluesky](https://bsky.app/profile/skrub-data.bsky.social) +::: + +![](images/qr-code.png){.absolute bottom=0 right=0 width="250" height="250"} diff --git a/content/slides/style.css b/content/slides/style.css new file mode 100644 index 0000000..b395eb0 --- /dev/null +++ b/content/slides/style.css @@ -0,0 +1,7 @@ +h1.title { + background-image: url(images/skrub.svg); + background-repeat: no-repeat; + background-position: left; + background-size: 200px; + min-height: 200px; /* Set the minimum height to match the background image height */ +} \ No newline at end of file diff --git a/pixi.toml b/pixi.toml index ab52fb4..84033bc 100644 --- a/pixi.toml +++ b/pixi.toml @@ -1,8 +1,10 @@ [project] -name = "forecasting" +name = "Euroscipy 2025 - Skrub tutorial" version = "0.1.0" -description = "Forecasting" -authors = ["Guillaume Lemaitre ", "Olivier Grisel "] +description = "Skrub tutorial for Euroscipy 2025." +authors = ["Guillaume Lemaitre ", +"Olivier Grisel ", +"Riccardo Cappuzzo "] channels = ["conda-forge"] platforms = ["win-64", "linux-64", "osx-64", "osx-arm64"] @@ -59,6 +61,7 @@ convert-to-executed-notebooks = { cmd = "jupytext --to notebook --execute ./cont build-book = { cmd = "jupyter-book build book", depends-on = ["convert-to-notebooks", "copy-tutorial-helpers", "copy-parallel-coordinates-plots"] } build-jupyterlite = { cmd = "jupyter lite build --contents content --output-dir dist", cwd = "jupyterlite", depends-on = ["convert-to-notebooks"] } serve-jupyterlite = { cmd = "python -m http.server", cwd = "jupyterlite/dist", depends-on = ["build-jupyterlite"] } +build-slides = { cmd = "quarto render", cwd = "content/slides", depends-on = ["convert-to-notebooks"] } [environments] lint = ["lint"] From 6aa3a279bd112a201d7acd602f275071c0830e0d Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Sun, 10 Aug 2025 21:05:14 +0200 Subject: [PATCH 05/11] _ --- content/slides/slides.qmd | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/slides/slides.qmd b/content/slides/slides.qmd index 479feb4..675eaf1 100644 --- a/content/slides/slides.qmd +++ b/content/slides/slides.qmd @@ -1,8 +1,8 @@ --- -title: "Tutorial: Skrub" +title: "Skrub: Machine Learning with Dataframes" title-block-banner: true date: 2025-08-18 -subtitle: "Machine Learning with Dataframes" +subtitle: "Tutorial at EuroSciPy 2025" author: "Riccardo Cappuzzo, Guillaume Lemaitre" format: revealjs: @@ -323,9 +323,7 @@ search.plot_parallel_coord() ![](images/plot-parallel-coord.png){fig-align="center"} -## Time for the tutorial ... - -## Getting involved +# Getting involved ::: {.nonincremental} - [Skrub website](https://skrub-data.org/stable/) (QR code below!) - [Skrub materials website](https://skrub-data.org/skrub-materials/index.html) @@ -335,3 +333,5 @@ search.plot_parallel_coord() ::: ![](images/qr-code.png){.absolute bottom=0 right=0 width="250" height="250"} + +# Time for the tutorial ... \ No newline at end of file From e969d4025f25c8059c03d22b648fa3ae95877da4 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Sun, 10 Aug 2025 21:39:11 +0200 Subject: [PATCH 06/11] fixes --- .../python_files/single_horizon_prediction.py | 38 +++++++++---------- content/slides/slides.qmd | 2 +- content/slides/style.css | 2 +- 3 files changed, 20 insertions(+), 22 deletions(-) diff --git a/content/python_files/single_horizon_prediction.py b/content/python_files/single_horizon_prediction.py index 910cca4..1a56330 100644 --- a/content/python_files/single_horizon_prediction.py +++ b/content/python_files/single_horizon_prediction.py @@ -72,21 +72,21 @@ features_with_dropped_cols = features.skb.apply( - skrub.DropCols( + skrub.SelectCols( cols=skrub.choose_from( { - "none": s.glob(""), # No column has an empty name. - # "load": s.glob("load_*"), - # "rolling_load": s.glob("load_mw_rolling_*"), - # "weather": s.glob("weather_*"), - # "temperature": s.glob("weather_temperature_*"), - # "moisture": s.glob("weather_moisture_*"), - # "cloud_cover": s.glob("weather_cloud_cover_*"), - # "calendar": s.glob("cal_*"), - # "holiday": s.glob("cal_is_holiday*"), - # "future_1h": s.glob("*_future_1h"), - # "future_24h": s.glob("*_future_24h"), - # "non_paris_weather": s.glob("weather_*") & ~s.glob("weather_*_paris_*"), + "none": ~s.glob(""), # No column has an empty name. + "load": ~s.glob("load_*"), + "rolling_load": ~s.glob("load_mw_rolling_*"), + "weather": ~s.glob("weather_*"), + "temperature": ~s.glob("weather_temperature_*"), + "moisture": ~s.glob("weather_moisture_*"), + "cloud_cover": ~s.glob("weather_cloud_cover_*"), + "calendar": ~s.glob("cal_*"), + "holiday": ~s.glob("cal_is_holiday*"), + "future_1h": ~s.glob("*_future_1h"), + "future_24h": ~s.glob("*_future_24h"), + # "non_paris_weather": ~s.glob("weather_*") & s.glob("weather_*_paris_*"), }, name="dropped_cols", ) @@ -108,7 +108,7 @@ ) hgbr_predictions -horizon_of_interest = horizons[-1] # Focus on the 24-hour horizon# %% [markdown] +horizon_of_interest = 24 # Focus on the 24-hour horizon# %% [markdown] # # The `predictions` expression captures the whole expression graph that # includes the feature engineering steps, the target variable, and the model @@ -126,7 +126,7 @@ # follows: # %% -hgbr_pipeline = hgbr_predictions.skb.get_pipeline() +hgbr_pipeline = hgbr_predictions.skb.make_learner() hgbr_pipeline.describe_params() # %% [markdown] @@ -227,13 +227,11 @@ scoring={ "mape": make_scorer(mean_absolute_percentage_error), "r2": get_scorer("r2"), - "d2_poisson": make_scorer(d2_tweedie_score, power=1.0), - "d2_gamma": make_scorer(d2_tweedie_score, power=2.0), }, return_train_score=True, - return_pipeline=True, + return_learner=True, verbose=1, - n_jobs=-1, + n_jobs=4, ) hgbr_cv_results.round(3) @@ -261,7 +259,7 @@ # %% hgbr_cv_predictions = collect_cv_predictions( - hgbr_cv_results["pipeline"], ts_cv_5, hgbr_predictions, prediction_time + hgbr_cv_results["learner"], ts_cv_5, hgbr_predictions, prediction_time ) hgbr_cv_predictions[0] diff --git a/content/slides/slides.qmd b/content/slides/slides.qmd index 675eaf1..f99afd4 100644 --- a/content/slides/slides.qmd +++ b/content/slides/slides.qmd @@ -1,5 +1,5 @@ --- -title: "Skrub: Machine Learning with Dataframes" +title: "Skrub: ML with Dataframes" title-block-banner: true date: 2025-08-18 subtitle: "Tutorial at EuroSciPy 2025" diff --git a/content/slides/style.css b/content/slides/style.css index b395eb0..e967072 100644 --- a/content/slides/style.css +++ b/content/slides/style.css @@ -1,7 +1,7 @@ h1.title { background-image: url(images/skrub.svg); background-repeat: no-repeat; - background-position: left; + background-position: left bottom; background-size: 200px; min-height: 200px; /* Set the minimum height to match the background image height */ } \ No newline at end of file From c04349fff64dae73b59dfc96b90af1981230cc66 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Tue, 12 Aug 2025 16:02:22 +0200 Subject: [PATCH 07/11] removing book --- book/_config.yml | 33 ------------------ book/_toc.yml | 15 --------- book/content | 1 - book/images/jupyterbook_badge.svg | 1 - book/images/jupyterlite_badge.svg | 3 -- book/intro.md | 36 -------------------- book/logo.svg | 25 -------------- book/references.bib | 56 ------------------------------- 8 files changed, 170 deletions(-) delete mode 100644 book/_config.yml delete mode 100644 book/_toc.yml delete mode 120000 book/content delete mode 100644 book/images/jupyterbook_badge.svg delete mode 100644 book/images/jupyterlite_badge.svg delete mode 100644 book/intro.md delete mode 100644 book/logo.svg delete mode 100644 book/references.bib diff --git a/book/_config.yml b/book/_config.yml deleted file mode 100644 index 06ce59b..0000000 --- a/book/_config.yml +++ /dev/null @@ -1,33 +0,0 @@ -# Book settings -# Learn more at https://jupyterbook.org/customize/config.html - -title: Forecasting with machine learning -author: Guillaume Lemaitre & Olivier Grisel -logo: logo.svg - -# Force re-execution of notebooks on each build. -# See https://jupyterbook.org/content/execute.html -execute: - execute_notebooks: force - timeout: 600 - -# Define the name of the latex output file for PDF builds -latex: - latex_documents: - targetname: book.tex - -# Add a bibtex file so that we can create citations -bibtex_bibfiles: - - references.bib - -# Information about where the book exists on the web -repository: - url: https://github.com/probabl-ai/forecasting - path_to_book: book - branch: main - -# Add GitHub buttons to your book -# See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository -html: - use_issues_button: true - use_repository_button: true diff --git a/book/_toc.yml b/book/_toc.yml deleted file mode 100644 index 05e5bef..0000000 --- a/book/_toc.yml +++ /dev/null @@ -1,15 +0,0 @@ -# Table of contents -# Learn more at https://jupyterbook.org/customize/toc.html - -format: jb-book -root: intro -parts: - -- caption: Forecasting with Machine Learning - chapters: - - file: content/notebooks/fetch_weather_data - - file: content/notebooks/feature_engineering - - file: content/notebooks/single_horizon_prediction - - file: content/notebooks/multiple_horizons_prediction - - file: content/notebooks/prediction_intervals - - file: content/notebooks/direct_vs_recursive_forecasting diff --git a/book/content b/book/content deleted file mode 120000 index efcdaa6..0000000 --- a/book/content +++ /dev/null @@ -1 +0,0 @@ -../content \ No newline at end of file diff --git a/book/images/jupyterbook_badge.svg b/book/images/jupyterbook_badge.svg deleted file mode 100644 index 54ee8dd..0000000 --- a/book/images/jupyterbook_badge.svg +++ /dev/null @@ -1 +0,0 @@ -jupyter: bookjupyterbook \ No newline at end of file diff --git a/book/images/jupyterlite_badge.svg b/book/images/jupyterlite_badge.svg deleted file mode 100644 index 5de36d7..0000000 --- a/book/images/jupyterlite_badge.svg +++ /dev/null @@ -1,3 +0,0 @@ - - -launchlaunchlitelite \ No newline at end of file diff --git a/book/intro.md b/book/intro.md deleted file mode 100644 index 8188a98..0000000 --- a/book/intro.md +++ /dev/null @@ -1,36 +0,0 @@ -# Forecasting with machine learning - -This tutorial introduces how to use machine learning to forecast time series data. - -## Table of contents - -### Slides - -### Notebooks [![Launch JupyterLite](/images/jupyterlite_badge.svg 'Our JupyterLite website')](https://probabl-ai.github.io/forecasting/jupyterlite/lab) - -```{tableofcontents} -``` - -## Running the notebooks locally - -### Install `pixi` - -You can refer to the [official website](https://pixi.sh/latest/#installation) for -installation. - -### Launching Jupyter Lab - -To launch Jupyter Lab, run the following command: - -```bash -pixi run jupyter lab -``` - -The Python environment and necessary packages will be automatically installed for you. - -### Opening lecture notes - -The lecture notes are available in the `python_files` directory. To open the Python -file as notebook, you need to right click on the file and select -`Open with` -> `Notebook`. - diff --git a/book/logo.svg b/book/logo.svg deleted file mode 100644 index f6f9763..0000000 --- a/book/logo.svg +++ /dev/null @@ -1,25 +0,0 @@ - - -image/svg+xml - - - - - - - - - - - - - - -scikit - - - - - - - \ No newline at end of file diff --git a/book/references.bib b/book/references.bib deleted file mode 100644 index 783ec6a..0000000 --- a/book/references.bib +++ /dev/null @@ -1,56 +0,0 @@ ---- ---- - -@inproceedings{holdgraf_evidence_2014, - address = {Brisbane, Australia, Australia}, - title = {Evidence for {Predictive} {Coding} in {Human} {Auditory} {Cortex}}, - booktitle = {International {Conference} on {Cognitive} {Neuroscience}}, - publisher = {Frontiers in Neuroscience}, - author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Knight, Robert T.}, - year = {2014} -} - -@article{holdgraf_rapid_2016, - title = {Rapid tuning shifts in human auditory cortex enhance speech intelligibility}, - volume = {7}, - issn = {2041-1723}, - url = {http://www.nature.com/doifinder/10.1038/ncomms13654}, - doi = {10.1038/ncomms13654}, - number = {May}, - journal = {Nature Communications}, - author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Rieger, Jochem W. and Crone, Nathan and Lin, Jack J. and Knight, Robert T. and Theunissen, Frédéric E.}, - year = {2016}, - pages = {13654}, - file = {Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:C\:\\Users\\chold\\Zotero\\storage\\MDQP3JWE\\Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:application/pdf} -} - -@inproceedings{holdgraf_portable_2017, - title = {Portable learning environments for hands-on computational instruction using container-and cloud-based technology to teach data science}, - volume = {Part F1287}, - isbn = {978-1-4503-5272-7}, - doi = {10.1145/3093338.3093370}, - abstract = {© 2017 ACM. There is an increasing interest in learning outside of the traditional classroom setting. This is especially true for topics covering computational tools and data science, as both are challenging to incorporate in the standard curriculum. These atypical learning environments offer new opportunities for teaching, particularly when it comes to combining conceptual knowledge with hands-on experience/expertise with methods and skills. Advances in cloud computing and containerized environments provide an attractive opportunity to improve the effciency and ease with which students can learn. This manuscript details recent advances towards using commonly-Available cloud computing services and advanced cyberinfrastructure support for improving the learning experience in bootcamp-style events. We cover the benets (and challenges) of using a server hosted remotely instead of relying on student laptops, discuss the technology that was used in order to make this possible, and give suggestions for how others could implement and improve upon this model for pedagogy and reproducibility.}, - booktitle = {{ACM} {International} {Conference} {Proceeding} {Series}}, - author = {Holdgraf, Christopher Ramsay and Culich, A. and Rokem, A. and Deniz, F. and Alegro, M. and Ushizima, D.}, - year = {2017}, - keywords = {Teaching, Bootcamps, Cloud computing, Data science, Docker, Pedagogy} -} - -@article{holdgraf_encoding_2017, - title = {Encoding and decoding models in cognitive electrophysiology}, - volume = {11}, - issn = {16625137}, - doi = {10.3389/fnsys.2017.00061}, - abstract = {© 2017 Holdgraf, Rieger, Micheli, Martin, Knight and Theunissen. Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of “Encoding” models, in which stimulus features are used to model brain activity, and “Decoding” models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aimis to provide a practical understanding of predictivemodeling of human brain data and to propose best-practices in conducting these analyses.}, - journal = {Frontiers in Systems Neuroscience}, - author = {Holdgraf, Christopher Ramsay and Rieger, J.W. and Micheli, C. and Martin, S. and Knight, R.T. and Theunissen, F.E.}, - year = {2017}, - keywords = {Decoding models, Encoding models, Electrocorticography (ECoG), Electrophysiology/evoked potentials, Machine learning applied to neuroscience, Natural stimuli, Predictive modeling, Tutorials} -} - -@book{ruby, - title = {The Ruby Programming Language}, - author = {Flanagan, David and Matsumoto, Yukihiro}, - year = {2008}, - publisher = {O'Reilly Media} -} From 886598698741e797d6c662e3b53616414287d3b4 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Tue, 12 Aug 2025 16:02:35 +0200 Subject: [PATCH 08/11] cleanup --- content/slides/slides.qmd | 12 +- pixi.lock | 1119 +++++++++++++++++++------------------ pixi.toml | 3 +- 3 files changed, 564 insertions(+), 570 deletions(-) diff --git a/content/slides/slides.qmd b/content/slides/slides.qmd index f99afd4..7a4f4eb 100644 --- a/content/slides/slides.qmd +++ b/content/slides/slides.qmd @@ -133,13 +133,12 @@ ct = make_column_transformer( model = make_pipeline(ct, SimpleImputer(), Ridge()) ``` -## Build a predictive pipeline with `tabular_pipeline` {auto-animate="true" .smaller} +## Build a predictive pipeline with `tabular_pipeline` {auto-animate="true"} ```{python} #| echo: true import skrub from sklearn.linear_model import Ridge model = skrub.tabular_pipeline(Ridge()) -model ``` ## We now have a pipeline! {.smaller} @@ -249,15 +248,6 @@ features = features.drop(columns=["ID", "basket_ID"]) predictions = features.skb.apply(ExtraTreesClassifier(n_jobs=-1), y=y) ``` -## Inspecting the data plan -```{.python} -predictions.skb.full_report() -``` - -
- -report - ## Exporting the plan in a `learner` {.smaller} The data plan can be exported as a `learner`: ```{.python} diff --git a/pixi.lock b/pixi.lock index 87c0e9b..85666d5 100644 --- a/pixi.lock +++ b/pixi.lock @@ -281,7 +281,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/rfc3986-validator-0.1.1-pyh9f0ad1d_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.26.0-py312h680f630_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/s2n-1.5.22-h96f233e_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.7.0-py312h7a48858_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.7.1-py312h4f0b9e3_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.15.2-py312ha707e6e_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/send2trash-1.8.3-pyh0d859eb_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-80.9.0-pyhff2d567_0.conda @@ -623,7 +623,7 @@ environments: - 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cffi >=1.11 - - python >=3.12,<3.13.0a0 - - python_abi 3.12.* *_cp312 + - python >=3.13,<3.14.0a0 + - python_abi 3.13.* *_cp313 - ucrt >=10.0.20348.0 - vc >=14.2,<15 - vc14_runtime >=14.29.30139 @@ -18449,8 +18454,8 @@ packages: license_family: BSD purls: - pkg:pypi/zstandard?source=hash-mapping - size: 444685 - timestamp: 1745870132644 + size: 449871 + timestamp: 1745870298072 - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda sha256: a4166e3d8ff4e35932510aaff7aa90772f84b4d07e9f6f83c614cba7ceefe0eb md5: 6432cb5d4ac0046c3ac0a8a0f95842f9 diff --git a/pixi.toml b/pixi.toml index 84033bc..9c7f3ac 100644 --- a/pixi.toml +++ b/pixi.toml @@ -58,10 +58,9 @@ copy-tutorial-helpers = { cmd = "cp ./content/python_files/tutorial_helpers.py . copy-parallel-coordinates-plots = { cmd = "cp ./content/python_files/*.json ./content/notebooks/", depends-on = ["create-notebooks-dir"] } convert-to-notebooks = { cmd = "jupytext --to notebook ./content/python_files/*.py && mv ./content/python_files/*.ipynb ./content/notebooks", depends-on = ["create-notebooks-dir", "copy-tutorial-helpers", "copy-parallel-coordinates-plots"] } convert-to-executed-notebooks = { cmd = "jupytext --to notebook --execute ./content/python_files/*.py && mv ./content/python_files/*.ipynb ./content/notebooks", depends-on = ["create-notebooks-dir", "copy-pickled-pipelines", "copy-tutorial-helpers", "copy-parallel-coordinates-plots"] } -build-book = { cmd = "jupyter-book build book", depends-on = ["convert-to-notebooks", "copy-tutorial-helpers", "copy-parallel-coordinates-plots"] } build-jupyterlite = { cmd = "jupyter lite build --contents content --output-dir dist", cwd = "jupyterlite", depends-on = ["convert-to-notebooks"] } serve-jupyterlite = { cmd = "python -m http.server", cwd = "jupyterlite/dist", depends-on = ["build-jupyterlite"] } -build-slides = { cmd = "quarto render", cwd = "content/slides", depends-on = ["convert-to-notebooks"] } +build-slides = { cmd = "quarto render", cwd = "content/slides"} [environments] lint = ["lint"] From 958f4c59fbb158bd3da65a331ec84b1a43debd56 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Tue, 12 Aug 2025 16:11:25 +0200 Subject: [PATCH 09/11] _ --- content/python_files/single_horizon_prediction.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/content/python_files/single_horizon_prediction.py b/content/python_files/single_horizon_prediction.py index 1a56330..bc5372d 100644 --- a/content/python_files/single_horizon_prediction.py +++ b/content/python_files/single_horizon_prediction.py @@ -108,9 +108,11 @@ ) hgbr_predictions -horizon_of_interest = 24 # Focus on the 24-hour horizon# %% [markdown] +horizon_of_interest = 24 # Focus on the 24-hour horizon + +# %% [markdown] # -# The `predictions` expression captures the whole expression graph that +# The `predictions` DataOp captures the whole expression graph that # includes the feature engineering steps, the target variable, and the model # training step. # @@ -140,7 +142,7 @@ # %% [markdown] # # Since we passed input values to all the upstream `skrub` variables, `skrub` -# automatically evaluates the whole expression graph graph (train and predict +# automatically evaluates the whole DataOps graph (train and predict # on the same data) so that we can interactively check that everything will # work as expected. # @@ -198,7 +200,7 @@ # # Once the cross-validation strategy is defined, we pass it to the # `cross_validate` function provided by `skrub` to compute the cross-validated -# scores. Here, we compute the mean absolute percentage error that is easily +# scores. Here, we compute the mean absolute percentage error because it is easily # interpretable and customary for regression tasks with a strictly positive # target variable such as electricity load forecasting. # @@ -211,9 +213,10 @@ # predicts the mean of the target variable for all observations, irrespective # of the features. # -# No that in general, a deviance score of 1.0 is not reachable since it +# Note that in general, a deviance score of 1.0 is not reachable since it # corresponds to a model that always predicts the target value exactly -# for all observations. In practice, because there is always a fraction of the +# for all observations. In practice, this does not happen because there is always +# a fraction of the # variability in the target variable that is not explained by the information # available to construct the features. From a211a8d224a31aaa13f214d9bcd2590f4a7b3dca Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Fri, 15 Aug 2025 12:25:12 +0200 Subject: [PATCH 10/11] MAINT update dependencies and call to skrub API --- content/python_files/feature_engineering.py | 17 +- .../parallel_coordinates_hgbr.json | 2 +- .../parallel_coordinates_ridge.json | 2 +- .../python_files/single_horizon_prediction.py | 79 +- environment.yml | 5 +- pixi.lock | 9921 +++++++---------- pixi.toml | 5 +- 7 files changed, 4350 insertions(+), 5681 deletions(-) diff --git a/content/python_files/feature_engineering.py b/content/python_files/feature_engineering.py index 299b9e1..2886a6c 100644 --- a/content/python_files/feature_engineering.py +++ b/content/python_files/feature_engineering.py @@ -27,13 +27,11 @@ # ## Environment setup # # We need to install some extra dependencies for this notebook if needed (when -# running jupyterlite). We need the development version of skrub to be able to -# use the skrub expressions. +# running jupyterlite). # %% # %pip install -q https://pypi.anaconda.org/ogrisel/simple/polars/1.24.0/polars-1.24.0-cp39-abi3-emscripten_3_1_58_wasm32.whl -# %pip install -q https://pypi.anaconda.org/ogrisel/simple/skrub/0.6.dev0/skrub-0.6.dev0-py3-none-any.whl -# %pip install -q altair holidays plotly nbformat +# %pip install -q altair holidays plotly nbformat skrub # %% [markdown] # @@ -209,6 +207,7 @@ def prepare_french_calendar_data(time): add_weekday=True, add_day_of_year=True, add_total_seconds=False ) + @skrub.deferred def prepare_holidays(time): fr_time = pl.col("time").dt.convert_time_zone("Europe/Paris") @@ -218,8 +217,7 @@ def prepare_holidays(time): "FR", years=range(fr_year_min, fr_year_max + 1) ) return time.select( - fr_time.dt.date().is_in(holidays_fr.keys()).alias("cal_is_holiday"), - + fr_time.dt.date().is_in(holidays_fr.keys()).alias("cal_is_holiday"), ) @@ -236,6 +234,7 @@ def prepare_holidays(time): # # Finally we load the electricity load data. This data will both be used as a # target variable but also to craft some lagged and window-aggregated features. + # %% @skrub.deferred def load_electricity_load_data(time, data_source_folder): @@ -440,19 +439,19 @@ def build_features( # Let's build training and evaluation targets for all possible horizons from 1 # to 24 hours. + # %% @skrub.deferred def build_targets(prediction_time, electricity): return prediction_time.join( electricity.with_columns( - pl.col("load_mw") - .shift(-24) - .alias("load_mw_horizon_24h") + pl.col("load_mw").shift(-24).alias("load_mw_horizon_24h") ), left_on="prediction_time", right_on="time", ) + targets = build_targets(prediction_time, electricity) targets diff --git a/content/python_files/parallel_coordinates_hgbr.json b/content/python_files/parallel_coordinates_hgbr.json index 40e5c4b..8725a07 100644 --- a/content/python_files/parallel_coordinates_hgbr.json +++ b/content/python_files/parallel_coordinates_hgbr.json @@ -1 +1 @@ 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\ No newline at end of file diff --git a/content/python_files/single_horizon_prediction.py b/content/python_files/single_horizon_prediction.py index bc5372d..bb99d6e 100644 --- a/content/python_files/single_horizon_prediction.py +++ b/content/python_files/single_horizon_prediction.py @@ -5,13 +5,11 @@ # ## Environment setup # # We need to install some extra dependencies for this notebook if needed (when -# running jupyterlite). We need the development version of skrub to be able to -# use the skrub expressions. +# running jupyterlite). # %% # %pip install -q https://pypi.anaconda.org/ogrisel/simple/polars/1.24.0/polars-1.24.0-cp39-abi3-emscripten_3_1_58_wasm32.whl -# %pip install -q https://pypi.anaconda.org/ogrisel/simple/skrub/0.6.dev0/skrub-0.6.dev0-py3-none-any.whl -# %pip install -q altair holidays plotly nbformat +# %pip install -q altair holidays plotly nbformat skrub # %% import warnings @@ -72,21 +70,21 @@ features_with_dropped_cols = features.skb.apply( - skrub.SelectCols( + skrub.DropCols( cols=skrub.choose_from( { - "none": ~s.glob(""), # No column has an empty name. - "load": ~s.glob("load_*"), - "rolling_load": ~s.glob("load_mw_rolling_*"), - "weather": ~s.glob("weather_*"), - "temperature": ~s.glob("weather_temperature_*"), - "moisture": ~s.glob("weather_moisture_*"), - "cloud_cover": ~s.glob("weather_cloud_cover_*"), - "calendar": ~s.glob("cal_*"), - "holiday": ~s.glob("cal_is_holiday*"), - "future_1h": ~s.glob("*_future_1h"), - "future_24h": ~s.glob("*_future_24h"), - # "non_paris_weather": ~s.glob("weather_*") & s.glob("weather_*_paris_*"), + "none": s.glob(""), # No column has an empty name. + "load": s.glob("load_*"), + "rolling_load": s.glob("load_mw_rolling_*"), + "weather": s.glob("weather_*"), + "temperature": s.glob("weather_temperature_*"), + "moisture": s.glob("weather_moisture_*"), + "cloud_cover": s.glob("weather_cloud_cover_*"), + "calendar": s.glob("cal_*"), + "holiday": s.glob("cal_is_holiday*"), + "future_1h": s.glob("*_future_1h"), + "future_24h": s.glob("*_future_24h"), + "non_paris_weather": s.glob("weather_*") & s.glob("weather_*_paris_*"), }, name="dropped_cols", ) @@ -108,7 +106,7 @@ ) hgbr_predictions -horizon_of_interest = 24 # Focus on the 24-hour horizon +horizon_of_interest = 24 # Focus on the 24-hour horizon # %% [markdown] # @@ -230,6 +228,8 @@ scoring={ "mape": make_scorer(mean_absolute_percentage_error), "r2": get_scorer("r2"), + "d2_poisson": make_scorer(d2_tweedie_score, power=1.0), + "d2_gamma": make_scorer(d2_tweedie_score, power=2.0), }, return_train_score=True, return_learner=True, @@ -273,13 +273,9 @@ # visualization to the last 7 days of the fold. # %% -altair.Chart( - hgbr_cv_predictions[0].tail(24 * 7) -).transform_fold( +altair.Chart(hgbr_cv_predictions[0].tail(24 * 7)).transform_fold( ["load_mw", "predicted_load_mw"], -).mark_line( - tooltip=True -).encode( +).mark_line(tooltip=True).encode( x="prediction_time:T", y="value:Q", color="key:N" ).interactive() @@ -354,7 +350,7 @@ ts_cv_2 = TimeSeriesSplit( n_splits=2, test_size=test_size, max_train_size=max_train_size, gap=24 ) -# randomized_search_hgbr = hgbr_predictions.skb.get_randomized_search( +# randomized_search_hgbr = hgbr_predictions.skb.make_randomized_search( # cv=ts_cv_2, # scoring="r2", # n_iter=100, @@ -362,7 +358,8 @@ # verbose=1, # n_jobs=-1, # ) -# # %% + +# %% # randomized_search_hgbr.results_.round(3) # %% @@ -370,20 +367,20 @@ # write_json(fig, "parallel_coordinates_hgbr.json") # %% -fig = read_json("parallel_coordinates_hgbr.json") -fig.update_layout(margin=dict(l=200)) +# fig = read_json("parallel_coordinates_hgbr.json") +# fig.update_layout(margin=dict(l=200)) # %% # nested_cv_results = skrub.cross_validate( -# environment=predictions.skb.get_data(), -# pipeline=randomized_search, +# environment=hgbr_predictions.skb.get_data(), +# learner=randomized_search_hgbr, # cv=ts_cv_5, # scoring={ # "r2": get_scorer("r2"), # "mape": make_scorer(mean_absolute_percentage_error), # }, # n_jobs=-1, -# return_pipeline=True, +# return_learner=True, # ).round(3) # nested_cv_results @@ -499,9 +496,9 @@ "mape": make_scorer(mean_absolute_percentage_error), }, return_train_score=True, - return_pipeline=True, + return_learner=True, verbose=1, - n_jobs=-1, + n_jobs=4, ) # %% [markdown] @@ -531,15 +528,13 @@ # %% cv_predictions_ridge = collect_cv_predictions( - cv_results_ridge["pipeline"], ts_cv_5, predictions_ridge, prediction_time + cv_results_ridge["learner"], ts_cv_5, predictions_ridge, prediction_time ) # %% altair.Chart(cv_predictions_ridge[0].tail(24 * 7)).transform_fold( ["load_mw", "predicted_load_mw"], -).mark_line( - tooltip=True -).encode( +).mark_line(tooltip=True).encode( x="prediction_time:T", y="value:Q", color="key:N" ).interactive() @@ -576,13 +571,13 @@ # expensive, we are reloading the results of the parallel coordinates plot. # %% -# randomized_search_ridge = predictions_ridge.skb.get_randomized_search( +# randomized_search_ridge = predictions_ridge.skb.make_randomized_search( # cv=ts_cv_2, # scoring="r2", # n_iter=100, # fitted=True, # verbose=1, -# n_jobs=-1, +# n_jobs=4, # ) # %% @@ -608,14 +603,14 @@ # %% # nested_cv_results_ridge = skrub.cross_validate( # environment=predictions_ridge.skb.get_data(), -# pipeline=randomized_search_ridge, +# learner=randomized_search_ridge, # cv=ts_cv_5, # scoring={ # "r2": get_scorer("r2"), # "mape": make_scorer(mean_absolute_percentage_error), # }, -# n_jobs=-1, -# return_pipeline=True, +# n_jobs=4, +# return_learner=True, # ).round(3) # %% diff --git a/environment.yml b/environment.yml index 301dbeb..e5192b2 100644 --- a/environment.yml +++ b/environment.yml @@ -13,14 +13,11 @@ dependencies: - pip >=25.1.1,<26 - ipyleaflet >=0.20.0,<0.21 - pandas >=2.3.0,<3 - - mlforecast >=1.0.2,<2 - altair >=5.5.0,<6 - graphviz >=13.1.0,<14 - pydot >=4.0.1,<5 - - u8darts >=0.36.0,<0.37 - - setuptools <81 - plotly >=6.2.0,<7 - - skrub ==0.6.0 + - skrub ==0.6.1 - pip: - openmeteo-requests >=1.5.0, <2 - requests-cache >=1.2.1, <2 diff --git a/pixi.lock b/pixi.lock index 85666d5..814c174 100644 --- a/pixi.lock +++ b/pixi.lock @@ -9,14 +9,11 @@ environments: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2 - - conda: 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a/pixi.toml +++ b/pixi.toml @@ -22,15 +22,12 @@ holidays = ">=0.75,<0.76" pip = ">=25.1.1,<26" ipyleaflet = ">=0.20.0,<0.21" pandas = ">=2.3.0,<3" -mlforecast = ">=1.0.2,<2" altair = ">=5.5.0,<6" graphviz = ">=13.1.0,<14" pydot = ">=4.0.1,<5" -u8darts = ">=0.36.0,<0.37" -setuptools = "<81" plotly = ">=6.2.0,<7" cloudpickle = ">=3.0.0,<3.1" -skrub = "==0.6.0" +skrub = "==0.6.1" [pypi-dependencies] openmeteo-requests = ">=1.5.0, <2" From d9c0b02cc5da76fcdbfefac51c6a73af57d5f80e Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Fri, 15 Aug 2025 13:00:09 +0200 Subject: [PATCH 11/11] DOC add static version for the notebook --- README.md | 6 +-- book/_config.yml | 33 +++++++++++++ book/_toc.yml | 12 +++++ book/content | 1 + book/images/jupyterbook_badge.svg | 1 + book/images/jupyterlite_badge.svg | 3 ++ book/intro.md | 38 +++++++++++++++ book/logo.svg | 79 +++++++++++++++++++++++++++++++ pixi.toml | 5 +- 9 files changed, 173 insertions(+), 5 deletions(-) create mode 100644 book/_config.yml create mode 100644 book/_toc.yml create mode 120000 book/content create mode 100644 book/images/jupyterbook_badge.svg create mode 100644 book/images/jupyterlite_badge.svg create mode 100644 book/intro.md create mode 100644 book/logo.svg diff --git a/README.md b/README.md index 9a707e2..b6237e6 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,13 @@ -# Tutorial on Forecasting with Machine Learning +# Tutorial on Skrub DataOps applied to forecasting timeseries ## Browse the contents online - [![Launch JupyterLite](./book/images/jupyterbook_badge.svg 'Our JupyterBook -website')](https://probabl-ai.github.io/forecasting/) +website')](skrub-data.org/EuroSciPy2025/) Static HTML with precomputed results. - [![Launch JupyterLite](./book/images/jupyterlite_badge.svg 'Our JupyterLite -website')](https://probabl-ai.github.io/forecasting/jupyterlite/lab) +website')](skrub-data.org/EuroSciPy2025/jupyterlite/lab) In-browser interactive execution environment, no install needed. ## Local setup diff --git a/book/_config.yml b/book/_config.yml new file mode 100644 index 0000000..ba87072 --- /dev/null +++ b/book/_config.yml @@ -0,0 +1,33 @@ +# Book settings +# Learn more at https://jupyterbook.org/customize/config.html + +title: Skrub DataOps applied to forecasting timeseries +author: Guillaume Lemaitre & Olivier Grisel & Riccardo Cappuzzo +logo: logo.svg + +# Force re-execution of notebooks on each build. +# See https://jupyterbook.org/content/execute.html +execute: + execute_notebooks: force + timeout: 600 + +# Define the name of the latex output file for PDF builds +latex: + latex_documents: + targetname: book.tex + +# Add a bibtex file so that we can create citations +bibtex_bibfiles: + - references.bib + +# Information about where the book exists on the web +repository: + url: https://github.com/skrub-data/EuroSciPy2025 + path_to_book: book + branch: main + +# Add GitHub buttons to your book +# See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository +html: + use_issues_button: true + use_repository_button: true diff --git a/book/_toc.yml b/book/_toc.yml new file mode 100644 index 0000000..b58b498 --- /dev/null +++ b/book/_toc.yml @@ -0,0 +1,12 @@ +# Table of contents +# Learn more at https://jupyterbook.org/customize/toc.html + +format: jb-book +root: intro +parts: + +- caption: Skrub DataOps applied to forecasting timeseries + chapters: + - file: content/notebooks/fetch_weather_data + - file: content/notebooks/feature_engineering + - file: content/notebooks/single_horizon_prediction diff --git a/book/content b/book/content new file mode 120000 index 0000000..efcdaa6 --- /dev/null +++ b/book/content @@ -0,0 +1 @@ +../content \ No newline at end of file diff --git a/book/images/jupyterbook_badge.svg b/book/images/jupyterbook_badge.svg new file mode 100644 index 0000000..54ee8dd --- /dev/null +++ b/book/images/jupyterbook_badge.svg @@ -0,0 +1 @@ +jupyter: bookjupyterbook \ No newline at end of file diff --git a/book/images/jupyterlite_badge.svg b/book/images/jupyterlite_badge.svg new file mode 100644 index 0000000..5de36d7 --- /dev/null +++ b/book/images/jupyterlite_badge.svg @@ -0,0 +1,3 @@ + + +launchlaunchlitelite \ No newline at end of file diff --git a/book/intro.md b/book/intro.md new file mode 100644 index 0000000..e8fee36 --- /dev/null +++ b/book/intro.md @@ -0,0 +1,38 @@ +# Skrub DataOps applied to forecasting timeseries + +This tutorial introduces the `skrub` +[DataOps](https://skrub-data.org/stable/userguide_data_ops.html) to a forecasting +timeseries use case. + +## Table of contents + +### Slides + +### Notebooks [![Launch JupyterLite](/images/jupyterlite_badge.svg 'Our JupyterLite website')](https://skrub-data.org/EuroSciPy2025/jupyterlite/lab) + +```{tableofcontents} +``` + +## Running the notebooks locally + +### Install `pixi` + +You can refer to the [official website](https://pixi.sh/latest/#installation) for +installation. + +### Launching Jupyter Lab + +To launch Jupyter Lab, run the following command: + +```bash +pixi run jupyter lab +``` + +The Python environment and necessary packages will be automatically installed for you. + +### Opening lecture notes + +The lecture notes are available in the `python_files` directory. To open the Python +file as notebook, you need to right click on the file and select +`Open with` -> `Notebook`. + diff --git a/book/logo.svg b/book/logo.svg new file mode 100644 index 0000000..744e2fd --- /dev/null +++ b/book/logo.svg @@ -0,0 +1,79 @@ + + + + + + + + + + + + + + + + diff --git a/pixi.toml b/pixi.toml index 43ca3bb..a57d0d8 100644 --- a/pixi.toml +++ b/pixi.toml @@ -1,7 +1,7 @@ [project] -name = "Euroscipy 2025 - Skrub tutorial" +name = "Skrub DataOps applied to forecasting timeseries" version = "0.1.0" -description = "Skrub tutorial for Euroscipy 2025." +description = "Skrub DataOps applied to forecasting timeseries" authors = ["Guillaume Lemaitre ", "Olivier Grisel ", "Riccardo Cappuzzo "] @@ -55,6 +55,7 @@ copy-tutorial-helpers = { cmd = "cp ./content/python_files/tutorial_helpers.py . copy-parallel-coordinates-plots = { cmd = "cp ./content/python_files/*.json ./content/notebooks/", depends-on = ["create-notebooks-dir"] } convert-to-notebooks = { cmd = "jupytext --to notebook ./content/python_files/*.py && mv ./content/python_files/*.ipynb ./content/notebooks", depends-on = ["create-notebooks-dir", "copy-tutorial-helpers", "copy-parallel-coordinates-plots"] } convert-to-executed-notebooks = { cmd = "jupytext --to notebook --execute ./content/python_files/*.py && mv ./content/python_files/*.ipynb ./content/notebooks", depends-on = ["create-notebooks-dir", "copy-pickled-pipelines", "copy-tutorial-helpers", "copy-parallel-coordinates-plots"] } +build-book = { cmd = "jupyter-book build book", depends-on = ["convert-to-notebooks", "copy-tutorial-helpers", "copy-parallel-coordinates-plots"] } build-jupyterlite = { cmd = "jupyter lite build --contents content --output-dir dist", cwd = "jupyterlite", depends-on = ["convert-to-notebooks"] } serve-jupyterlite = { cmd = "python -m http.server", cwd = "jupyterlite/dist", depends-on = ["build-jupyterlite"] } build-slides = { cmd = "quarto render", cwd = "content/slides"}