Merge pull request #9 from soda-inria/data

New method to load data and new release 0.0.23

build/lib/carte_ai/__init__.py

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+from carte_ai.src import *
+from carte_ai.configs import *
+from carte_ai.data import *
+from carte_ai.scripts import *
+from .src import CARTERegressor, CARTEClassifier, Table2GraphTransformer

build/lib/carte_ai/configs/__init__.py

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+from carte_ai.configs.carte_configs import *
+from carte_ai.configs.directory import *
+from carte_ai.configs.model_parameters import *
+from carte_ai.configs.visuailization import *

build/lib/carte_ai/configs/carte_configs.py

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+"""Specific configurations for the CARTE paper."""
+
+## Dataset names
+carte_datalist = [
+    "anime_planet",
+    "babies_r_us",
+    "beer_ratings",
+    "bikedekho",
+    "bikewale",
+    "buy_buy_baby",
+    "cardekho",
+    "chocolate_bar_ratings",
+    "clear_corpus",
+    "coffee_ratings",
+    "company_employees",
+    "employee_remuneration",
+    "employee_salaries",
+    "fifa22_players",
+    "filmtv_movies",
+    "journal_jcr",
+    "journal_sjr",
+    "jp_anime",
+    "k_drama",
+    "michelin",
+    "mlds_salaries",
+    "movies",
+    "museums",
+    "mydramalist",
+    "nba_draft",
+    "prescription_drugs",
+    "ramen_ratings",
+    "roger_ebert",
+    "rotten_tomatoes",
+    "spotify",
+    "us_accidents_counts",
+    "us_accidents_severity",
+    "us_presidential",
+    "used_cars_24",
+    "used_cars_benz_italy",
+    "used_cars_dot_com",
+    "used_cars_pakistan",
+    "used_cars_saudi_arabia",
+    "videogame_sales",
+    "whisky",
+    "wikiliq_beer",
+    "wikiliq_spirit",
+    "wina_pl",
+    "wine_dot_com_prices",
+    "wine_dot_com_ratings",
+    "wine_enthusiasts_prices",
+    "wine_enthusiasts_ratings",
+    "wine_vivino_price",
+    "wine_vivino_rating",
+    "yelp",
+    "zomato",
+]
+
+## Dictionary of baseline methods
+carte_singletable_baselines = dict()
+carte_singletable_baselines["full"] = [
+    "carte-gnn",
+    "catboost",
+    "sentence-llm-concat-num_histgb",
+    "sentence-llm-concat-num_xgb",
+    "sentence-llm-embed-num_histgb",
+    "sentence-llm-embed-num_xgb",
+    "tablevectorizer-fasttext_histgb",
+    "tablevectorizer-fasttext_xgb",
+    "tablevectorizer-llm_histgb",
+    "tablevectorizer-llm_xgb",
+    "tablevectorizer_histgb",
+    "tablevectorizer_logistic",
+    "tablevectorizer_mlp",
+    "tablevectorizer_randomforest",
+    "tablevectorizer_resnet",
+    "tablevectorizer_ridge",
+    "tablevectorizer_xgb",
+    "tablevectorizer_tabpfn",
+    "target-encoder_histgb",
+    "target-encoder_logistic",
+    "target-encoder_mlp",
+    "target-encoder_randomforest",
+    "target-encoder_resnet",
+    "target-encoder_ridge",
+    "target-encoder_xgb",
+    "target-encoder_tabpfn",
+]
+
+carte_singletable_baselines["reduced"] = [
+    "carte-gnn",
+    "catboost",
+    "sentence-llm-concat-num_xgb",
+    "sentence-llm-embed-num_xgb",
+    "tablevectorizer_logistic",
+    "tablevectorizer_mlp",
+    "tablevectorizer_randomforest",
+    "tablevectorizer_resnet",
+    "tablevectorizer_ridge",
+    "tablevectorizer_xgb",
+    "target-encoder_tabpfn",
+]
+
+carte_multitable_baselines = [
+    "original_carte-multitable",
+    "matched_carte-multitable",
+    "original_catboost-multitable",
+    "matched_catboost-multitable",
+    "original-sentence-llm_histgb-multitable",
+    "matched-sentence-llm_histgb-multitable",
+]
+
+
+## Dictionary of method mapping
+carte_singletable_baseline_mapping = dict()
+carte_singletable_baseline_mapping["carte-gnn"] = "CARTE"
+
+# Preprocessings
+carte_singletable_baseline_mapping["tablevectorizer_"] = "TabVec-"
+carte_singletable_baseline_mapping["tablevectorizer-"] = "TabVec-"
+carte_singletable_baseline_mapping["target-encoder_"] = "TarEnc-"
+carte_singletable_baseline_mapping["fasttext_"] = "FT-"
+carte_singletable_baseline_mapping["llm_"] = "LLM-"
+carte_singletable_baseline_mapping["sentence-llm-concat-num_"] = "S-LLM-CN-"
+carte_singletable_baseline_mapping["sentence-llm-embed-num_"] = "S-LLM-EN-"
+
+# Estimators
+carte_singletable_baseline_mapping["catboost"] = "CatBoost"
+carte_singletable_baseline_mapping["xgb"] = "XGB"
+carte_singletable_baseline_mapping["histgb"] = "HGB"
+carte_singletable_baseline_mapping["randomforest"] = "RF"
+carte_singletable_baseline_mapping["ridge"] = "Ridge"
+carte_singletable_baseline_mapping["logistic"] = "Logistic"
+carte_singletable_baseline_mapping["mlp"] = "MLP"
+carte_singletable_baseline_mapping["resnet"] = "ResNet"
+carte_singletable_baseline_mapping["tabpfn"] = "TabPFN"
+
+# Bagging
+carte_singletable_baseline_mapping["bagging"] = "Bagging"
+
+## Colors for visualization
+carte_singletable_color_palette = dict()
+carte_singletable_color_palette["CARTE"] = "C3"
+carte_singletable_color_palette["CatBoost"] = "C0"
+carte_singletable_color_palette["TabVec-XGB"] = "C1"
+carte_singletable_color_palette["TabVec-RF"] = "C2"
+carte_singletable_color_palette["TabVec-Ridge"] = "C4"
+carte_singletable_color_palette["TabVec-Logistic"] = "C5"
+carte_singletable_color_palette["S-LLM-CN-XGB"] = "C6"
+carte_singletable_color_palette["S-LLM-EN-XGB"] = "C7"
+carte_singletable_color_palette["TabVec-ResNet"] = "C8"
+carte_singletable_color_palette["TabVec-MLP"] = "C9"
+carte_singletable_color_palette["TarEnc-TabPFN"] = "#A9561E"
+
+## Markers for visualization
+carte_singletable_markers = dict()
+carte_singletable_markers["CARTE"] = "o"
+carte_singletable_markers["TabVec-XGB"] = (4, 0, 45)
+carte_singletable_markers["TabVec-RF"] = "P"
+carte_singletable_markers["CatBoost"] = "X"
+carte_singletable_markers["S-LLM-CN-XGB"] = (4, 0, 0)
+carte_singletable_markers["S-LLM-EN-XGB"] = "d"
+carte_singletable_markers["TabVec-Ridge"] = "v"
+carte_singletable_markers["TabVec-Logistic"] = "v"
+carte_singletable_markers["TabVec-ResNet"] = "^"
+carte_singletable_markers["TabVec-MLP"] = "p"
+carte_singletable_markers["TarEnc-TabPFN"] = (5, 1, 0)

build/lib/carte_ai/configs/directory.py

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+from pathlib import Path
+
+# Get the base path relative to this file's location
+base_path = Path(__file__).resolve().parent.parent  # This gives '/home/infres/gbrison/carte/carte_ai'
+
+config_directory = dict()
+config_directory["base_path"] = base_path
+
+config_directory["data"] = str(base_path / "data/")
+config_directory["pretrained_model"] = str(base_path / "data/etc/kg_pretrained.pt")  # Correct path
+config_directory["data_raw"] = str(base_path / "data/data_raw/")
+config_directory["data_singletable"] = str(base_path / "data/data_singletable/")
+config_directory["data_yago"] = str(base_path / "data/data_yago/")
+config_directory["etc"] = str(base_path / "data/etc/")
+
+config_directory["results"] = str(base_path / "results/")
+config_directory["compiled_results"] = str(base_path / "results/compiled_results/")
+config_directory["visualization"] = str(base_path / "visualization/")
+
+# Specify the directory in which you have downloaded each
+config_directory["fasttext"] = str(base_path / "data/etc/cc.en.300.bin")
+config_directory["ken_embedding"] = str(base_path / "data/etc/ken_embedding.parquet")

build/lib/carte_ai/configs/model_parameters.py

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+"""
+Parameter distributions for hyperparameter optimization
+"""
+
+import numpy as np
+from scipy.stats import loguniform, randint, uniform, norm
+import copy
+
+
+class loguniform_int:
+    """Integer valued version of the log-uniform distribution"""
+
+    def __init__(self, a, b):
+        self._distribution = loguniform(a, b)
+
+    def rvs(self, *args, **kwargs):
+        """Random variable sample"""
+        return self._distribution.rvs(*args, **kwargs).astype(int)
+
+
+class norm_int:
+    """Integer valued version of the normal distribution"""
+
+    def __init__(self, a, b):
+        self._distribution = norm(a, b)
+
+    def rvs(self, *args, **kwargs):
+        """Random variable sample"""
+        if self._distribution.rvs(*args, **kwargs).astype(int) < 1:
+            return 1
+        else:
+            return self._distribution.rvs(*args, **kwargs).astype(int)
+
+
+param_distributions_total = dict()
+
+# carte-gnn
+param_distributions = dict()
+lr_grid = [1e-4, 2.5e-4, 5e-4, 7.5e-4, 1e-3]
+param_distributions["learning_rate"] = lr_grid
+param_distributions_total["carte-gnn"] = param_distributions
+
+# histgb
+param_distributions = dict()
+param_distributions["learning_rate"] = loguniform(1e-2, 10)
+param_distributions["max_depth"] = [None, 2, 3, 4]
+param_distributions["max_leaf_nodes"] = norm_int(31, 5)
+param_distributions["min_samples_leaf"] = norm_int(20, 2)
+param_distributions["l2_regularization"] = loguniform(1e-6, 1e3)
+param_distributions_total["histgb"] = param_distributions
+
+# catboost
+param_distributions = dict()
+param_distributions["max_depth"] = randint(2, 11)
+param_distributions["learning_rate"] = loguniform(1e-5, 1)
+param_distributions["bagging_temperature"] = uniform(0, 1)
+param_distributions["l2_leaf_reg"] = loguniform(1, 10)
+param_distributions["iterations"] = randint(400, 1001)
+param_distributions["one_hot_max_size"] = randint(2, 26)
+param_distributions_total["catboost"] = param_distributions
+
+# xgb
+param_distributions = dict()
+param_distributions["n_estimators"] = randint(50, 1001)
+param_distributions["max_depth"] = randint(2, 11)
+param_distributions["min_child_weight"] = loguniform(1, 100)
+param_distributions["subsample"] = uniform(0.5, 1 - 0.5)
+param_distributions["learning_rate"] = loguniform(1e-5, 1)
+param_distributions["colsample_bylevel"] = uniform(0.5, 1 - 0.5)
+param_distributions["colsample_bytree"] = uniform(0.5, 1 - 0.5)
+param_distributions["gamma"] = loguniform(1e-8, 7)
+param_distributions["lambda"] = loguniform(1, 4)
+param_distributions["alpha"] = loguniform(1e-8, 100)
+param_distributions_total["xgb"] = param_distributions
+
+# RandomForest
+param_distributions = dict()
+param_distributions["n_estimators"] = randint(50, 250)
+param_distributions["max_depth"] = [None, 2, 3, 4]
+param_distributions["max_features"] = [
+    "sqrt",
+    "log2",
+    None,
+    0.1,
+    0.2,
+    0.3,
+    0.4,
+    0.5,
+    0.6,
+    0.7,
+    0.8,
+    0.9,
+]
+param_distributions["min_samples_leaf"] = loguniform_int(0.5, 50.5)
+param_distributions["bootstrap"] = [True, False]
+param_distributions["min_impurity_decrease"] = [0.0, 0.01, 0.02, 0.05]
+param_distributions_total["randomforest"] = param_distributions
+
+
+# resnet
+param_distributions = dict()
+param_distributions["normalization"] = ["batchnorm", "layernorm"]
+param_distributions["num_layers"] = randint(1, 9)
+param_distributions["hidden_dim"] = randint(32, 513)
+param_distributions["hidden_factor"] = randint(1, 3)
+param_distributions["hidden_dropout_prob"] = uniform(0.0, 0.5)
+param_distributions["residual_dropout_prob"] = uniform(0.0, 0.5)
+param_distributions["learning_rate"] = loguniform(1e-5, 1e-2)
+param_distributions["weight_decay"] = loguniform(1e-8, 1e-2)
+param_distributions["batch_size"] = [16, 32]
+param_distributions_total["resnet"] = param_distributions
+
+# mlp
+param_distributions = dict()
+param_distributions["hidden_dim"] = [2**x for x in range(4, 11)]
+param_distributions["num_layers"] = randint(1, 5)
+param_distributions["dropout_prob"] = uniform(0.0, 0.5)
+param_distributions["learning_rate"] = loguniform(1e-5, 1e-2)
+param_distributions["weight_decay"] = loguniform(1e-8, 1e-2)
+param_distributions["batch_size"] = [16, 32]
+param_distributions_total["mlp"] = param_distributions
+
+# ridge regression
+param_distributions = dict()
+param_distributions["solver"] = ["svd", "cholesky", "lsqr", "sag"]
+param_distributions["alpha"] = loguniform(1e-5, 100)
+param_distributions_total["ridge"] = param_distributions
+
+# logistic regression
+param_distributions = dict()
+param_distributions["solver"] = ["newton-cg", "lbfgs", "liblinear"]
+param_distributions["penalty"] = ["none", "l1", "l2", "elasticnet"]
+param_distributions["C"] = loguniform(1e-5, 100)
+param_distributions_total["logistic"] = param_distributions
+
+# tabpfn
+param_distributions = dict()
+param_distributions_total["tabpfn"] = param_distributions
+
+# catboost-multitable
+param_distributions = copy.deepcopy(param_distributions_total["catboost"])
+param_distributions["source_fraction"] = uniform(0, 1)
+param_distributions_total["catboost-multitable"] = param_distributions
+
+# histgb-multitable
+param_distributions = copy.deepcopy(param_distributions_total["histgb"])
+param_distributions["source_fraction"] = uniform(0, 1)
+param_distributions_total["histgb-multitable"] = param_distributions

build/lib/carte_ai/configs/visuailization.py

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+"""
+Visualization configurations
+"""
+
+# Main models
+model_color_palette = dict()
+model_color_palette["CARTE"] = "C3"
+model_color_palette["CatBoost"] = "C0"
+model_color_palette["TabVec-XGB"] = "C1"
+model_color_palette["TabVec-RF"] = "C2"
+model_color_palette["TabVec-Ridge"] = "C4"
+model_color_palette["TabVec-Logistic"] = "C5"
+model_color_palette["S-LLM-CN-XGB"] = "C6"  # ""
+model_color_palette["S-LLM-EN-XGB"] = "C7"  # "C7" "#C875C4" mediumorchid
+model_color_palette["ResNet"] = "C8"
+model_color_palette["MLP"] = "C9"
+model_color_palette["TabPFN"] = "#A9561E"
+
+model_color_palette["TabVec-RandomForest"] = "C2"
+model_color_palette["TabVec-ResNet"] = "C8"
+model_color_palette["TabVec-MLP"] = "C9"
+model_color_palette["TarEnc-TabPFN"] = "#A9561E"
+
+
+# model_color_palette["CARTE-B"] = "C3"
+# model_color_palette["CatBoost-B"] = "C0"
+# model_color_palette["TabVec-XGB-B"] = "C1"
+# model_color_palette["TabVec-RF-B"] = "C2"
+# model_color_palette["TabVec-Ridge-B"] = "C4"
+# model_color_palette["TabVec-Logistic-B"] = "C5"
+# model_color_palette["S-LLM-CN-XGB-B"] = "C6"
+# model_color_palette["S-LLM-EN-XGB-B"] = "C7"
+# model_color_palette["ResNet-B"] = "C8"
+# model_color_palette["MLP-B"] = "C9"
+# model_color_palette["TabPFN-B"] = "#A9561E"
+
+
+# model_color_palette["TabVec-HGB"] = "#650021"
+# model_color_palette["TabVec-TabPFN"] = "#650021"
+# model_color_palette["TabVec-FT-XGB"] = "#650021"
+# model_color_palette["TabVec-FT-HGB"] = "#650021"
+
+# model_color_palette["TabLLM"] = "#653700"

build/lib/carte_ai/data/__init__.py

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+from carte_ai.data.load_data import *