Merge pull request #31 from artefactory/suggestions

Added Suggestions
artefactory · Mar 14, 2024 · bd7f59b · bd7f59b
2 parents 8864d44 + 2951eb2
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diff --git a/LICENSE.md b/LICENSE.md
@@ -0,0 +1,22 @@
+The MIT License (MIT)
+
+Copyright (c) 2023 The choice-learn developers, artefactory
+All rights reserved.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
@@ -14,7 +14,7 @@
 <img src="docs/choice_learn_official_logo.png" width="256">
 
 Choice-Learn is a Python package designed to help you build discrete choice models.
-The package provides ready to use datasets and different models from the litterature. It also provides a lower level use if you want to customize any model or create your own from scratch. In particular you will find smart datasets handling to limit RAM usage and different structure commons to any choice model.
+The package provides ready-to-use datasets and models from the litterature. It also provides a lower level use if you want to customize any model or create your own from scratch. In particular you will find efficient data handling to limit RAM usage and structure commons to any choice model.
 
 Choice-Learn uses NumPy and pandas as data backend engines and TensorFlow for models.
 
@@ -55,7 +55,7 @@ If you are new to choice modelling, you can check this [resource](https://www.pu
   - Conditional MultiNomialLogit [[4]](#citation)[[Example]](https://github.com/artefactory/choice-learn-private/blob/main/notebooks/choice_learn_introduction_clogit.ipynb)
   - Latent Class MultiNomialLogit [[Example]](https://github.com/artefactory/choice-learn-private/blob/main/notebooks/latent_class_model.ipynb)
   - RUMnet [[1]](#citation)[[Example]](https://github.com/artefactory/choice-learn-private/blob/main/notebooks/rumnet_example.ipynb)
-- Ready-to-use models to be implemented:
+- (WIP) - Ready-to-use models to be implemented:
   - Nested MultiNomialLogit
   - [TasteNet](https://arxiv.org/abs/2002.00922)
   - [SHOPPER](https://projecteuclid.org/journals/annals-of-applied-statistics/volume-14/issue-1/SHOPPER--A-probabilistic-model-of-consumer-choice-with-substitutes/10.1214/19-AOAS1265.full)
@@ -102,6 +102,10 @@ For modelling you need:
 Finally, an optional requirement used for report and LBFG-S optimization is:
 - TensorFlow Probability (>=0.20.1)
 
+Once you have created your conda/pip python==3.9 environment, you can install requirements by:
+```bash
+pip install choice-learn
+```
 ## Usage
 ```python
 from choice_learn.data import ChoiceDataset
@@ -160,6 +164,14 @@ A detailed documentation of this project is available [here](https://artefactory
 
 ## Citation
 
+If you consider this package and any of its feature useful for your research, please cite our paper:
+
+(WIP - Paper to come)
+
+### License
+
+The use of this software is under the MIT license, with no limitation of usage, including for commercial applications.
+
 ### Contributors
 ### Special Thanks
 
@@ -170,7 +182,7 @@ A detailed documentation of this project is available [here](https://artefactory
 [2][The Acceptance of Model Innovation: The Case of Swissmetro](https://www.researchgate.net/publication/37456549_The_acceptance_of_modal_innovation_The_case_of_Swissmetro), Bierlaire, M.; Axhausen, K., W.; Abay, G. (2001)\
 [3][Applications and Interpretation of Nested Logit Models of Intercity Mode Choice](https://trid.trb.org/view/385097), Forinash, C., V.; Koppelman, F., S. (1993)\
 [4][The Demand for Local Telephone Service: A Fully Discrete Model of Residential Calling Patterns and Service Choices](https://www.jstor.org/stable/2555538), Train K., E.; McFadden, D., L.; Moshe, B. (1987)\
-[5] [Estimation of Travel Choice Models with Randomly Distributed Values of Time](https://ideas.repec.org/p/fth/lavaen/9303.html), Ben-Akiva M; Bolduc D; Bradley M(1993)\
+[5] [Estimation of Travel Choice Models with Randomly Distributed Values of Time](https://ideas.repec.org/p/fth/lavaen/9303.html), Ben-Akiva, M.; Bolduc, D.; Bradley, M. (1993)\
 [6] [Personalize Expedia Hotel Searches - ICDM 2013](https://www.kaggle.com/c/expedia-personalized-sort), Ben Hamner, A.; Friedman, D.; SSA_Expedia. (2013)
 
 ### Code and Repositories

diff --git a/choice_learn/data/choice_dataset.py b/choice_learn/data/choice_dataset.py
@@ -363,7 +363,6 @@ def _build_features_by_ids(self):
             indexes and features_by_id of contexts_items_features
         """
         if len(self.features_by_ids) == 0:
-            print("No features_by_ids given.")
             return {}, {}, {}
 
         if (
@@ -629,6 +628,21 @@ def __len__(self):
         """
         return len(self.choices)
 
+    def __str__(self):
+        """Returns short representation of ChoiceDataset.
+
+        Returns:
+        --------
+        str
+            short representation of ChoiceDataset
+        """
+        template = """First choice is:\nItems features: {}\nContexts features: {}\n
+                      Contexts Items features: {}\nContexts Items Availabilities: {}\n
+                      Contexts Choice: {}"""
+        return template.format(
+            self.batch[0][0], self.batch[0][1], self.batch[0][2], self.batch[0][3], self.batch[0][4]
+        )
+
     def get_n_items(self):
         """Method to access the total number of different items.
 
@@ -741,7 +755,7 @@ def from_single_wide_df(
         contexts_items_availabilities_prefix=None,
         delimiter="_",
         choices_column="choice",
-        choice_mode="items_id",
+        choice_format="items_id",
     ):
         """Builds numpy arrays for ChoiceDataset from a single dataframe in wide format.
 
@@ -770,7 +784,7 @@ def from_single_wide_df(
             default is "_"
         choice_column: str, optional
             Name of the column containing the choices, default is "choice"
-        choice_mode: str, optional
+        choice_format: str, optional
             How choice is indicated in df, either "items_name" or "items_index",
             default is "items_id"
 
@@ -853,7 +867,7 @@ def from_single_wide_df(
             contexts_items_features = []
             for item in items_id:
                 columns = [
-                    f"{feature}{delimiter}{item}" for feature in contexts_items_features_suffixes
+                    f"{feature}{delimiter}{item}" for feature in contexts_items_features_prefixes
                 ]
                 for col in columns:
                     if col not in df.columns:
@@ -901,10 +915,11 @@ def from_single_wide_df(
             contexts_items_availabilities = None
 
         choices = df[choices_column].to_numpy()
-        if choice_mode == "items_id":
+        if choice_format == "items_id":
             if items_id is None:
-                raise ValueError("items_id must be given to use choice_mode 'items_id'")
+                raise ValueError("items_id must be given to use choice_format='items_id'")
             items_id = np.array(items_id)
+
             choices = np.squeeze([np.where(items_id == c)[0] for c in choices])
             if choices.shape[0] == 0:
                 raise ValueError("No choice found in the items_id list")
@@ -922,33 +937,33 @@ def from_single_wide_df(
     def from_single_long_df(
         cls,
         df,
+        choices_column="choice",
+        items_id_column="item_id",
+        contexts_id_column="context_id",
         fixed_items_features_columns=None,
         contexts_features_columns=None,
         contexts_items_features_columns=None,
-        items_id_column="item_id",
-        contexts_id_column="context_id",
-        choices_column="choice",
-        choice_mode="items_id",
+        choice_format="items_id",
     ):
         """Builds numpy arrays for ChoiceDataset from a single dataframe in long format.
 
         Parameters
         ----------
         df : pandas.DataFrame
             dataframe in Long format
+        choices_column: str, optional
+            Name of the column containing the choices, default is "choice"
+        items_id_column: str, optional
+            Name of the column containing the item ids, default is "items_id"
+        contexts_id_column: str, optional
+            Name of the column containing the sessions ids, default is "contexts_id"
         fixed_items_features_columns : list
             Columns of the dataframe that are item features, default is None
         contexts_features_columns : list
             Columns of the dataframe that are contexts features, default is None
         contexts_items_features_columns : list
             Columns of the dataframe that are context-item features, default is None
-        items_id_column: str, optional
-            Name of the column containing the item ids, default is "items_id"
-        contexts_id_column: str, optional
-            Name of the column containing the sessions ids, default is "contexts_id"
-        choices_column: str, optional
-            Name of the column containing the choices, default is "choice"
-        choice_mode: str, optional
+        choice_format: str, optional
             How choice is indicated in df, either "items_name" or "one_zero",
             default is "items_id"
 
@@ -1000,13 +1015,13 @@ def from_single_long_df(
             else None
         )
 
-        if choice_mode == "items_id":
+        if choice_format == "items_id":
             choices = df[[choices_column, contexts_id_column]].drop_duplicates(contexts_id_column)
             choices = choices.set_index(contexts_id_column)
             choices = choices.loc[sessions].to_numpy()
             # items is the value (str) of the item
             choices = np.squeeze([np.where(items == c)[0] for c in choices])
-        elif choice_mode == "one_zero":
+        elif choice_format == "one_zero":
             choices = df[[items_id_column, choices_column, contexts_id_column]]
             choices = choices.loc[choices[choices_column] == 1]
             choices = choices.set_index(contexts_id_column)
@@ -1017,7 +1032,7 @@ def from_single_long_df(
             )
         else:
             raise ValueError(
-                f"choice_mode {choice_mode} not recognized. Must be in ['items_id', 'one_zero']"
+                f"choice_format {choice_format} not recognized. Must be in ['items_id', 'one_zero']"
             )
         return ChoiceDataset(
             fixed_items_features=items_features,

diff --git a/choice_learn/datasets/base.py b/choice_learn/datasets/base.py
@@ -1,13 +1,15 @@
 """Datasets loader."""
 import csv
 import gzip
+import os
 from importlib import resources
 
 import numpy as np
 import pandas as pd
 
 from choice_learn.data.choice_dataset import ChoiceDataset
 
+OS_DATA_MODULE = os.path.join(os.path.abspath(".."), "choice_learn", "datasets", "data")
 DATA_MODULE = "choice_learn.datasets.data"
 
 
@@ -36,7 +38,7 @@ def get_path(data_file_name, module=DATA_MODULE):
         return path
 
 
-def load_csv(data_file_name, data_module=DATA_MODULE, encoding="utf-8"):
+def load_csv(data_file_name, data_module=OS_DATA_MODULE, encoding="utf-8"):
     """Base function to load csv files.
 
     Parameters
@@ -55,8 +57,7 @@ def load_csv(data_file_name, data_module=DATA_MODULE, encoding="utf-8"):
     np.ndarray
         data contained in the csv file
     """
-    data_path = resources.files(data_module)
-    with (data_path / data_file_name).open("r", encoding=encoding) as csv_file:
+    with open(os.path.join(data_module, data_file_name), "r", encoding=encoding) as csv_file:
         data_file = csv.reader(csv_file)
         names = next(data_file)
         data = []
@@ -66,7 +67,7 @@ def load_csv(data_file_name, data_module=DATA_MODULE, encoding="utf-8"):
     return names, np.stack(data)
 
 
-def load_gzip(data_file_name, data_module=DATA_MODULE, encoding="utf-8"):
+def load_gzip(data_file_name, data_module=OS_DATA_MODULE, encoding="utf-8"):
     """Base function to load zipped .csv.gz files.
 
     Parameters
@@ -85,8 +86,7 @@ def load_gzip(data_file_name, data_module=DATA_MODULE, encoding="utf-8"):
     np.ndarray
         data contained in the csv file
     """
-    data_path = resources.files(data_module)
-    with (data_path / data_file_name).open("rb") as compressed_file:
+    with open(os.path.join(data_module, data_file_name), "rb") as compressed_file:
         compressed_file = gzip.open(compressed_file, mode="rt", encoding=encoding)
         names = next(compressed_file)
         names = names.replace("\n", "")
@@ -363,7 +363,7 @@ def load_swissmetro(add_items_one_hot=False, as_frame=False, return_desc=False,
         contexts_items_features_suffixes=contexts_items_features_names,
         contexts_items_availabilities_suffix=availabilities_column,
         choices_column=choice_column,
-        choice_mode="item_index",
+        choice_format="item_index",
     )
 
 
@@ -372,7 +372,7 @@ def load_modecanada(
     add_is_public=False,
     as_frame=False,
     return_desc=False,
-    choice_mode="one_zero",
+    choice_format="one_zero",
     split_features=False,
     to_wide=False,
     preprocessing=None,
@@ -392,8 +392,8 @@ def load_modecanada(
         by default False.
     return_desc : bool, optional
         Whether to return the description, by default False.
-    choice_mode : str, optional, among ["one_zero", "items_id"]
-        mode indicating how the choice is encoded, by default "one_zero".
+    choice_format : str, optional, among ["one_zero", "items_id"]
+        format indicating how the choice is encoded, by default "one_zero".
     split_features : bool, optional
         Whether to split features by type in different dataframes, by default False.
     to_wide : bool, optional
@@ -461,7 +461,7 @@ def load_modecanada(
     for col in canada_df.columns:
         canada_df[col] = pd.to_numeric(canada_df[col], errors="ignore")
 
-    if choice_mode == "items_id":
+    if choice_format == "items_id":
         # We need to transform how the choice is encoded to add the chosen item id
         named_choice = [0] * len(canada_df)
         for n_row, row in canada_df.iterrows():
@@ -565,7 +565,7 @@ def load_modecanada(
             items_id_column="alt",
             contexts_id_column="case",
             choices_column="choice",
-            choice_mode="one_zero",
+            choice_format="one_zero",
         )
 
     return ChoiceDataset.from_single_long_df(
@@ -576,7 +576,7 @@ def load_modecanada(
         items_id_column="alt",
         contexts_id_column="case",
         choices_column=choice_column,
-        choice_mode="one_zero",
+        choice_format="one_zero",
     )
 
 
@@ -706,7 +706,7 @@ def load_electricity(
         contexts_items_features_columns=["pf", "cl", "loc", "wk", "tod", "seas"],
         items_id_column="alt",
         contexts_id_column="chid",
-        choice_mode="one_zero",
+        choice_format="one_zero",
     )
 
 
@@ -750,6 +750,7 @@ def load_train(
     if as_frame:
         return train_df
     train_df["choice"] = train_df.apply(lambda row: row.choice[-1], axis=1)
+    """
     train_df = train_df.rename(
         columns={
             "price1": "1_price",
@@ -766,14 +767,15 @@ def load_train(
             "comfort2": "2_comfort",
         }
     )
-
+    """
     return ChoiceDataset.from_single_wide_df(
         df=train_df,
         items_id=["1", "2"],
         fixed_items_suffixes=None,
         contexts_features_columns=["id"],
-        contexts_items_features_suffixes=["price", "time", "change", "comfort"],
+        contexts_items_features_prefixes=["price", "time", "change", "comfort"],
+        delimiter="",
         contexts_items_availabilities_suffix=None,
         choices_column="choice",
-        choice_mode="items_id",
+        choice_format="items_id",
     )