Only drop the support for Python 3.7

Signed-off-by: Simon Zhao <[email protected]>

.github/workflows/sarplus.yml

@@ -39,7 +39,7 @@ jobs:
     runs-on: ubuntu-22.04
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9"]
     steps:
       - uses: actions/checkout@v3
 

recommenders/models/deeprec/models/sequential/rnn_cell_implement.py

@@ -581,9 +581,9 @@ def __init__(
     ):
         self._build_bias = build_bias
 
-        if args is None or (nest.is_nested(args) and not args):
+        if args is None or (nest.is_sequence(args) and not args):
             raise ValueError("`args` must be specified")
-        if not nest.is_nested(args):
+        if not nest.is_sequence(args):
             args = [args]
             self._is_sequence = False
         else:

recommenders/models/rlrmc/RLRMCdataset.py

@@ -68,8 +68,8 @@ def _data_processing(self, train, validation=None, test=None, mean_center=True):
         """
         # Data processing and reindexing code is adopted from https://github.com/Microsoft/Recommenders/blob/main/recommenders/models/ncf/dataset.py
         # If validation dataset is None
-        df = train if validation is None else pd.concat([train, validation])
-        df = df if test is None else pd.concat([df, test])
+        df = train if validation is None else train.append(validation)
+        df = df if test is None else df.append(test)
 
         # Reindex user and item index
         if self.user_idx is None:

recommenders/models/tfidf/tfidf_utils.py

@@ -115,7 +115,7 @@ def clean_dataframe(self, df, cols_to_clean, new_col_name="cleaned_text"):
         return df
 
     def tokenize_text(
-        self, df_clean, text_col="cleaned_text", ngram_range=(1, 3), min_df=1
+        self, df_clean, text_col="cleaned_text", ngram_range=(1, 3), min_df=0
     ):
         """Tokenize the input text.
         For more details on the TfidfVectorizer, see https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html

tests/ci/azureml_tests/submit_groupwise_azureml_pytest.py

@@ -37,6 +37,7 @@
 """
 import argparse
 import logging
+import glob
 
 from azureml.core.authentication import AzureCliAuthentication
 from azureml.core import Workspace

tests/unit/recommenders/evaluation/test_python_evaluation.py

@@ -6,7 +6,7 @@
 import pytest
 from unittest.mock import Mock
 from sklearn.preprocessing import minmax_scale
-from pandas.testing import assert_frame_equal
+from pandas.util.testing import assert_frame_equal
 
 from recommenders.utils.constants import (
     DEFAULT_USER_COL,

tests/unit/recommenders/evaluation/test_spark_evaluation.py

@@ -4,7 +4,7 @@
 import numpy as np
 import pandas as pd
 import pytest
-from pandas.testing import assert_frame_equal
+from pandas.util.testing import assert_frame_equal
 
 from recommenders.evaluation.python_evaluation import (
     precision_at_k,
@@ -441,7 +441,7 @@ def test_item_novelty(spark_diversity_data, target_metrics):
     )
     actual = evaluator.historical_item_novelty().toPandas()
     assert_frame_equal(
-        target_metrics["item_novelty"], actual, check_exact=False, atol=0.0001,
+        target_metrics["item_novelty"], actual, check_exact=False, check_less_precise=4
     )
     assert np.all(actual["item_novelty"].values >= 0)
     # Test that novelty is zero when data includes only one item
@@ -482,7 +482,7 @@ def test_user_diversity(spark_diversity_data, target_metrics):
         target_metrics["user_diversity"],
         actual,
         check_exact=False,
-        atol=0.0001,
+        check_less_precise=4,
     )
 
 
@@ -510,7 +510,7 @@ def test_user_item_serendipity(spark_diversity_data, target_metrics):
         target_metrics["user_item_serendipity"],
         actual,
         check_exact=False,
-        atol=0.0001,
+        check_less_precise=4,
     )
 
 
@@ -529,7 +529,7 @@ def test_user_serendipity(spark_diversity_data, target_metrics):
         target_metrics["user_serendipity"],
         actual,
         check_exact=False,
-        atol=0.0001,
+        check_less_precise=4,
     )
 
 
@@ -562,7 +562,7 @@ def test_user_diversity_item_feature_vector(spark_diversity_data, target_metrics
         target_metrics["user_diversity_item_feature_vector"],
         actual,
         check_exact=False,
-        atol=0.0001,
+        check_less_precise=4,
     )
 
 
@@ -599,7 +599,7 @@ def test_user_item_serendipity_item_feature_vector(
         target_metrics["user_item_serendipity_item_feature_vector"],
         actual,
         check_exact=False,
-        atol=0.0001,
+        check_less_precise=4,
     )
 
 
@@ -620,7 +620,7 @@ def test_user_serendipity_item_feature_vector(spark_diversity_data, target_metri
         target_metrics["user_serendipity_item_feature_vector"],
         actual,
         check_exact=False,
-        atol=0.0001,
+        check_less_precise=4,
     )