Merge pull request #60 from mindsdb/staging

Release 0.0.18

Author: paxcema

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "type_infer"
-version = "0.0.17"
+version = "0.0.18"
 description = "Automated type inference for Machine Learning pipelines."
 authors = ["MindsDB Inc. <[email protected]>"]
 license = "GPL-3.0"
@@ -15,12 +15,17 @@ numpy = "^1.15"
 pandas = "^2"
 dataclasses-json = "^0.6.3"
 colorlog = "^6.5.0"
-langid = "^1.1.6"
-nltk = "^3"
-toml = "^0.10.2"
 psutil = "^5.9.0"
+toml = "^0.10.2"
 
+# rule based deps, part of core
+langid = "^1.1.6"
+nltk = "^3"
 
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
+
+# TODO: update once this engine is introduced
+[tool.poetry.extras]
+# bert = ["torch"]

tests/integration_tests/test_type_infer.py renamed to tests/integration_tests/test_rule_based.py

@@ -5,13 +5,14 @@
 from datetime import datetime, timedelta
 
 from type_infer.dtype import dtype
-from type_infer.infer import infer_types
+from type_infer.api import infer_types
 
 
-class TestTypeInference(unittest.TestCase):
+class TestRuleBasedTypeInference(unittest.TestCase):
     def test_0_airline_sentiment(self):
         df = pd.read_csv("tests/data/airline_sentiment_sample.csv")
-        inferred_types = infer_types(df, pct_invalid=0)
+        config = {'engine': 'rule_based', 'pct_invalid': 0, 'seed': 420, 'mp_cutoff': 1e4}
+        inferred_types = infer_types(df, config=config)
 
         expected_types = {
             'airline_sentiment': 'categorical',
@@ -44,6 +45,7 @@ def test_0_airline_sentiment(self):
 
     def test_1_stack_overflow_survey(self):
         df = pd.read_csv("tests/data/stack_overflow_survey_sample.csv")
+        config = {'engine': 'rule_based', 'pct_invalid': 0, 'seed': 420, 'mp_cutoff': 1e4}
 
         expected_types = {
             'Respondent': 'integer',
@@ -68,7 +70,7 @@ def test_1_stack_overflow_survey(self):
             'Professional': 'No Information'
         }
 
-        inferred_types = infer_types(df, pct_invalid=0)
+        inferred_types = infer_types(df, config=config)
 
         for col in expected_types:
             self.assertTrue(expected_types[col], inferred_types.dtypes[col])
@@ -90,7 +92,10 @@ def test_2_simple(self):
         # manual tinkering
         df['float'].iloc[-n_corrupted:] = 'random string'
 
-        inferred_types = infer_types(df, pct_invalid=100 * (n_corrupted) / n_points)
+        pct_invalid = 100 * (n_corrupted) / n_points
+        config = {'engine': 'rule_based', 'pct_invalid': pct_invalid, 'seed': 420, 'mp_cutoff': 1e4}
+
+        inferred_types = infer_types(df, config=config)
         expected_types = {
             'date': dtype.date,
             'datetime': dtype.datetime,

tests/unit_tests/rule_based/__init__.py

@@ -1,7 +1,9 @@
 import unittest
 
 from type_infer.dtype import dtype
-from type_infer.infer import type_check_date
+from type_infer.rule_based.core import RuleBasedEngine
+
+type_check_date = RuleBasedEngine.type_check_date
 
 
 class TestDates(unittest.TestCase):

tests/unit_tests/test_dates.py renamed to tests/unit_tests/rule_based/test_dates.py

@@ -0,0 +1,22 @@
+import unittest
+import random
+
+import pandas as pd
+from type_infer.rule_based.core import RuleBasedEngine
+from type_infer.dtype import dtype
+
+get_column_data_type = RuleBasedEngine.get_column_data_type
+
+
+class TestInferDtypes(unittest.TestCase):
+    def test_negative_integers(self):
+        data = pd.DataFrame([-random.randint(-10, 10) for _ in range(100)], columns=['test_col'])
+        engine = RuleBasedEngine()
+        dtyp, dist, ainfo, warn, info = engine.get_column_data_type(data['test_col'], data, 'test_col', 0.0)
+        self.assertEqual(dtyp, dtype.integer)
+
+    def test_negative_floats(self):
+        data = pd.DataFrame([float(-random.randint(-10, 10)) for _ in range(100)] + [0.1], columns=['test_col'])
+        engine = RuleBasedEngine()
+        dtyp, dist, ainfo, warn, info = engine.get_column_data_type(data['test_col'], data, 'test_col', 0.0)
+        self.assertEqual(dtyp, dtype.float)

tests/unit_tests/rule_based/test_infer_dtypes.py

@@ -0,0 +1,12 @@
+import unittest
+
+from type_infer.rule_based.helpers import tokenize_text
+
+
+class TestDates(unittest.TestCase):
+    def test_get_tokens(self):
+        sentences = ['hello, world!', ' !hello! world!!,..#', '#hello!world']
+        for sent in sentences:
+            assert list(tokenize_text(sent)) == ['hello', 'world']
+
+        assert list(tokenize_text("don't wouldn't")) == ['do', 'not', 'would', 'not']

tests/unit_tests/rule_based/test_misc.py

@@ -3,7 +3,6 @@
 from pathlib import Path
 
 import type_infer
-from type_infer.helpers import tokenize_text
 
 
 class TestDates(unittest.TestCase):
@@ -19,10 +18,3 @@ def test_versions_are_in_sync(self):
         package_init_version = type_infer.__version__
 
         self.assertEqual(package_init_version, pyproject_version)
-
-    def test_get_tokens(self):
-        sentences = ['hello, world!', ' !hello! world!!,..#', '#hello!world']
-        for sent in sentences:
-            assert list(tokenize_text(sent)) == ['hello', 'world']
-
-        assert list(tokenize_text("don't wouldn't")) == ['do', 'not', 'would', 'not']

tests/unit_tests/test_infer_dtypes.py

@@ -1,10 +1,12 @@
 from type_infer import base
 from type_infer import dtype
-from type_infer import infer
+from type_infer import api
 from type_infer import helpers
 
+__version__ = '0.0.18'
 
-__version__ = '0.0.17'
 
-
-__all__ = ['base', 'dtype', 'infer', 'helpers', '__version__']
+__all__ = [
+    '__version__',
+    'base', 'dtype', 'api', 'helpers',
+]

tests/unit_tests/test_misc.py

@@ -0,0 +1,40 @@
+from typing import Dict, Optional
+import pandas as pd
+
+from type_infer.base import TypeInformation, ENGINES
+from type_infer.rule_based.core import RuleBasedEngine
+
+
+def infer_types(
+        data: pd.DataFrame,
+        config: Optional[Dict] = None
+) -> TypeInformation:
+    """
+    Infers the data types of each column of the dataset by analyzing a small sample of
+    each column's items.
+
+    Inputs
+    ----------
+    data : pd.DataFrame
+        The input dataset for which we want to infer data type information.
+    """
+    # Set global defaults if missing
+    if config is None:
+        config = {'engine': 'rule_based', 'pct_invalid': 2, 'seed': 420, 'mp_cutoff': 1e4}
+    elif 'engine' not in config:
+        config['engine'] = 'rule_based'
+
+    if 'pct_invalid' not in config:
+        config['pct_invalid'] = 2
+
+    if 'seed' not in config:
+        config['seed'] = 420
+
+    if config['engine'] == ENGINES.RULE_BASED:
+        if 'mp_cutoff' not in config:
+            config['mp_cutoff'] = 1e4
+
+        engine = RuleBasedEngine(config)
+        return engine.infer(data)
+    else:
+        raise Exception(f'Unknown engine {config["engine"]}')