Fix typos in code

runtime/databricks/automl_runtime/forecast/__init__.py

@@ -77,7 +77,7 @@
     }
 }
 
-QUATERLY_OFFSET_ALIAS = [
+QUARTERLY_OFFSET_ALIAS = [
     'Q', 'QS', 'BQ', 'BQS'
 ]
 

runtime/databricks/automl_runtime/forecast/pmdarima/training.py

@@ -50,7 +50,7 @@ def __init__(self, horizon: int, frequency_unit: str, metric: str, seasonal_peri
         :param frequency_quantity: The number of frequency units in the frequency. Default is 1.
         it is the starting point of cutoffs for cross validation.
         For tuning job, it is the cutoff between train and validate split.
-        For training job, it is the cutoff bewteen validate and test split.
+        For training job, it is the cutoff between validate and test split.
         """
         self._horizon = horizon
         self._frequency_unit = OFFSET_ALIAS_MAP[frequency_unit]

runtime/databricks/automl_runtime/forecast/prophet/forecast.py

@@ -117,7 +117,7 @@ def __init__(self, horizon: int, frequency_unit: str, metric: str, interval_widt
         :param split_cutoff: Optional cutoff specified by user. If provided, 
         it is the starting point of cutoffs for cross validation.
         For tuning job, it is the cutoff between train and validate split.
-        For training job, it is the cutoff bewteen validate and test split.
+        For training job, it is the cutoff between validate and test split.
         :param prophet_kwargs: Optional keyword arguments for Prophet model.
             For information about the parameters see:
             `The Prophet source code <https://github.com/facebook/prophet/blob/master/python/prophet/forecaster.py>`_.

runtime/databricks/automl_runtime/forecast/prophet/model.py

@@ -27,7 +27,7 @@
 from databricks.automl_runtime.forecast import OFFSET_ALIAS_MAP, DATE_OFFSET_KEYWORD_MAP
 from databricks.automl_runtime.forecast.model import ForecastModel, mlflow_forecast_log_model
 from databricks.automl_runtime import version
-from databricks.automl_runtime.forecast.utils import is_quaterly_alias, make_future_dataframe, apply_preprocess_func
+from databricks.automl_runtime.forecast.utils import is_quarterly_alias, make_future_dataframe, apply_preprocess_func
 
 
 PROPHET_ADDITIONAL_PIP_DEPS = [
@@ -71,7 +71,7 @@ def __init__(self,
         self._frequency_unit = frequency_unit
         self._frequency_quantity = frequency_quantity
         self._time_col = time_col
-        self._is_quaterly = is_quaterly_alias(frequency_unit)
+        self._is_quarterly = is_quarterly_alias(frequency_unit)
         self._split_col = split_col
         self._preprocess_func = preprocess_func
         super().__init__()

runtime/databricks/automl_runtime/forecast/utils.py

@@ -16,7 +16,7 @@
 import logging
 from typing import Dict, List, Optional, Tuple, Union
 from databricks.automl_runtime.forecast import DATE_OFFSET_KEYWORD_MAP,\
-    QUATERLY_OFFSET_ALIAS, NON_DAILY_OFFSET_ALIAS, OFFSET_ALIAS_MAP, PERIOD_ALIAS_MAP
+    QUARTERLY_OFFSET_ALIAS, NON_DAILY_OFFSET_ALIAS, OFFSET_ALIAS_MAP, PERIOD_ALIAS_MAP
 
 import pandas as pd
 
@@ -155,7 +155,7 @@ def generate_cutoffs(df: pd.DataFrame, horizon: int, frequency_unit: str,
     """
     period = max(0.5 * horizon, 1)  # avoid empty cutoff buckets
 
-    # avoid non-integer months, quaters ands years.
+    # avoid non-integer months, quarters ands years.
     if frequency_unit in NON_DAILY_OFFSET_ALIAS:
         period = int(period)
         period_dateoffset = pd.DateOffset(**DATE_OFFSET_KEYWORD_MAP[frequency_unit])*frequency_quantity*period
@@ -208,15 +208,15 @@ def generate_custom_cutoffs(df: pd.DataFrame, horizon: int, frequency_unit: str,
     :param split_cutoff: the user-specified cutoff, as the starting point of cutoffs.
     :param frequency_quantity: frequency quantity of the time series.
     For tuning job, it is the cutoff between train and validate split.
-    For training job, it is the cutoff bewteen validate and test split.
+    For training job, it is the cutoff between validate and test split.
     :return: list of pd.Timestamp cutoffs for cross-validation.
     """
     # TODO: [ML-43528] expose period as input.
     period = 1 
     period_dateoffset = pd.DateOffset(**DATE_OFFSET_KEYWORD_MAP[frequency_unit])*period*frequency_quantity
     horizon_dateoffset = pd.DateOffset(**DATE_OFFSET_KEYWORD_MAP[frequency_unit])*horizon*frequency_quantity
 
-    # First cutoff is the cutoff bewteen splits
+    # First cutoff is the cutoff between splits
     cutoff = split_cutoff
     result = []
     max_cutoff = max(df["ds"])
@@ -230,8 +230,8 @@ def generate_custom_cutoffs(df: pd.DataFrame, horizon: int, frequency_unit: str,
         cutoff += period_dateoffset
     return result
 
-def is_quaterly_alias(freq: str):
-    return freq in QUATERLY_OFFSET_ALIAS
+def is_quarterly_alias(freq: str):
+    return freq in QUARTERLY_OFFSET_ALIAS
 
 def is_frequency_consistency(
                 start_time: pd.Timestamp,

runtime/tests/automl_runtime/forecast/prophet/model_test.py

@@ -143,7 +143,7 @@ def preprocess_func(df):
 
     def test_make_future_dataframe(self):
         for feq_unit in OFFSET_ALIAS_MAP:
-            # Temporally disable the year, month and quater since we
+            # Temporally disable the year, month and quarter since we
             # don't have full support yet.
             if OFFSET_ALIAS_MAP[feq_unit] in ['YS', 'MS', 'QS']:
                 continue

runtime/tests/automl_runtime/forecast/utils_test.py

@@ -71,7 +71,7 @@ def test_truncate(self):
         validation_horizon = get_validation_horizon(df, 17, "YS")
         self.assertEqual(validation_horizon, 2)
 
-        # for dataframe with 12 quaters of data, maximum horizon is 3 quaters.
+        # for dataframe with 12 quarters of data, maximum horizon is 3 quarters.
         df = pd.DataFrame(pd.date_range(start="2012-01-14", periods=13, freq=pd.DateOffset(months=3)), columns=["ds"])
         validation_horizon = get_validation_horizon(df, 17, "QS")
         self.assertEqual(validation_horizon, 3)
@@ -197,7 +197,7 @@ def test_generate_cutoffs_success_monthly(self):
         cutoffs = generate_cutoffs(df, horizon=2, frequency_unit="MS", num_folds=3, seasonal_period=1)
         self.assertEqual([pd.Timestamp('2021-08-12 00:00:00'), pd.Timestamp('2021-9-12 00:00:00'), pd.Timestamp('2021-10-12 00:00:00')], cutoffs)
 
-    def test_generate_cutoffs_success_quaterly(self):
+    def test_generate_cutoffs_success_quarterly(self):
         df = pd.DataFrame(
             pd.date_range(start="2020-07-12", periods=9, freq=pd.DateOffset(months=3)), columns=["ds"]
         ).rename_axis("y").reset_index()
@@ -277,14 +277,14 @@ def test_generate_custom_cutoffs_success_monthly(self):
         cutoffs = generate_custom_cutoffs(df, horizon=7, frequency_unit="MS", split_cutoff=pd.Timestamp('2021-03-12 00:00:00'))
         self.assertEqual([pd.Timestamp('2021-03-12 00:00:00'), pd.Timestamp('2021-04-12 00:00:00'), pd.Timestamp('2021-05-12 00:00:00')], cutoffs)
 
-    def test_generate_custom_cutoffs_success_quaterly(self):
+    def test_generate_custom_cutoffs_success_quarterly(self):
         df = pd.DataFrame(
             pd.date_range(start="2020-07-12", periods=9, freq=pd.DateOffset(months=3)), columns=["ds"]
         ).rename_axis("y").reset_index()
         cutoffs = generate_custom_cutoffs(df, horizon=7, frequency_unit="QS", split_cutoff=pd.Timestamp('2020-07-12 00:00:00'))
         self.assertEqual([pd.Timestamp('2020-07-12 00:00:00'), pd.Timestamp('2020-10-12 00:00:00')], cutoffs)
 
-    def test_generate_custom_cutoffs_success_quaterly_end(self):
+    def test_generate_custom_cutoffs_success_quarterly_end(self):
         df = pd.DataFrame(
             pd.date_range(start="2020-03-31", periods=3, freq=pd.DateOffset(months=3)), columns=["ds"]
         ).rename_axis("y").reset_index()