Merge pull request #127 from amosproj/110-docs

110 docs

docs/sdk/code-reference/pipelines/data_quality/data_manipulation/spark/prediction/arima.md

@@ -0,0 +1 @@
+::: src.sdk.python.rtdip_sdk.pipelines.data_quality.data_manipulation.spark.prediction.arima

docs/sdk/code-reference/pipelines/data_quality/data_manipulation/spark/prediction/auto_arima.md

@@ -0,0 +1 @@
+::: src.sdk.python.rtdip_sdk.pipelines.data_quality.data_manipulation.spark.prediction.auto_arima

mkdocs.yml

@@ -236,18 +236,34 @@ nav:
               - Deploy:
                   - Databricks: sdk/code-reference/pipelines/deploy/databricks.md
               - Data Quality:
-                  - Monitoring: 
+                  - Monitoring:
                       - Check Value Ranges: sdk/code-reference/pipelines/data_quality/monitoring/spark/check_value_ranges.md
-                      - Great Expectations: 
-                        - Data Quality Monitoring: sdk/code-reference/pipelines/data_quality/monitoring/spark/great_expectations.md
+                      - Great Expectations:
+                          - Data Quality Monitoring: sdk/code-reference/pipelines/data_quality/monitoring/spark/great_expectations.md
                       - Flatline Detection: sdk/code-reference/pipelines/data_quality/monitoring/spark/flatline_detection.md
                       - Identify Missing Data:
                         - Interval Based: sdk/code-reference/pipelines/data_quality/monitoring/spark/identify_missing_data_interval.md
                         - Pattern Based: sdk/code-reference/pipelines/data_quality/monitoring/spark/identify_missing_data_pattern.md
-                  - Data Manipulation: 
+                  - Data Manipulation:
                       - Duplicate Detection: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/duplicate_detection.md
-                      - Filter Out of Range Values: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/delete_out_of_range_values.md
+                      - Filter Out of Range Values: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/out_of_range_value_filter.md
                       - Flatline Filter: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/flatline_filter.md
+                      - Dimensionality Reduction: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/dimensionality_reduction.md
+                      - Interval Filtering: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/interval_filtering.md
+                      - K-Sigma Anomaly Detection: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/k_sigma_anomaly_detection.md
+                      - Missing Value Imputation: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/missing_value_imputation.md
+                      - Normalization:
+                          - Normalization: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/normalization/normalization.md
+                          - Normalization Mean: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/normalization/normalization_mean.md
+                          - Normalization MinMax: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/normalization/normalization_minmax.md
+                          - Normalization ZScore: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/normalization/normalization_zscore.md
+                      - Prediction:
+                          - Arima: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/prediction/arima.md
+                          - Auto Arima: sdk/code-reference/pipelines/data_quality/data_manipulation/spark/prediction/auto_arima.md
+              - Machine Learning:
+                  - Data Binning: sdk/code-reference/pipelines/machine_learning/spark/data_binning.md
+                  - Linear Regression: sdk/code-reference/pipelines/machine_learning/spark/linear_regression.md
+
           - Jobs: sdk/pipelines/jobs.md
           - Deploy:
               - Databricks Workflows: sdk/pipelines/deploy/databricks.md
@@ -339,4 +355,4 @@ nav:
       - blog/index.md
   - University:
       - University: university/overview.md
-
+

src/sdk/python/rtdip_sdk/pipelines/data_quality/data_manipulation/spark/prediction/arima.py

@@ -99,7 +99,6 @@ class ArimaPrediction(DataManipulationBaseInterface, InputValidator):
         timestamp_name (str): Name of column, where event timestamps are stored
         source_name (str): Name of column in source-based format, where source of events are stored
         status_name (str): Name of column in source-based format, where status of events are stored
-        # Options for ARIMA
         external_regressor_names (List[str]): Currently not working. Names of the columns with data to use for prediction, but not extend
         number_of_data_points_to_predict (int): Amount of points to forecast
         number_of_data_points_to_analyze (int): Amount of most recent points to train on
@@ -319,7 +318,7 @@ def filter(self) -> PySparkDataFrame:
         value imputation to prevent learning on dirty data.
 
         Returns:
-            DataFrame: A PySpark DataFrame with forcasted value entries depending on constructor parameters.
+            DataFrame: A PySpark DataFrame with forecasted value entries depending on constructor parameters.
         """
         # expected_scheme = StructType(
         #    [

src/sdk/python/rtdip_sdk/pipelines/data_quality/data_manipulation/spark/prediction/auto_arima.py

@@ -77,8 +77,6 @@ class ArimaAutoPrediction(ArimaPrediction):
         number_of_data_points_to_predict (int): Amount of points to forecast
         number_of_data_points_to_analyze (int): Amount of most recent points to train on
         seasonal (bool): Setting for AutoArima, is past_data seasonal?
-        # Options for ARIMA
-        trend (str): ARIMA-Specific setting
         enforce_stationarity (bool): ARIMA-Specific setting
         enforce_invertibility (bool): ARIMA-Specific setting
         concentrate_scale (bool): ARIMA-Specific setting