Merge pull request #10 from enriquea/main

added fs pipeline & code clean-up

.gitignore

@@ -132,3 +132,4 @@ dmypy.json
 local/
 testscripts/
 .idea/*
+/benchmarking/

docs/README.methods.md

@@ -53,75 +53,4 @@ A typical workflow written using `fsspark` can be divided roughly in four major
 
 ### 5. Feature selection pipeline example
 
-Check it out [here](fsspark/pipeline/fs_corr_rf.py) a full FS pipeline example.
-
-```python
-"""
-Example of a feature selection pipeline implemented in fsspark.
-
-After data import and pre-processing, the pipeline applies univariate correlation filter,
-multivariate correlation filter and Randon Forest classification.
-
-"""
-
-from fsspark.config.context import init_spark, stop_spark_session
-from fsspark.fs.core import FSDataFrame
-from fsspark.fs.ml import cv_rf_classification, get_accuracy, get_predictions
-from fsspark.fs.multivariate import multivariate_filter
-from fsspark.fs.univariate import univariate_filter
-from fsspark.fs.utils import (remove_features_by_missingness_rate,
-                              impute_missing)
-from fsspark.utils.datasets import get_tnbc_data_path
-from fsspark.utils.io import import_table_as_psdf
-
-# Init spark
-init_spark()
-
-# Import data
-fsdf = import_table_as_psdf(get_tnbc_data_path(),
-                            n_partitions=5)
-
-fsdf = FSDataFrame(fsdf, sample_col='Sample', label_col='label')
-
-# Step 1. Data pre-processing.
-
-# a) Filter missingness rate
-fsdf = remove_features_by_missingness_rate(fsdf, threshold=0.1)
-
-# b) Impute data frame
-fsdf = impute_missing(fsdf)
-
-# c) Scale features
-fsdf = fsdf.scale_features(scaler_method='standard')
-
-# Split dataset in training/testing
-training_df, testing_df = fsdf.split_df(label_type_cat=True,
-                                        split_training_factor=0.8)
-
-# Step 2. Apply univariate correlation filter
-training_df = univariate_filter(training_df,
-                                univariate_method='u_corr',
-                                corr_threshold=0.3)
-
-# Step 3. Apply multivariate correlation filter
-training_df = multivariate_filter(training_df,
-                                  multivariate_method='m_corr',
-                                  corr_threshold=0.7
-                                  )
-
-# Step 4. ML-algorithm with cross-validation
-cv_model = cv_rf_classification(training_df,
-                                binary_classification=False)
-
-# Print out some stats
-
-# Get accuracy from training
-acc = get_accuracy(model=cv_model)
-print(f"Training accuracy: {acc}")
-
-# Get predictions from training
-pred = get_predictions(model=cv_model)
-pred.show()
-
-stop_spark_session()
-```
+[FS pipeline example](../fsspark/pipeline/fs_pipeline_example.py)

fsspark/config/context.py

@@ -6,12 +6,14 @@
                                             PYARROW_SETTINGS,
                                             PANDAS_ON_SPARK_API_SETTINGS)
 
-
 os.environ['PYARROW_IGNORE_TIMEZONE'] = "1"
+
+
 # os.environ['JAVA_HOME'] = "/Library/Java/JavaVirtualMachines/jdk1.8.0_162.jdk/Contents/Home"
 # os.environ['SPARK_HOME'] = "/usr/local/spark-3.3.0-bin-hadoop3"
 
-def init_spark(apply_pyarrow_settings: bool = True,
+def init_spark(master: str = "local[8]",
+               apply_pyarrow_settings: bool = True,
                apply_extra_spark_settings: bool = True,
                apply_pandas_settings: bool = True) -> SparkSession:
     """
@@ -24,7 +26,7 @@ def init_spark(apply_pyarrow_settings: bool = True,
 
     # init or get spark session.
     spark = (SparkSession.builder
-             .master("local[8]")
+             .master(master)
              .appName("fs-spark")
              )
 

fsspark/fs/constants.py

@@ -0,0 +1,53 @@
+# Define constants for the project
+
+
+# Define univariate feature selection methods constants
+ANOVA = 'anova'
+UNIVARIATE_CORRELATION = 'u_corr'
+F_REGRESSION = 'f_regression'
+
+# Define dict with univariate feature selection methods and brief description
+UNIVARIATE_METHODS = {
+    ANOVA: 'ANOVA univariate feature selection (F-classification)',
+    UNIVARIATE_CORRELATION: 'Univariate Correlation',
+    F_REGRESSION: 'Univariate F-regression'
+}
+
+# Define multivariate feature selection methods constants
+MULTIVARIATE_CORRELATION = 'm_corr'
+MULTIVARIATE_VARIANCE = 'variance'
+
+# Define dict with multivariate feature selection methods and brief description
+MULTIVARIATE_METHODS = {
+    MULTIVARIATE_CORRELATION: 'Multivariate Correlation',
+    MULTIVARIATE_VARIANCE: 'Multivariate Variance'
+}
+
+# Define machine learning wrapper methods constants
+
+# binary classification
+RF_BINARY = 'rf_binary'
+LSVC_BINARY = 'lsvc_binary'
+FM_BINARY = 'fm_binary'  # TODO: implement this method
+
+# multilabel classification
+RF_MULTILABEL = 'rf_multilabel'
+LR_MULTILABEL = 'lg_multilabel'  # TODO: implement this method
+
+# regression
+RF_REGRESSION = 'rf_regression'
+FM_REGRESSION = 'fm_regression'  # TODO: implement this method
+
+
+# Define dict with machine learning wrapper methods and brief description
+ML_METHODS = {
+    RF_BINARY: 'Random Forest Binary Classifier',
+    LSVC_BINARY: 'Linear SVC Binary Classifier',
+    FM_BINARY: 'Factorization Machine Binary Classifier',
+
+    RF_MULTILABEL: 'Random Forest Multi-label Classifier',
+    LR_MULTILABEL: 'Logistic Regression Multi-label Classifier',
+
+    RF_REGRESSION: 'Random Forest Regression',
+    FM_REGRESSION: 'Factorization Machine Regression'
+}

fsspark/fs/core.py

@@ -179,14 +179,6 @@ def get_sample_label(self) -> list:
         """
         return self.__indexed_instances.tolist()
 
-    # def get_samples(self) -> pyspark.pandas.Series:
-    #     """
-    #     Get samples identifiers from DataFrame. Coerce data type to string.
-    #
-    #     :return: Pandas Series
-    #     """
-    #     return self.__df[self.__sample_col].astype("str")
-
     def get_sdf_vector(self, output_column_vector: str = 'features') -> pyspark.sql.DataFrame:
         """
         Return a Spark dataframe with feature columns assembled into a column vector (a.k.a. Dense Vector column).
@@ -204,6 +196,18 @@ def get_sdf_vector(self, output_column_vector: str = 'features') -> pyspark.sql.
 
         return sdf_vector
 
+    def get_sdf_and_label(self,
+                          output_column_vector: str = 'features') -> Tuple[pyspark.sql.dataframe.DataFrame, str, str]:
+        """
+        Extracts the Spark DataFrame and label column name from FSDataFrame.
+
+        :param: output_column_vector: Name of the output column vector.
+        :return: A tuple containing the Spark DataFrame and the label column name.
+        """
+        sdf = self.get_sdf_vector(output_column_vector=output_column_vector)
+        label_col = self.get_label_col_name()
+        return sdf, label_col, output_column_vector
+
     def _collect_features_as_array(self) -> np.array:
         """
         Collect features from FSDataFrame as an array.