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from gnomad_hail import *
import pyspark.sql
from import *
from import *
from import *
import hail as hl
import pandas as pd
from pyspark.sql.functions import udf, col
from pyspark.sql.types import ArrayType, DoubleType
def run_rf_test(
mt: hl.MatrixTable,
output: str = '/tmp'
) -> Tuple[, hl.MatrixTable]:
Runs a dummy test RF on a given MT:
1. Creates row annotations and labels to run model on
2. Trains a RF pipeline model (including median imputation of missing values in created annotations)
3. Saves the RF pipeline model
4. Applies the model to the MT and prints features importance
:param MatrixTable mt: Input MT
:param str output: Output files prefix to save the RF model
:return: RF model and MatrixTable after applying RF model
:rtype: (PipelineModel, MatrixTable)
mt = mt.annotate_rows(
feature2=hl.rand_norm(0.0, 1.0),
feature3=hl.or_missing(hl.rand_bool(0.5), hl.rand_norm(0.0, 1.0)))
mt = mt.annotate_rows(label=hl.cond(mt['feature1'] & (mt['feature2'] > 0), "TP", "FP"))
ht = mt.rows()
def f3stats(ht):
return ht.aggregate(hl.struct(
f3_before_imputation = f3stats(ht)'Feature3 defined values before imputation: {}'.format(f3_before_imputation.n))'Feature3 median: {}'.format(
features_to_impute = ['feature3']
quantiles = get_columns_quantiles(ht, features_to_impute, [0.5])
quantiles = {k: v[0] for k, v in quantiles.items()}'Features median:\n{}'.format(f'{k}: {v}\n' for k, v in quantiles.items()))
ht = ht.annotate(
**{f: hl.or_else(ht[f], quantiles[f]) for f in features_to_impute}
ht = ht.annotate_globals(medians=quantiles)
f3_after_imputation = f3stats(ht)'Feature3 defined values after imputation: {}'.format(f3_after_imputation.n))'Feature3 median: {}'.format(
ht ='label', 'feature1', 'feature2', 'feature3')
label = 'label'
features = ['feature1', 'feature2', 'feature3']
rf_model = train_rf(ht, features, label)
save_model(rf_model, out_path=output + '/rf.model', overwrite=True)
rf_model = load_model(output + '/rf.model')
return rf_model, apply_rf_model(ht, rf_model, features, label)
def check_ht_fields_for_spark(ht: hl.Table, fields: List[str]) -> None:
Checks specified fields of a hail table for Spark DataFrame conversion (type and name)
:param Table ht: input Table
:param list of str fields: Fields to test
:return: None
:rtype: None
allowed_types = [hl.tfloat, hl.tfloat32, hl.tfloat64, hl.tint, hl.tint32, hl.tint64, hl.tstr, hl.tbool]
bad_field_names = [c for c in fields if '.' in c]
bad_types = [c[0] for c in ht.key_by().select(*fields).row.items() if c[1].dtype not in allowed_types]
if bad_field_names or bad_types:
raise ValueError('Only basic type fields can be converted from Hail to Spark. In addition, `.` are not allowed in field names in Spark.\n' +
'Offending fields (non basic type): {}'.format(bad_types) +
'Offending fields (bad field name): {}\n'.format(','.join(bad_field_names))
def get_columns_quantiles(
ht: hl.Table,
fields: List[str],
quantiles: List[float],
relative_error: int = 0.001
) -> Dict[str, List[float]]:
Computes approximate quantiles of specified numeric fields from non-missing values.
Non-numeric fields are ignored.
This function returns a Dict of column name -> list of quantiles in the same order specified.
If a column only has NAs, None is returned.
:param Table ht: input HT
:param list of str fields: list of features to impute. If none given, all numerical features with missing data are imputed
:param list of float quantiles: list of quantiles to return (e.g. [0.5] would return the median)
:param float relative_error: The relative error on the quantile approximation
:return: Dict of column -> quantiles
:rtype: dict of str -> list of float
check_ht_fields_for_spark(ht, fields)
df = ht.key_by().select(*fields).to_spark()
res = {}
for f in fields:"Computing median for column: {}".format(f))
col_no_na =
if col_no_na.first() is not None:
res[f] = col_no_na.approxQuantile(str(f), quantiles, relative_error)
res[f] = None
return res
def ht_to_rf_df(
ht: hl.Table,
features: List[str],
label: str,
index: str = None
) -> pyspark.sql.DataFrame:
Creates a Spark dataframe ready for RF from a HT.
Rows with any missing features are dropped.
Missing labels are replaced with 'NA'
Note: Only basic types are supported!
:param Table ht: Input HT
:param list of str features: Features that will be used for RF
:param str label: Label column that will be predicted by RF
:param str index: Optional index column to keep (E.g. for joining results back at later stage)
:return: Spark Dataframe
:rtype: DataFrame
cols_to_keep = features + [label]
if index:
df = ht.key_by().select(*cols_to_keep).to_spark()
df = df.dropna(subset=features).fillna('NA', subset=label)
return df
def get_features_importance(
rf_index: int = -2,
assembler_index: int =-3
) -> Dict[str, float]:
Extract the features importance from a Pipeline model containing a RandomForestClassifier stage.
:param PipelineModel rf_pipeline: Input pipeline
:param int rf_index: index of the RandomForestClassifier stage
:param int assembler_index: index of the VectorAssembler stage
:return: feature importance for each feature in the RF model
:rtype: dict of str: float
feature_names = [x[:-len("_indexed")] if x.endswith("_indexed") else x for x in
return dict(zip(feature_names, rf_pipeline.stages[rf_index].featureImportances))
def get_labels(
) -> List[str]:
Returns the labels from the StringIndexer stage at index 0 from an RF pipeline model
:param PipelineModel rf_pipeline: Input pipeline
:return: labels
:rtype: list of str
return rf_pipeline.stages[0].labels
def test_model(
ht: hl.Table,
features: List[str],
label: str,
prediction_col_name: str = 'rf_prediction'
) -> List[hl.tstruct]:
A wrapper to test a model on a set of examples with known labels:
1) Runs the model on the data
2) Prints confusion matrix and accuracy
3) Returns confusion matrix as a list of struct
:param Table ht: Input table
:param PipelineModel rf_model: RF Model
:param list of str features: Columns containing features that were used in the model
:param str label: Column containing label to be predicted
:param str prediction_col_name: Where to store the prediction
:return: A list containing structs with {label, prediction, n}
:rtype: list of Struct
ht = apply_rf_model(ht.filter(hl.is_defined(ht[label])),
test_results = ht.group_by(ht[prediction_col_name], ht[label]).aggregate(n=hl.agg.count()).collect()
# Print results
df = pd.DataFrame(test_results)
df = df.pivot(index=label, columns=prediction_col_name, values='n')"Testing results:\n{}".format(pformat(df)))"Accuracy: {}".format(
sum([x.n for x in test_results if x[label] == x[prediction_col_name]]) /
sum([x.n for x in test_results])
return test_results
def apply_rf_model(
ht: hl.Table,
features: List[str],
label: str,
probability_col_name: str = 'rf_probability',
prediction_col_name: str = 'rf_prediction'
) -> hl.Table:
Applies a Random Forest (RF) pipeline model to a Table and annotate the RF probabilities and predictions.
:param MatrixTable ht: Input HT
:param PipelineModel rf_model: Random Forest pipeline model
:param list of str features: List of feature columns in the pipeline. !Should match the model list of features!
:param str label: Column containing the labels. !Should match the model labels!
:param str probability_col_name: Name of the column that will store the RF probabilities
:param str prediction_col_name: Name of the column that will store the RF predictions
:return: Table with RF columns
:rtype: Table
""""Applying RF model.")
check_ht_fields_for_spark(ht, features + [label])
index_name = 'rf_idx'
while index_name in ht.row:
index_name += '_tmp'
ht = ht.add_index(name=index_name)
ht_keys = ht.key
ht = ht.key_by(index_name)
df = ht_to_rf_df(ht, features, label, index_name)
rf_df = rf_model.transform(df)
def to_array(col):
def to_array_(v):
return v.toArray().tolist()
return udf(to_array_, ArrayType(DoubleType()))(col)
rf_ht = hl.Table.from_spark(
rf_df.withColumn("probability", to_array(col("probability")))
rf_ht = rf_ht.key_by(index_name)
ht = ht.annotate(
probability_col_name: {label: rf_ht[ht[index_name]]["probability"][i] for i, label in enumerate(get_labels(rf_model))},
prediction_col_name: rf_ht[ht[index_name]]["predictedLabel"]
ht = ht.key_by(*ht_keys)
ht = ht.drop(index_name)
return ht
def save_model(
out_path: str,
overwrite: bool = False
) -> None:
Saves a Random Forest pipeline model.
:param PipelineModel rf_pipeline: Pipeline to save
:param str out_path: Output path
:param bool overwrite: If set, will overwrite existing file(s) at output location
:return: Nothing
:rtype: NoneType
""""Saving model to %s" % out_path)
if overwrite:
def load_model(
input_path: str
) ->
Loads a Random Forest pipeline model.
:param str input_path: Location of model to load
:return: Random Forest pipeline model
:rtype: PipelineModel
""""Loading model from {}".format(input_path))
def train_rf(
ht: hl.Table,
features: List[str],
label: str,
num_trees: int = 500,
max_depth: int = 5
) ->
Trains a Random Forest (RF) pipeline model.
:param Table ht: Input HT
:param list of str features: List of columns to be used as features
:param str label: Column containing the label to predict
:param int num_trees: Number of trees to use
:param int max_depth: Maximum tree depth
:return: Random Forest pipeline model
:rtype: PipelineModel
""""Training RF model using:\n"
"features: {}\n"
"labels: {}\n"
"num_trees: {}\n"
"max_depth: {}".format(",".join(features),
label, num_trees, max_depth))
check_ht_fields_for_spark(ht, features + [label])
df = ht_to_rf_df(ht, features, label)
label_indexer = StringIndexer(inputCol=label, outputCol=label + "_indexed").fit(df)
labels = label_indexer.labels"Found labels: {}".format(labels))
string_features = [x[0] for x in df.dtypes if x[0] != label and x[1] == 'string']
if string_features:"Indexing string features: {}".format(",".join(string_features)))
string_features_indexers = [StringIndexer(inputCol=x, outputCol=x + "_indexed").fit(df)
for x in string_features]
assembler = VectorAssembler(inputCols=[x[0] + "_indexed" if x[1] == 'string' else x[0]
for x in df.dtypes if x[0] != label],
rf = RandomForestClassifier(labelCol=label + "_indexed", featuresCol="features",
maxDepth=max_depth, numTrees=num_trees)
label_converter = IndexToString(inputCol='prediction', outputCol='predictedLabel', labels=labels)
pipeline = Pipeline(stages=[label_indexer] + string_features_indexers +
[assembler, rf, label_converter])
# Train model"Training RF model")
rf_model =
feature_importance = get_features_importance(rf_model)
"RF features importance:\n{}".format(
"\n".join(["{}: {}".format(f, i) for f, i in feature_importance.items()])
return rf_model