def add_row(self, column_one_value, row_data):
if column_one_value not in self.column_one_values:
raise BIException('Unknown value: "%s" for column' %
(column_one_value, ))
if len(row_data) != len(self.column_two_values):
raise BIException('Row for: "%s" should have %d values, but has %d values only', \
column_one_value, len(self.column_two_values), len(row_data))
index = self.column_one_values.index(column_one_value)
self.table[index] = [row_data[i] for i in self._col2_order]
def get_value_column_percent(self, column_one_value, column_two_value):
if column_one_value not in self.column_one_values:
raise BIException('Unknown column one value: %s' %
(column_one_value, ))
if column_two_value not in self.column_two_values:
raise BIException('Unknown column two value: %s' %
(column_two_value, ))
column_one_index = self.column_one_values.index(column_one_value)
column_two_index = self.column_two_values.index(column_two_value)
return self.table_percent_by_column[column_one_index][column_two_index]
def correlation(self, column_one, column_two):
"""
Find correlation between two numeric columns
:param column_one:
:param column_two:
:return:
"""
if column_one not in self._dataframe_helper.get_numeric_columns():
raise BIException.non_numeric_column(column_one)
if column_two not in self._dataframe_helper.get_numeric_columns():
raise BIException.non_numeric_column(column_two)
return self._corr(column_one, column_two)
def get_coeff(self, input_column):
if input_column not in self.input_columns:
raise BIException('Input column(%s) has no impact on output column(%s)' \
%(input_column, self.output_column))
return self.stats.get(
RegressionResult.COEFFICIENTS).get(input_column).get(
RegressionResult.COEFF)
def assert_non_negative_parameter(param_type,
param_name,
param_value,
raise_exception=True):
if type(param_value) != param_type:
if raise_exception:
raise BIException.parameter_invalid_type(
param_name, param_type, type(param_value))
else:
return False
if param_value < 0:
if raise_exception:
raise BIException.parameter_has_negative_value(
param_name, param_value)
else:
return False
return True
def __init__(self, data_frame, column1, column2):
dataframe_helper = DataFrameHelper(data_frame)
if not dataframe_helper.is_valid_data_frame():
raise BIException.dataframe_invalid()
if not dataframe_helper.has_column(column1):
raise BIException.column_does_not_exist(column1)
if not dataframe_helper.is_numeric_column(column1):
raise BIException.non_numeric_column(column1)
if not dataframe_helper.has_column(column2):
raise BIException.column_does_not_exist(column2)
if not dataframe_helper.is_numeric_column(column2):
raise BIException.non_numeric_column(column2)
self._data_frame = data_frame
self._column1 = column1
self._column2 = column2
def __init__(self,
data_frame,
independent_var,
dependent_var,
independent_var_levels=None):
"""
:param data_frame: data frame to use for tests
:param independent_var: a string type column with at least two levels
:param dependent_var: a measure type column
:param independent_var_levels: if independent_var has exactly two levels this parameter can be omitted,
otherwise two levels in independent_var need to be supplied as a tuple
"""
dataframe_helper = DataFrameHelper(data_frame)
# ensure data_frame is valid
if not dataframe_helper.is_valid_data_frame():
raise BIException.dataframe_invalid()
# ensure data_frame contains a column by name independent_var
if not dataframe_helper.has_column(independent_var):
raise BIException.column_does_not_exist(independent_var)
# ensure column, independent_var, is of type string
if not dataframe_helper.is_string_column(independent_var):
raise BIException.non_string_column(independent_var)
# ensure data_frame contains a column by name dependent_var
if not dataframe_helper.has_column(dependent_var):
raise BIException.column_does_not_exist(dependent_var)
# ensure column, dependent_var, is of numeric type
if not dataframe_helper.is_numeric_column(dependent_var):
raise BIException.non_numeric_column(dependent_var)
self._data_frame = data_frame
self._independent_var = independent_var
self._dependent_var = dependent_var
self._independent_var_levels = self._get_independent_var_levels()
if independent_var_levels != None and type(independent_var_levels) in [
list, tuple
]:
if len(independent_var_levels) != 2:
raise BIException(
"independent_var_levels should only contain two levels")
for level in independent_var_levels:
if level not in self._independent_var_levels:
raise BIException('Column, %s, does not have level "%s"' %
(self._independent_var, level))
self._independent_var_levels = independent_var_levels
else:
if len(self._independent_var_levels) != 2:
raise BIException(
'Column, %s, should have exactly two levels, but it has %d levels'
%
(self._independent_var, len(self._independent_var_levels)))
def fit(self, output_column, input_columns=None):
print "linear regression fit started"
if output_column not in self._dataframe_helper.get_numeric_columns():
raise BIException('Output column: %s is not a measure column' %
(output_column, ))
if input_columns == None:
input_columns = list(
set(self._dataframe_helper.get_numeric_columns()) -
{output_column})
nColsToUse = self._analysisDict[self._analysisName]["noOfColumnsToUse"]
if nColsToUse != None:
input_columns = input_columns[:nColsToUse]
if len(
set(input_columns) -
set(self._dataframe_helper.get_numeric_columns())) != 0:
raise BIException(
'At least one of the input columns %r is not a measure column'
% (input_columns, ))
all_measures = input_columns + [output_column]
print all_measures
measureDf = self._data_frame.select(all_measures)
lr = LR(maxIter=LinearRegression.MAX_ITERATIONS,
regParam=LinearRegression.REGULARIZATION_PARAM,
elasticNetParam=1.0,
labelCol=LinearRegression.LABEL_COLUMN_NAME,
featuresCol=LinearRegression.FEATURES_COLUMN_NAME)
st = time.time()
pipeline = MLUtils.create_pyspark_ml_pipeline(input_columns, [],
output_column)
pipelineModel = pipeline.fit(measureDf)
training_df = pipelineModel.transform(measureDf)
training_df = training_df.withColumn("label",
training_df[output_column])
print "time taken to create training_df", time.time() - st
# st = time.time()
# training_df.cache()
# print "caching in ",time.time()-st
st = time.time()
lr_model = lr.fit(training_df)
lr_summary = lr_model.evaluate(training_df)
print "lr model summary", time.time() - st
sample_data_dict = {}
for input_col in input_columns:
sample_data_dict[input_col] = None
coefficients = [
float(val) if val != None else None
for val in lr_model.coefficients.values
]
try:
p_values = [
float(val) if val != None else None
for val in lr_model.summary.pValues
]
except:
p_values = [None] * len(coefficients)
# print p_values
# print coefficients
regression_result = RegressionResult(output_column,
list(set(input_columns)))
regression_result.set_params(intercept=float(lr_model.intercept),\
coefficients=coefficients,\
p_values = p_values,\
rmse=float(lr_summary.rootMeanSquaredError), \
r2=float(lr_summary.r2),\
sample_data_dict=sample_data_dict)
self._completionStatus = self._dataframe_context.get_completion_status(
)
self._completionStatus += self._scriptWeightDict[
self._analysisName]["script"]
progressMessage = CommonUtils.create_progress_message_object(self._analysisName,\
"regressionTrainingEnd",\
"info",\
self._scriptStages["regressionTrainingEnd"]["summary"],\
self._completionStatus,\
self._completionStatus)
if self._ignoreRegressionElasticityMessages != True:
CommonUtils.save_progress_message(
self._messageURL,
progressMessage,
ignore=self._ignoreRegressionElasticityMessages)
self._dataframe_context.update_completion_status(
self._completionStatus)
return regression_result
def get_regression_result(self, output_column):
if output_column not in self.measures:
raise BIException('No regression result found for column(%s)' %
(output_column, ))
return self.results.get(output_column)
def fit(self, output_column, input_columns=None):
if output_column not in self._dataframe_helper.get_numeric_columns():
raise BIException('Output column: %s is not a measure column' % (output_column,))
if input_columns == None:
input_columns = list(set(self._dataframe_helper.get_numeric_columns()) - {output_column})
if len(set(input_columns) - set(self._dataframe_helper.get_numeric_columns())) != 0:
raise BIException('At least one of the input columns %r is not a measure column' % (input_columns,))
# TODO: ensure no duplicates are present in input_columns
regression_result = RegressionResult(output_column, input_columns)
training_df = self._data_frame.rdd.map(lambda row: \
(float(row[output_column]),
DenseVector([float(row[col]) for col in input_columns]))).toDF()
lr = LR(maxIter=LinearRegression.MAX_ITERATIONS, regParam=LinearRegression.REGULARIZATION_PARAM,
elasticNetParam=1.0, labelCol=LinearRegression.LABEL_COLUMN_NAME,
featuresCol=LinearRegression.FEATURES_COLUMN_NAME)
lr_model = lr.fit(training_df)
lr_summary = lr_model.evaluate(training_df)
#regression_result.set_params(intercept=lr_model.intercept, coefficients=lr_model.coefficients,
# rmse=lr_summary.rootMeanSquaredError, r2=lr_summary.r2,
# t_values=lr_summary.tValues, p_values=lr_summary.pValues)
# TODO: pass t_values and p_values
coefficients = [float(i) for i in lr_model.coefficients.values]
if not any([coeff != 0 for coeff in coefficients]):
return None
sample_data_dict = {}
lr_dimension = {}
for c in input_columns:
sample_data_dict[c] = None
lr_dimension[c] = {'dimension':'', 'levels': [], 'coefficients':[],
'dimension2':'', 'levels2': [], 'coefficients2':[]}
diff = 0
diff2 = 0
for dim in self._string_columns:
# sample_data_dict[col] = self._dataframe_helper.get_sample_data(col, output_column, self._sample_size)
temp = []
if len(self._levels[dim])>0 and len(self._levels[dim])<16:
for level in self._levels[dim]:
sub_df = self._data_frame.select(*[c,output_column]).filter(col(dim)==level)
train = sub_df.rdd.map(lambda row: (float(row[output_column]),
DenseVector([float(row[c])]))).toDF()
sub_lr_model = lr.fit(train)
temp = temp + [float(i) for i in sub_lr_model.coefficients.values]
if max(temp)-min(temp) > diff:
diff = max(temp)-min(temp)
diff2 = diff
lr_dimension[c]['dimension2']= lr_dimension[c]['dimension']
lr_dimension[c]['levels2'] = lr_dimension[c]['levels']
lr_dimension[c]['coefficients2'] = lr_dimension[c]['coefficients']
lr_dimension[c]['dimension'] = dim
X = self._levels[dim]
Y = temp
Z = [abs(y) for y in Y]
lr_dimension[c]['levels'] = [x for (z,y,x) in sorted(zip(Z,Y,X))]
lr_dimension[c]['coefficients'] = [y for (z,y,x) in sorted(zip(Z,Y,X))]
elif max(temp)-min(temp) > diff2:
diff2 = max(temp)-min(temp)
lr_dimension[c]['dimension2'] = dim
X = self._levels[dim]
Y = temp
Z = [abs(y) for y in Y]
lr_dimension[c]['levels2'] = [x for (z,y,x) in sorted(zip(Z,Y,X))]
lr_dimension[c]['coefficients2'] = [y for (z,y,x) in sorted(zip(Z,Y,X))]
regression_result.set_params(intercept=float(lr_model.intercept), coefficients=coefficients,
rmse=float(lr_summary.rootMeanSquaredError), r2=float(lr_summary.r2),
sample_data_dict=sample_data_dict, lr_dimension=lr_dimension)
return regression_result
