def compute_series(dep_var, categories, grouping_var=None):
series = list()
has_nulls = pd.isnull(dep_var['series']).any()
if utils.is_nominal(dep_var):
if not grouping_var:
series.append({"name": "all", "data": count(dep_var['series'], categories)})
else:
for series_name in grouping_var['type']['enumeration']:
filtered_data = [v for v, d in zip(dep_var['series'], grouping_var['series']) if d == series_name]
series.append({"name": series_name, "data": count(filtered_data, categories)})
else:
if not grouping_var:
values = pd.Series(dep_var['series'])
if has_nulls:
data = [int(i) for i in histogram(values.dropna(), categories[:-1])[0]] + [int(values.isnull().sum())]
else:
data = [int(i) for i in histogram(values, categories)[0]]
series.append({"name": 'all', "data": data})
else:
values = pd.Series(dep_var['series'])
grouping_values = pd.Series(grouping_var['series']).fillna('No data')
for series_name in grouping_var['type']['enumeration']:
filtered_data = pd.Series([v for v, d in zip(values, grouping_values) if d == series_name])
if has_nulls:
data = [int(i) for i in histogram(filtered_data.dropna(), categories[:-1])[0]] + [int(filtered_data.isnull().sum())]
else:
data = [int(i) for i in histogram(filtered_data, categories)[0]]
series.append({"name": series_name, "data": data})
return series
def error_histograms(dep_var, indep_vars):
histograms = list()
if len(dep_var) > 0:
histograms.append(error_histogram(dep_var))
grouping_vars = [indep_var for indep_var in indep_vars if utils.is_nominal(indep_var)]
for grouping_var in grouping_vars:
histograms.append(error_histogram(dep_var, grouping_var))
return json.dumps(histograms)
def compute_histograms(dep_var, indep_vars, nb_bins=DEFAULT_BINS):
histograms = list()
if len(dep_var) > 0:
histograms.append(compute_histogram(dep_var, nb_bins=nb_bins))
grouping_vars = [indep_var for indep_var in indep_vars if utils.is_nominal(indep_var)]
for grouping_var in grouping_vars:
histograms.append(compute_histogram(dep_var, grouping_var, nb_bins))
return histograms
def _create_featurizer(indep_vars):
transforms = []
for var in indep_vars:
if utils.is_nominal(var):
transforms.append(
OneHotEncoding(var['name'], var['type']['enumeration']))
else:
transforms.append(DummyTransform(var['name']))
return Featurizer(transforms)
def intermediate():
# Read inputs
inputs = io_helper.fetch_data()
dep_var = inputs["data"]["dependent"][0]
indep_vars = inputs["data"]["independent"]
data = io_helper.fetch_dataframe(variables=[dep_var] + indep_vars)
data = utils.remove_nulls(data, errors='ignore')
y = data.pop(dep_var['name'])
featurizer = _create_featurizer(indep_vars)
X = pd.DataFrame(featurizer.transform(data),
columns=featurizer.columns,
index=data.index)
if not indep_vars:
raise errors.UserError('No covariables selected.')
# Distributed linear regression only works for continuous variables
if utils.is_nominal(dep_var):
raise errors.UserError(
'Dependent variable must be continuous in distributed mode. Use SGD Regression for '
'nominal variables instead.')
if data.empty:
logging.warning('All values are NAN, returning zero values')
result = {
'summary': {},
'columns': [],
'means': 0,
'X^T * X': 0,
'count': 0,
'scale': 0,
}
else:
# Compute linear-regression
X.insert(loc=0, column='intercept', value=1.)
lm = OLS(y, X)
flm = lm.fit()
logging.info(flm.summary())
output = format_output(flm)
result = {
'summary': output,
'columns': list(X.columns),
'means': X.mean().values,
'X^T * X': X.T.values.dot(X.values),
'count': len(X),
'scale': flm.scale,
}
# Store results
io_helper.save_results(json.dumps(result), 'application/json')
def compute_categories(dep_var, nb_bins=DEFAULT_BINS):
values = pd.Series(dep_var['series'])
if len(values) == 0:
raise errors.UserError('Dependent variable {} is empty.'.format(
dep_var['name']))
# TODO: dep_var['series'] can contain both np.nan (in numerical variables) and None (in nominal), pd.isnull
# can handle them both
has_nulls = pd.isnull(dep_var['series']).any()
if utils.is_nominal(dep_var):
categories = [str(c) for c in dep_var['type']['enumeration']]
if 'enumeration_labels' in dep_var['type']:
categories_labels = [
str(c) for c in dep_var['type']['enumeration_labels']
]
else:
categories_labels = categories
if has_nulls:
categories.append('None')
categories_labels.append('No data')
else:
# calculate min and max if not available in variable (ignore null values)
values = pd.Series(dep_var['series'])
minimum = dep_var.get('minValue', values.min())
maximum = dep_var.get('maxValue', values.max())
all_nulls = values.isnull().all()
if all_nulls:
categories = ['None']
categories_labels = ['No data']
else:
if utils.is_integer(dep_var):
step = math.ceil((maximum - minimum) / nb_bins)
categories = list(arange(minimum, maximum, step).tolist())
categories_labels = [
"%d - %d" % (v, v + step) for v in categories
]
else:
step = (maximum - minimum) / nb_bins
categories = list(arange(minimum, maximum, step).tolist())
categories_labels = [
"%s - %s" % ("{:.2f}".format(v), "{:.2f}".format(v + step))
for v in categories
]
categories.append(categories[-1] + step)
if has_nulls:
categories.append('None')
categories_labels.append('No data')
return categories, categories_labels
def _compute_intermediate_result(inputs):
dep_var = inputs["data"]["dependent"][0]
indep_vars = inputs["data"]["independent"]
nominal_vars = []
numeric_vars = []
for var in [dep_var] + indep_vars:
if utils.is_nominal(var):
nominal_vars.append(var['name'])
else:
numeric_vars.append(var['name'])
# Load data into a Pandas dataframe
logging.info("Loading data...")
X = io_helper.fetch_dataframe(variables=[dep_var] + indep_vars)
logging.info('Dropping NULL values')
X = utils.remove_nulls(X, errors='ignore')
# Generate results
logging.info("Generating results...")
result = {
'columns': numeric_vars,
'nominal_columns': nominal_vars,
}
if len(X):
result.update({
'means':
X[numeric_vars].mean().values,
'X^T * X':
X[numeric_vars].T.dot(X[numeric_vars].values).values,
'count':
len(X),
})
if nominal_vars:
result['crosstab'] = X[nominal_vars].groupby(nominal_vars).size()\
.reset_index()\
.rename(columns={0: 'count'})\
.to_dict(orient='records')
else:
result['crosstab'] = []
else:
logging.warning('All values are NAN, returning zero values')
k = len(result['columns'])
result.update({
'means': np.zeros(k),
'X^T * X': np.zeros((k, k)),
'count': 0,
'crosstab': [],
})
return result
def intermediate_stats():
"""Calculate summary statistics for single node."""
# Read inputs
logging.info("Fetching data...")
inputs = io_helper.fetch_data()
dep_var = inputs["data"]["dependent"][0]
indep_vars = inputs["data"]["independent"]
labels = _get_labels(indep_vars + [dep_var])
types = _get_types(indep_vars + [dep_var])
if len(dep_var['series']) == 0:
logging.warning(
'Dependent variable has no values, check your SQL query.')
# Load data into a Pandas dataframe
logging.info("Loading data...")
df = io_helper.fetch_dataframe(variables=[dep_var] + indep_vars)
# Generate results
logging.info("Generating results...")
group_variables = [
var['name'] for var in indep_vars if utils.is_nominal(var)
]
# grouped statistics
data = []
if group_variables:
for group_name, group in df.groupby(group_variables):
# if there's only one nominal column
if not isinstance(group_name, tuple):
group_name = (group_name, )
data += _calc_stats(group, group_name, group_variables, labels,
types)
# overall statistics
data += _calc_stats(df, ('all', ), [], labels, types)
logging.info("Results:\n{}".format(data))
table = {
'schema': OUTPUT_SCHEMA_INTERMEDIATE,
'data': data,
}
io_helper.save_results(pd.io.json.dumps(table),
shapes.Shapes.TABULAR_DATA_RESOURCE)
logging.info("DONE")
def _compute_intermediate_result(inputs):
dep_var = inputs["data"]["dependent"][0]
indep_vars = inputs["data"]["independent"]
# Use only numeric variables
variables = []
for var in [dep_var] + indep_vars:
if utils.is_nominal(var):
logging.warning('Correlation heatmap works only with numerical types ({} is {})'.format(var['name'], var['type']['name']))
else:
variables.append(var)
# Load data into a Pandas dataframe
logging.info("Loading data...")
X = io_helper.fetch_dataframe(variables=variables)
logging.info('Dropping NULL values')
X = utils.remove_nulls(X, errors='ignore')
# Generate results
logging.info("Generating results...")
if len(X):
result = {
'columns': list(X.columns),
'means': X.mean().values,
'X^T * X': X.T.dot(X.values).values,
'count': len(X),
}
else:
logging.warning('All values are NAN, returning zero values')
k = X.shape[1]
result = {
'columns': list(X.columns),
'means': np.zeros(k),
'X^T * X': np.zeros((k, k)),
'count': 0,
}
return result
def main():
# Read inputs
inputs = io_helper.fetch_data()
dep_var = inputs["data"]["dependent"][0]
indep_vars = inputs["data"]["independent"]
if not indep_vars:
raise errors.UserError('No covariables selected.')
if utils.is_nominal(dep_var):
job_type = 'classification'
else:
job_type = 'regression'
data = io_helper.fetch_dataframe(variables=[dep_var] + indep_vars)
data = utils.remove_nulls(data, errors='ignore')
y = data.pop(dep_var['name'])
featurizer = _create_featurizer(indep_vars)
X = pd.DataFrame(featurizer.transform(data), columns=featurizer.columns)
if X.empty:
logging.warning('All values are NAN, returning zero values')
result = {}
pfa = None
else:
# Add intercept
X.insert(loc=0, column='intercept', value=1.)
# Remove linearly dependent columns
X = X.iloc[:, _independent_columns(X)]
# Fit regresssion
if job_type == 'regression':
result, metadata = _fit_regression(X, y)
# Generate PFA for predictions
pfa = _generate_pfa_regressor(result, indep_vars, featurizer)
elif job_type == 'classification':
# Run one-vs-others for each class
result = {}
metadata = {}
for cat in y.cat.categories:
r, m = _fit_logit(X, y == cat)
result[cat] = r
metadata[cat] = m
if all(result[cat]['intercept']['coef'] is None
for cat in y.cat.categories):
raise errors.UserError(
'Not enough data to apply logistic regression.')
# Generate PFA for predictions
pfa = _generate_pfa_classifier(result, indep_vars, featurizer,
y.cat.categories)
# Add metadata from model
pfa['metadata'] = metadata
# TODO: save multiple outputs - PFA and coeficients
# Store results
io_helper.save_results(json.dumps(result), 'application/json')
