class ChiSquareAnalysis:
def __init__(self,
df_context,
df_helper,
chisquare_result,
target_dimension,
analysed_dimension,
significant_variables,
num_analysed_variables,
data_frame,
measure_columns,
base_dir,
appid=None,
target_chisquare_result=None):
self._blockSplitter = "|~NEWBLOCK~|"
self._highlightFlag = "|~HIGHLIGHT~|"
self._dimensionNode = NarrativesTree()
self._dimensionNode.set_name(target_dimension)
self._data_frame = data_frame
self._dataframe_context = df_context
self._dataframe_helper = df_helper
self._chisquare_result = chisquare_result
self._target_dimension = target_dimension
self._analysed_dimension = analysed_dimension
self._significant_variables = significant_variables
self._target_chisquare_result = target_chisquare_result
self._measure_columns = self._dataframe_helper.get_numeric_columns()
self._chiSquareLevelLimit = GLOBALSETTINGS.CHISQUARELEVELLIMIT
self._num_analysed_variables = num_analysed_variables
self._chiSquareTable = chisquare_result.get_contingency_table()
significant_variables = list(
set(significant_variables) - {analysed_dimension})
if len(significant_variables) <= 20:
if len(significant_variables) <= 3:
self._second_level_dimensions = list(significant_variables)
else:
self._second_level_dimensions = list(significant_variables)[:3]
else:
self._second_level_dimensions = list(significant_variables)[:5]
print self._second_level_dimensions
self._appid = appid
self._card1 = NormalCard()
self._targetCards = []
self._base_dir = base_dir
self._binTargetCol = False
self._binAnalyzedCol = False
print "--------Chi-Square Narratives for ", analysed_dimension, "---------"
if self._dataframe_context.get_custom_analysis_details() != None:
binnedColObj = [
x["colName"]
for x in self._dataframe_context.get_custom_analysis_details()
]
print "analysed_dimension : ", self._analysed_dimension
if binnedColObj != None and self._target_dimension in binnedColObj:
self._binTargetCol = True
if binnedColObj != None and (
self._analysed_dimension in binnedColObj
or self._analysed_dimension in self._measure_columns):
self._binAnalyzedCol = True
if self._appid == None:
self._generate_narratives()
self._dimensionNode.add_cards([self._card1] + self._targetCards)
self._dimensionNode.set_name("{}".format(analysed_dimension))
elif self._appid == "2":
self._generate_narratives()
self._dimensionNode.add_cards([self._card1])
self._dimensionNode.set_name("{}".format(analysed_dimension))
elif self._appid == "1":
self._generate_narratives()
self._dimensionNode.add_cards([self._card1])
self._dimensionNode.set_name("{}".format(analysed_dimension))
def get_dimension_node(self):
return json.loads(
CommonUtils.convert_python_object_to_json(self._dimensionNode))
def get_dimension_card1(self):
return self._card1
def _generate_narratives(self):
chisquare_result = self._chisquare_result
target_dimension = self._target_dimension
analysed_dimension = self._analysed_dimension
significant_variables = self._significant_variables
num_analysed_variables = self._num_analysed_variables
table = self._chiSquareTable
total = self._chiSquareTable.get_total()
levels = self._chiSquareTable.get_column_two_levels()
level_counts = self._chiSquareTable.get_column_total()
levels_count_sum = sum(level_counts)
levels_percentages = [
i * 100.0 / levels_count_sum for i in level_counts
]
sorted_levels = sorted(zip(level_counts, levels), reverse=True)
level_differences = [0.0] + [
sorted_levels[i][0] - sorted_levels[i + 1][0]
for i in range(len(sorted_levels) - 1)
]
top_dims = [
j for i, j in
sorted_levels[:level_differences.index(max(level_differences))]
]
top_dims_contribution = sum([
i for i, j in
sorted_levels[:level_differences.index(max(level_differences))]
])
bottom_dim = sorted_levels[-1][1]
bottom_dim_contribution = sorted_levels[-1][0]
bottom_dims = [
y for x, y in sorted_levels if x == bottom_dim_contribution
]
target_levels = self._chiSquareTable.get_column_one_levels()
target_counts = self._chiSquareTable.get_row_total()
sorted_target_levels = sorted(zip(target_counts, target_levels),
reverse=True)
top_target_count, top_target = sorted_target_levels[0]
second_target_count, second_target = sorted_target_levels[1]
top_target_contributions = [
table.get_value(top_target, i) for i in levels
]
sum_top_target = sum(top_target_contributions)
sorted_levels = sorted(zip(top_target_contributions, levels),
reverse=True)
level_differences = [0.0] + [
sorted_levels[i][0] - sorted_levels[i + 1][0]
for i in range(len(sorted_levels) - 1)
]
top_target_top_dims = [
j for i, j in
sorted_levels[:level_differences.index(max(level_differences))]
]
top_target_top_dims_contribution = sum([
i for i, j in
sorted_levels[:level_differences.index(max(level_differences))]
])
top_target_bottom_dim = sorted_levels[-1][1]
top_target_bottom_dim_contribution = sorted_levels[-1][0]
top_target_percentages = [
i * 100.0 / sum_top_target for i in top_target_contributions
]
best_top_target_index = top_target_contributions.index(
max(top_target_contributions))
worst_top_target_index = top_target_contributions.index(
min(top_target_contributions))
top_target_differences = [
x - y for x, y in zip(levels_percentages, top_target_percentages)
]
if len(top_target_differences) > 6:
tops = 2
bottoms = -2
elif len(top_target_differences) > 4:
tops = 2
bottoms = -1
else:
tops = 1
bottoms = -1
sorted_ = sorted(enumerate(top_target_differences),
key=lambda x: x[1],
reverse=True)
best_top_difference_indices = [x for x, y in sorted_[:tops]]
worst_top_difference_indices = [x for x, y in sorted_[bottoms:]]
top_target_shares = [
x * 100.0 / y
for x, y in zip(top_target_contributions, level_counts)
]
max_top_target_shares = max(top_target_shares)
best_top_target_share_index = [
idx for idx, val in enumerate(top_target_shares)
if val == max_top_target_shares
]
level_counts_threshold = sum(level_counts) * 0.05 / len(level_counts)
min_top_target_shares = min([
x for x, y in zip(top_target_shares, level_counts)
if y >= level_counts_threshold
])
worst_top_target_share_index = [
idx for idx, val in enumerate(top_target_shares)
if val == min_top_target_shares
]
overall_top_percentage = sum_top_target * 100.0 / total
second_target_contributions = [
table.get_value(second_target, i) for i in levels
]
sum_second_target = sum(second_target_contributions)
sorted_levels = sorted(zip(second_target_contributions, levels),
reverse=True)
level_differences = [0.0] + [
sorted_levels[i][0] - sorted_levels[i + 1][0]
for i in range(len(sorted_levels) - 1)
]
second_target_top_dims = [
j for i, j in
sorted_levels[:level_differences.index(max(level_differences))]
]
second_target_top_dims_contribution = sum([
i for i, j in
sorted_levels[:level_differences.index(max(level_differences))]
])
second_target_bottom_dim = sorted_levels[-1][1]
second_target_bottom_dim_contribution = sorted_levels[-1][0]
second_target_percentages = [
i * 100.0 / sum_second_target for i in second_target_contributions
]
best_second_target_index = second_target_contributions.index(
max(second_target_contributions))
worst_second_target_index = second_target_contributions.index(
min(second_target_contributions))
second_target_differences = [
x - y
for x, y in zip(levels_percentages, second_target_percentages)
]
if len(second_target_differences) > 6:
tops = 2
bottoms = -2
elif len(second_target_differences) > 4:
tops = 2
bottoms = -1
else:
tops = 1
bottoms = -1
sorted_ = sorted(enumerate(second_target_differences),
key=lambda x: x[1],
reverse=True)
best_second_difference_indices = [x for x, y in sorted_[:tops]]
worst_second_difference_indices = [x for x, y in sorted_[bottoms:]]
second_target_shares = [
x * 100.0 / y
for x, y in zip(second_target_contributions, level_counts)
]
max_second_target_shares = max(second_target_shares)
best_second_target_share_index = [
idx for idx, val in enumerate(second_target_shares)
if val == max_second_target_shares
]
level_counts_threshold = sum(level_counts) * 0.05 / len(level_counts)
min_second_target_shares = min([
x for x, y in zip(second_target_shares, level_counts)
if y >= level_counts_threshold
])
# worst_second_target_share_index = second_target_shares.index(min_second_target_shares)
worst_second_target_share_index = [
idx for idx, val in enumerate(second_target_shares)
if val == min_second_target_shares
]
overall_second_percentage = sum_second_target * 100.0 / total
targetCardDataDict = {}
targetCardDataDict['target'] = target_dimension
targetCardDataDict['colname'] = analysed_dimension
targetCardDataDict['num_significant'] = len(significant_variables)
targetCardDataDict['plural_colname'] = NarrativesUtils.pluralize(
analysed_dimension)
targetCardDataDict["blockSplitter"] = self._blockSplitter
targetCardDataDict["binTargetCol"] = self._binTargetCol
targetCardDataDict["binAnalyzedCol"] = self._binAnalyzedCol
targetCardDataDict['highlightFlag'] = self._highlightFlag
targetCardDataDict['levels'] = levels
data_dict = {}
data_dict[
'best_second_difference'] = best_second_difference_indices ##these changed
data_dict['worst_second_difference'] = worst_second_difference_indices
data_dict['best_top_difference'] = best_top_difference_indices
data_dict['worst_top_difference'] = worst_top_difference_indices
data_dict['levels_percentages'] = levels_percentages
data_dict['top_target_percentages'] = top_target_percentages
data_dict['second_target_percentages'] = second_target_percentages
data_dict['levels'] = levels
data_dict['best_top_share'] = best_top_target_share_index
data_dict['worst_top_share'] = worst_top_target_share_index
data_dict['best_second_share'] = best_second_target_share_index
data_dict['worst_second_share'] = worst_second_target_share_index
data_dict['top_target_shares'] = top_target_shares
data_dict['second_target_shares'] = second_target_shares
data_dict['overall_second'] = overall_second_percentage
data_dict['overall_top'] = overall_top_percentage
data_dict['num_significant'] = len(significant_variables)
data_dict['colname'] = analysed_dimension
data_dict['plural_colname'] = NarrativesUtils.pluralize(
analysed_dimension)
data_dict['target'] = target_dimension
data_dict['top_levels'] = top_dims
data_dict['top_levels_percent'] = round(
top_dims_contribution * 100.0 / total, 1)
data_dict['bottom_level'] = bottom_dim
data_dict['bottom_levels'] = bottom_dims
data_dict['bottom_level_percent'] = round(
bottom_dim_contribution * 100 / sum(level_counts), 2)
data_dict['second_target'] = second_target
data_dict['second_target_top_dims'] = second_target_top_dims
data_dict[
'second_target_top_dims_contribution'] = second_target_top_dims_contribution * 100.0 / sum(
second_target_contributions)
data_dict['second_target_bottom_dim'] = second_target_bottom_dim
data_dict[
'second_target_bottom_dim_contribution'] = second_target_bottom_dim_contribution
data_dict['best_second_target'] = levels[best_second_target_index]
data_dict['best_second_target_count'] = second_target_contributions[
best_second_target_index]
data_dict['best_second_target_percent'] = round(
second_target_contributions[best_second_target_index] * 100.0 /
sum(second_target_contributions), 2)
data_dict['worst_second_target'] = levels[worst_second_target_index]
data_dict['worst_second_target_percent'] = round(
second_target_contributions[worst_second_target_index] * 100.0 /
sum(second_target_contributions), 2)
data_dict['top_target'] = top_target
data_dict['top_target_top_dims'] = top_target_top_dims
data_dict[
'top_target_top_dims_contribution'] = top_target_top_dims_contribution * 100.0 / sum(
top_target_contributions)
data_dict['top_target_bottom_dim'] = top_target_bottom_dim
data_dict[
'top_target_bottom_dim_contribution'] = top_target_bottom_dim_contribution
data_dict['best_top_target'] = levels[best_top_target_index]
data_dict['best_top_target_count'] = top_target_contributions[
best_top_target_index]
data_dict['best_top_target_percent'] = round(
top_target_contributions[best_top_target_index] * 100.0 /
sum(top_target_contributions), 2)
data_dict['worst_top_target'] = levels[worst_top_target_index]
data_dict['worst_top_target_percent'] = round(
top_target_contributions[worst_top_target_index] * 100.0 /
sum(top_target_contributions), 2)
data_dict["blockSplitter"] = self._blockSplitter
data_dict["binTargetCol"] = self._binTargetCol
data_dict["binAnalyzedCol"] = self._binAnalyzedCol
data_dict['highlightFlag'] = self._highlightFlag
###############
# CARD1 #
###############
print "self._binTargetCol & self._binAnalyzedCol : ", self._binTargetCol, self._binAnalyzedCol
if (self._binTargetCol == True & self._binAnalyzedCol == False):
print "Only Target Column is Binned, : ", self._binTargetCol
output = NarrativesUtils.block_splitter(
NarrativesUtils.get_template_output(
self._base_dir, 'card1_binned_target.html', data_dict),
self._blockSplitter,
highlightFlag=self._highlightFlag)
elif (self._binTargetCol == True & self._binAnalyzedCol == True):
print "Target Column and IV is Binned : ", self._binTargetCol, self._binAnalyzedCol
output = NarrativesUtils.block_splitter(
NarrativesUtils.get_template_output(
self._base_dir, 'card1_binned_target_and_IV.html',
data_dict),
self._blockSplitter,
highlightFlag=self._highlightFlag)
else:
output = NarrativesUtils.block_splitter(
NarrativesUtils.get_template_output(self._base_dir,
'card1.html', data_dict),
self._blockSplitter,
highlightFlag=self._highlightFlag)
targetDimCard1Data = []
targetDimcard1Heading = '<h3>Relationship between ' + self._target_dimension + ' and ' + self._analysed_dimension + "</h3>"
toggledata = ToggleData()
targetDimTable1Data = self.generate_card1_table1()
targetDimCard1Table1 = TableData()
targetDimCard1Table1.set_table_type("heatMap")
targetDimCard1Table1.set_table_data(targetDimTable1Data)
toggledata.set_toggleon_data({
"data": {
"tableData": targetDimTable1Data,
"tableType": "heatMap"
},
"dataType": "table"
})
targetDimTable2Data = self.generate_card1_table2()
targetDimCard1Table2 = TableData()
targetDimCard1Table2.set_table_type("normal")
table2Data = targetDimTable2Data["data1"]
table2Data = [
innerList[1:] for innerList in table2Data
if innerList[0].strip() != ""
]
targetDimCard1Table2.set_table_data(table2Data)
toggledata.set_toggleoff_data({
"data": {
"tableData": table2Data,
"tableType": "heatMap"
},
"dataType": "table"
})
targetDimCard1Data.append(HtmlData(data=targetDimcard1Heading))
targetDimCard1Data.append(toggledata)
targetDimCard1Data += output
self._card1.set_card_data(targetDimCard1Data)
self._card1.set_card_name("{}: Relationship with {}".format(
self._analysed_dimension, self._target_dimension))
###############
# CARD2 #
###############
if self._appid == None:
key_factors = ''
num_key_factors = len(self._second_level_dimensions)
if len(self._second_level_dimensions) == 5:
key_factors = ', '.join(
self._second_level_dimensions[:4]
) + ' and ' + self._second_level_dimensions[4]
elif len(self._second_level_dimensions) == 4:
key_factors = ', '.join(
self._second_level_dimensions[:3]
) + ' and ' + self._second_level_dimensions[3]
elif len(self._second_level_dimensions) == 3:
key_factors = ', '.join(
self._second_level_dimensions[:2]
) + ' and ' + self._second_level_dimensions[2]
elif len(self._second_level_dimensions) == 2:
key_factors = ' and '.join(self._second_level_dimensions)
elif len(self._second_level_dimensions) == 1:
key_factors = self._second_level_dimensions[0]
targetCardDataDict['num_key_factors'] = num_key_factors
targetCardDataDict['key_factors'] = key_factors
dict_for_test = {}
for tupleObj in sorted_target_levels[:self._chiSquareLevelLimit]:
targetLevel = tupleObj[1]
targetCardDataDict['random_card2'] = random.randint(1, 100)
targetCardDataDict['random_card4'] = random.randint(1, 100)
second_target_contributions = [
table.get_value(targetLevel, i) for i in levels
]
sum_second_target = sum(second_target_contributions)
sorted_levels = sorted(zip(second_target_contributions,
levels),
reverse=True)
level_differences = [0.0] + [
sorted_levels[i][0] - sorted_levels[i + 1][0]
for i in range(len(sorted_levels) - 1)
]
second_target_top_dims = [
j for i, j in sorted_levels[:level_differences.
index(max(level_differences))]
]
second_target_top_dims_contribution = sum([
i for i, j in sorted_levels[:level_differences.
index(max(level_differences))]
])
second_target_bottom_dim = sorted_levels[-1][1]
second_target_bottom_dim_contribution = sorted_levels[-1][0]
second_target_percentages = [
i * 100.0 / sum_second_target
for i in second_target_contributions
]
best_second_target_index = second_target_contributions.index(
max(second_target_contributions))
worst_second_target_index = second_target_contributions.index(
min(second_target_contributions))
second_target_differences = [
x - y for x, y in zip(levels_percentages,
second_target_percentages)
]
if len(second_target_differences) > 6:
tops = 2
bottoms = -2
elif len(second_target_differences) > 4:
tops = 2
bottoms = -1
else:
tops = 1
bottoms = -1
sorted_ = sorted(enumerate(second_target_differences),
key=lambda x: x[1],
reverse=True)
best_second_difference_indices = [x for x, y in sorted_[:tops]]
worst_second_difference_indices = [
x for x, y in sorted_[bottoms:]
]
second_target_shares = [
x * 100.0 / y
for x, y in zip(second_target_contributions, level_counts)
]
max_second_target_shares = max(second_target_shares)
best_second_target_share_index = [
idx for idx, val in enumerate(second_target_shares)
if val == max_second_target_shares
]
level_counts_threshold = sum(level_counts) * 0.05 / len(
level_counts)
min_second_target_shares = min([
x for x, y in zip(second_target_shares, level_counts)
if y >= level_counts_threshold
])
worst_second_target_share_index = [
idx for idx, val in enumerate(second_target_shares)
if val == min_second_target_shares
]
overall_second_percentage = sum_second_target * 100.0 / total
# DataFrame for contribution calculation
df_second_target = self._data_frame.filter(col(self._target_dimension)==targetLevel).\
filter(col(self._analysed_dimension)==second_target_top_dims[0]).\
select(self._second_level_dimensions).toPandas()
df_second_dim = self._data_frame.filter(col(self._analysed_dimension)==second_target_top_dims[0]).\
select(self._second_level_dimensions).toPandas()
# if self._chisquare_result.get_splits():
# splits = self._chisquare_result.get_splits()
# idx = self._chiSquareTable.get_bin_names(splits).index(second_target_top_dims[0])
# idx1 = self._chiSquareTable.get_bin_names(splits).index(top_target_top_dims[0])
# splits[len(splits)-1] = splits[len(splits)-1]+1
# df_second_target = self._data_frame.filter(col(self._target_dimension)==targetLevel).\
# filter(col(self._analysed_dimension)>=splits[idx]).filter(col(self._analysed_dimension)<splits[idx+1]).\
# select(self._second_level_dimensions).toPandas()
# df_second_dim = self._data_frame.filter(col(self._analysed_dimension)>=splits[idx]).\
# filter(col(self._analysed_dimension)<splits[idx+1]).\
# select(self._second_level_dimensions).toPandas()
# else:
# df_second_target = self._data_frame.filter(col(self._target_dimension)==targetLevel).\
# filter(col(self._analysed_dimension)==second_target_top_dims[0]).\
# select(self._second_level_dimensions).toPandas()
# df_second_dim = self._data_frame.filter(col(self._analysed_dimension)==second_target_top_dims[0]).\
# select(self._second_level_dimensions).toPandas()
# print self._data_frame.select('Sales').show()
distribution_second = []
for d in self._second_level_dimensions:
grouped = df_second_target.groupby(d).agg({
d: 'count'
}).sort_values(d, ascending=False)
contributions = df_second_dim.groupby(d).agg({d: 'count'})
contribution_index = list(contributions.index)
contributions_val = contributions[d].tolist()
contributions_list = dict(
zip(contribution_index, contributions_val))
index_list = list(grouped.index)
grouped_list = grouped[d].tolist()
contributions_percent_list = [
round(y * 100.0 / contributions_list[x], 2)
for x, y in zip(index_list, grouped_list)
]
sum_ = grouped[d].sum()
diffs = [0] + [
grouped_list[i] - grouped_list[i + 1]
for i in range(len(grouped_list) - 1)
]
max_diff = diffs.index(max(diffs))
index_txt = ''
if max_diff == 1:
index_txt = index_list[0]
elif max_diff == 2:
index_txt = index_list[0] + '(' + str(
round(grouped_list[0] * 100.0 / sum_, 1)
) + '%)' + ' and ' + index_list[1] + '(' + str(
round(grouped_list[1] * 100.0 / sum_, 1)) + '%)'
elif max_diff > 2:
index_txt = 'including ' + index_list[0] + '(' + str(
round(grouped_list[0] * 100.0 / sum_, 1)
) + '%)' + ' and ' + index_list[1] + '(' + str(
round(grouped_list[1] * 100.0 / sum_, 1)) + '%)'
distribution_second.append({'contributions':[round(i*100.0/sum_,2) for i in grouped_list[:max_diff]],\
'levels': index_list[:max_diff],'variation':random.randint(1,100),\
'index_txt': index_txt, 'd':d,'contributions_percent':contributions_percent_list})
targetCardDataDict['distribution_second'] = distribution_second
targetCardDataDict['second_target'] = targetLevel
targetCardDataDict[
'second_target_top_dims'] = second_target_top_dims
targetCardDataDict[
'second_target_top_dims_contribution'] = second_target_top_dims_contribution * 100.0 / sum(
second_target_contributions)
targetCardDataDict[
'second_target_bottom_dim'] = second_target_bottom_dim
targetCardDataDict[
'second_target_bottom_dim_contribution'] = second_target_bottom_dim_contribution
targetCardDataDict['best_second_target'] = levels[
best_second_target_index]
targetCardDataDict[
'best_second_target_count'] = second_target_contributions[
best_second_target_index]
targetCardDataDict['best_second_target_percent'] = round(
second_target_contributions[best_second_target_index] *
100.0 / sum(second_target_contributions), 2)
targetCardDataDict['worst_second_target'] = levels[
worst_second_target_index]
targetCardDataDict['worst_second_target_percent'] = round(
second_target_contributions[worst_second_target_index] *
100.0 / sum(second_target_contributions), 2)
card2Data = []
targetLevelContributions = [
table.get_value(targetLevel, i) for i in levels
]
card2Heading = '<h3>Distribution of ' + self._target_dimension + ' (' + targetLevel + ') across ' + self._analysed_dimension + "</h3>"
chart, bubble = self.generate_distribution_card_chart(
targetLevel, targetLevelContributions, levels,
level_counts, total)
card2ChartData = NormalChartData(data=chart["data"])
card2ChartJson = ChartJson()
card2ChartJson.set_data(card2ChartData.get_data())
card2ChartJson.set_chart_type("combination")
card2ChartJson.set_types({
"total": "bar",
"percentage": "line"
})
card2ChartJson.set_legend({
"total": "# of " + targetLevel,
"percentage": "% of " + targetLevel
})
card2ChartJson.set_axes({
"x": "key",
"y": "total",
"y2": "percentage"
})
card2ChartJson.set_label_text({
"x": " ",
"y": "Count",
"y2": "Percentage"
})
print "self._binTargetCol & self._binAnalyzedCol : ", self._binTargetCol, self._binAnalyzedCol
if (self._binTargetCol == True & self._binAnalyzedCol ==
False):
print "Only Target Column is Binned"
output2 = NarrativesUtils.block_splitter(
NarrativesUtils.get_template_output(
self._base_dir, 'card2_binned_target.html',
targetCardDataDict), self._blockSplitter)
elif (self._binTargetCol == True & self._binAnalyzedCol ==
True):
print "Target Column and IV is Binned"
output2 = NarrativesUtils.block_splitter(
NarrativesUtils.get_template_output(
self._base_dir, 'card2_binned_target_and_IV.html',
targetCardDataDict), self._blockSplitter)
else:
print "In Else, self._binTargetCol should be False : ", self._binTargetCol
output2 = NarrativesUtils.block_splitter(
NarrativesUtils.get_template_output(
self._base_dir, 'card2.html', targetCardDataDict),
self._blockSplitter)
card2Data.append(HtmlData(data=card2Heading))
statistical_info_array = [
("Test Type", "Chi-Square"),
("Chi-Square statistic",
str(round(self._chisquare_result.get_stat(), 3))),
("P-Value",
str(round(self._chisquare_result.get_pvalue(), 3))),
("Inference",
"Chi-squared analysis shows a significant association between {} (target) and {}."
.format(self._target_dimension, self._analysed_dimension))
]
statistical_info_array = NarrativesUtils.statistical_info_array_formatter(
statistical_info_array)
card2Data.append(
C3ChartData(data=card2ChartJson,
info=statistical_info_array))
card2Data += output2
card2BubbleData = "<div class='col-md-6 col-xs-12'><h2 class='text-center'><span>{}</span><br /><small>{}</small></h2></div><div class='col-md-6 col-xs-12'><h2 class='text-center'><span>{}</span><br /><small>{}</small></h2></div>".format(
bubble[0]["value"], bubble[0]["text"], bubble[1]["value"],
bubble[1]["text"])
card2Data.append(HtmlData(data=card2BubbleData))
targetCard = NormalCard()
targetCard.set_card_data(card2Data)
targetCard.set_card_name("{} : Distribution of {}".format(
self._analysed_dimension, targetLevel))
self._targetCards.append(targetCard)
dict_for_test[targetLevel] = targetCardDataDict
out = {'data_dict': data_dict, 'target_dict': dict_for_test}
return out
# def generate_card2_narratives(self):
def generate_distribution_card_chart(self, __target,
__target_contributions, levels,
levels_count, total):
chart = {}
label = {'total': '# of ' + __target, 'percentage': '% of ' + __target}
label_text = {
'x': self._analysed_dimension,
'y': '# of ' + __target,
'y2': '% of ' + __target,
}
data = {}
data['total'] = dict(zip(levels, __target_contributions))
__target_percentages = [
x * 100.0 / y for x, y in zip(__target_contributions, levels_count)
]
data['percentage'] = dict(zip(levels, __target_percentages))
chartData = []
for val in zip(levels, __target_contributions, __target_percentages):
chartData.append({
"key": val[0],
"total": val[1],
"percentage": val[2]
})
# c3_data = [levels,__target_contributions,__target_percentages]
chart_data = {'label': label, 'data': chartData}
bubble_data1 = {}
bubble_data2 = {}
bubble_data1['value'] = str(
round(
max(__target_contributions) * 100.0 /
sum(__target_contributions), 1)) + '%'
m_index = __target_contributions.index(max(__target_contributions))
bubble_data1[
'text'] = 'Overall ' + __target + ' comes from ' + levels[m_index]
bubble_data2['value'] = str(round(max(__target_percentages), 1)) + '%'
m_index = __target_percentages.index(max(__target_percentages))
bubble_data2[
'text'] = levels[m_index] + ' has the highest rate of ' + __target
bubble_data = [bubble_data1, bubble_data2]
return chart_data, bubble_data
def generate_card1_table1(self):
table_percent_by_column = self._chiSquareTable.table_percent_by_column
column_two_values = self._chiSquareTable.column_two_values
header_row = [self._analysed_dimension
] + self._chiSquareTable.get_column_one_levels()
all_columns = [column_two_values] + table_percent_by_column
other_rows = zip(*all_columns)
other_rows = [list(tup) for tup in other_rows]
table_data = [header_row] + other_rows
return table_data
def generate_card1_table2(self):
table = self._chiSquareTable.table
table_percent = self._chiSquareTable.table_percent
table_percent_by_row = self._chiSquareTable.table_percent_by_row
table_percent_by_column = self._chiSquareTable.table_percent_by_column
target_levels = self._chiSquareTable.get_column_one_levels()
dim_levels = self._chiSquareTable.get_column_two_levels()
header1 = [self._analysed_dimension] + target_levels + ['Total']
header = ['State', 'Active', 'Churn', 'Total'] #TODO remove
data = []
data1 = [['Tag'] + header1]
for idx, lvl in enumerate(dim_levels):
first_row = ['Tag'] + header
col_2_vals = zip(*table)[idx]
data2 = ['bold'] + [lvl] + list(col_2_vals) + [sum(col_2_vals)]
dict_ = dict(zip(first_row, data2))
data.append(dict_)
data1.append(data2)
col_2_vals = zip(*table_percent_by_column)[idx]
data2 = [''] + ['As % within ' + self._analysed_dimension
] + list(col_2_vals) + [100.0]
dict_ = dict(zip(first_row, data2))
data.append(dict_)
data1.append(data2)
col_2_vals = zip(*table_percent_by_row)[idx]
col_2_vals1 = zip(*table_percent)[idx]
data2 = [''] + [
'As % within ' + self._target_dimension
] + list(col_2_vals) + [round(sum(col_2_vals1), 2)]
dict_ = dict(zip(first_row, data2))
data.append(dict_)
data1.append(data2)
# col_2_vals = zip(*table_percent)[idx]
data2 = [''] + ['As % of Total'] + list(col_2_vals1) + [
round(sum(col_2_vals1), 2)
]
dict_ = dict(zip(first_row, data2))
data.append(dict_)
data1.append(data2)
out = {
'header': header,
'header1': header1,
'data': data,
'label': self._analysed_dimension,
'data1': data1
}
return out
def generate_narratives(self):
regression_narrative_obj = LinearRegressionNarrative(
self._df_regression_result,
self._correlations,
self._dataframe_helper,
self._dataframe_context,
self._metaParser,
self._spark
)
main_card_data = regression_narrative_obj.generate_main_card_data()
main_card_narrative = NarrativesUtils.get_template_output(self._base_dir,\
'regression_main_card.html',main_card_data)
self.narratives['main_card'] = {}
self.narratives["main_card"]['paragraphs'] = NarrativesUtils.paragraph_splitter(main_card_narrative)
self.narratives["main_card"]['header'] = 'Key Measures that affect ' + self.result_column
self.narratives["main_card"]['chart'] = {}
self.narratives["main_card"]['chart']['heading'] = ''
self.narratives["main_card"]['chart']['data'] = [[i for i,j in self._all_coeffs],
[j['coefficient'] for i,j in self._all_coeffs]]
self.narratives["main_card"]['chart']['label'] = {'x':'Measure Name',
'y': 'Change in ' + self.result_column + ' per unit increase'}
main_card = NormalCard()
main_card_header = HtmlData(data = '<h3>Key Measures that affect ' + self.result_column+"</h3>")
main_card_paragraphs = NarrativesUtils.block_splitter(main_card_narrative,self._blockSplitter)
main_card_chart_data = [{"key":val[0],"value":val[1]} for val in zip([i for i,j in self._all_coeffs],[j['coefficient'] for i,j in self._all_coeffs])]
main_card_chart = NormalChartData(data=main_card_chart_data)
mainCardChartJson = ChartJson()
mainCardChartJson.set_data(main_card_chart.get_data())
mainCardChartJson.set_label_text({'x':'Influencing Factors','y': 'Change in ' + self.result_column + ' per unit increase'})
mainCardChartJson.set_chart_type("bar")
mainCardChartJson.set_axes({"x":"key","y":"value"})
mainCardChartJson.set_yaxis_number_format(".2f")
# st_info = ["Test : Regression","Threshold for p-value: 0.05", "Effect Size: Regression Coefficient"]
chart_data = sorted(main_card_chart_data,key=lambda x:x["value"],reverse=True)
statistical_info_array=[
("Test Type","Regression"),
("Effect Size","Coefficients"),
("Max Effect Size",chart_data[0]["key"]),
("Min Effect Size",chart_data[-1]["key"]),
]
statistical_inferenc = ""
if len(chart_data) == 1:
statistical_inference = "{} is the only variable that have significant influence over {} (Target) having an \
Effect size of {}".format(chart_data[0]["key"],self._dataframe_context.get_result_column(),round(chart_data[0]["value"],4))
elif len(chart_data) == 2:
statistical_inference = "There are two variables ({} and {}) that have significant influence over {} (Target) and the \
Effect size ranges are {} and {} respectively".format(chart_data[0]["key"],chart_data[1]["key"],self._dataframe_context.get_result_column(),round(chart_data[0]["value"],4),round(chart_data[1]["value"],4))
else:
statistical_inference = "There are {} variables that have significant influence over {} (Target) and the \
Effect size ranges from {} to {}".format(len(chart_data),self._dataframe_context.get_result_column(),round(chart_data[0]["value"],4),round(chart_data[-1]["value"],4))
if statistical_inference != "":
statistical_info_array.append(("Inference",statistical_inference))
statistical_info_array = NarrativesUtils.statistical_info_array_formatter(statistical_info_array)
main_card.set_card_data(data = [main_card_header]+main_card_paragraphs+[C3ChartData(data=mainCardChartJson,info=statistical_info_array)])
main_card.set_card_name("Key Influencers")
self._regressionNode.add_a_card(main_card)
count = 0
for measure_column in self.significant_measures:
sigMeasureNode = NarrativesTree()
sigMeasureNode.set_name(measure_column)
measureCard1 = NormalCard()
measureCard1.set_card_name("{}: Impact on {}".format(measure_column,self.result_column))
measureCard1Data = []
if self._run_dimension_level_regression:
measureCard2 = NormalCard()
measureCard2.set_card_name("Key Areas where it Matters")
measureCard2Data = []
measure_column_cards = {}
card0 = {}
card1data = regression_narrative_obj.generate_card1_data(measure_column)
card1heading = "<h3>Impact of "+measure_column+" on "+self.result_column+"</h3>"
measureCard1Header = HtmlData(data=card1heading)
card1data.update({"blockSplitter":self._blockSplitter})
card1narrative = NarrativesUtils.get_template_output(self._base_dir,\
'regression_card1.html',card1data)
card1paragraphs = NarrativesUtils.block_splitter(card1narrative,self._blockSplitter)
card0 = {"paragraphs":card1paragraphs}
card0["charts"] = {}
card0['charts']['chart2']={}
# card0['charts']['chart2']['data']=card1data["chart_data"]
# card0['charts']['chart2']['heading'] = ''
# card0['charts']['chart2']['labels'] = {}
card0['charts']['chart1']={}
card0["heading"] = card1heading
measure_column_cards['card0'] = card0
measureCard1Header = HtmlData(data=card1heading)
measureCard1Data += [measureCard1Header]
measureCard1para = card1paragraphs
measureCard1Data += measureCard1para
if self._run_dimension_level_regression:
print("running narratives for key area dict")
self._dim_regression = self.run_regression_for_dimension_levels()
card2table, card2data=regression_narrative_obj.generate_card2_data(measure_column,self._dim_regression)
card2data.update({"blockSplitter":self._blockSplitter})
card2narrative = NarrativesUtils.get_template_output(self._base_dir,\
'regression_card2.html',card2data)
card2paragraphs = NarrativesUtils.block_splitter(card2narrative,self._blockSplitter)
card1 = {'tables': card2table, 'paragraphs' : card2paragraphs,
'heading' : 'Key Areas where ' + measure_column + ' matters'}
measure_column_cards['card1'] = card1
measureCard2Data += card2paragraphs
if "table1" in card2table:
table1data = regression_narrative_obj.convert_table_data(card2table["table1"])
card2Table1 = TableData()
card2Table1.set_table_data(table1data)
card2Table1.set_table_type("heatMap")
card2Table1.set_table_top_header(card2table["table1"]["heading"])
card2Table1Json = json.loads(CommonUtils.convert_python_object_to_json(card2Table1))
# measureCard2Data.insert(3,card2Table1)
measureCard2Data.insert(3,card2Table1Json)
if "table2" in card2table:
table2data = regression_narrative_obj.convert_table_data(card2table["table2"])
card2Table2 = TableData()
card2Table2.set_table_data(table2data)
card2Table2.set_table_type("heatMap")
card2Table2.set_table_top_header(card2table["table2"]["heading"])
# measureCard2Data.insert(5,card2Table2)
card2Table2Json = json.loads(CommonUtils.convert_python_object_to_json(card2Table2))
# measureCard2Data.append(card2Table2)
measureCard2Data.append(card2Table2Json)
# self._result_setter.set_trend_section_data({"result_column":self.result_column,
# "measure_column":measure_column,
# "base_dir":self._base_dir
# })
# trend_narratives_obj = TimeSeriesNarrative(self._dataframe_helper, self._dataframe_context, self._result_setter, self._spark, self._story_narrative)
# card2 = trend_narratives_obj.get_regression_trend_card_data()
# if card2:
# measure_column_cards['card2'] = card2
#
#
# card3 = {}
progressMessage = CommonUtils.create_progress_message_object(self._analysisName,"custom","info","Analyzing Key Influencers",self._completionStatus,self._completionStatus,display=True)
CommonUtils.save_progress_message(self._messageURL,progressMessage,ignore=False)
card4data = regression_narrative_obj.generate_card4_data(self.result_column,measure_column)
card4data.update({"blockSplitter":self._blockSplitter})
# card4heading = "Sensitivity Analysis: Effect of "+self.result_column+" on Segments of "+measure_column
card4narrative = NarrativesUtils.get_template_output(self._base_dir,\
'regression_card4.html',card4data)
card4paragraphs = NarrativesUtils.block_splitter(card4narrative,self._blockSplitter)
# card3 = {"paragraphs":card4paragraphs}
card0['paragraphs'] = card1paragraphs+card4paragraphs
card4Chart = card4data["charts"]
# st_info = ["Test : Regression", "Variables : "+ self.result_column +", "+measure_column,"Intercept : "+str(round(self._df_regression_result.get_intercept(),2)), "Regression Coefficient : "+ str(round(self._df_regression_result.get_coeff(measure_column),2))]
statistical_info_array=[
("Test Type","Regression"),
("Coefficient",str(round(self._df_regression_result.get_coeff(measure_column),2))),
("P-Value","<= 0.05"),
("Intercept",str(round(self._df_regression_result.get_intercept(),2))),
("R Square ",str(round(self._df_regression_result.get_rsquare(),2))),
]
inferenceTuple = ()
coeff = self._df_regression_result.get_coeff(measure_column)
if coeff > 0:
inferenceTuple = ("Inference","For every additional unit of increase in {} there will be an increase of {} units in {} (target).".format(measure_column,str(round(coeff,2)),self._dataframe_context.get_result_column()))
else:
inferenceTuple = ("Inference","For every additional unit of decrease in {} there will be an decrease of {} units in {} (target).".format(measure_column,str(round(coeff,2)),self._dataframe_context.get_result_column()))
if len(inferenceTuple) > 0:
statistical_info_array.append(inferenceTuple)
statistical_info_array = NarrativesUtils.statistical_info_array_formatter(statistical_info_array)
card4paragraphs.insert(2,C3ChartData(data=card4Chart,info=statistical_info_array))
measureCard1Data += card4paragraphs
self.narratives['cards'].append(measure_column_cards)
if count == 0:
card4data.pop("charts")
self._result_setter.update_executive_summary_data(card4data)
count += 1
measureCard1.set_card_data(measureCard1Data)
if self._run_dimension_level_regression:
measureCard2.set_card_data(measureCard2Data)
sigMeasureNode.add_cards([measureCard1,measureCard2])
sigMeasureNode.add_cards([measureCard1])
self._regressionNode.add_a_node(sigMeasureNode)
# self._result_setter.set_trend_section_completion_status(True)
self._story_narrative.add_a_node(self._regressionNode)
class ChiSquareAnalysis(object):
def __init__(self,
df_context,
df_helper,
chisquare_result,
target_dimension,
analysed_dimension,
significant_variables,
num_analysed_variables,
data_frame,
measure_columns,
base_dir,
appid=None,
target_chisquare_result=None):
self._blockSplitter = "|~NEWBLOCK~|"
self._highlightFlag = "|~HIGHLIGHT~|"
self._dimensionNode = NarrativesTree()
self._dimensionNode.set_name(target_dimension)
self._data_frame = data_frame
self._dataframe_context = df_context
self._pandas_flag = df_context._pandas_flag
self._dataframe_helper = df_helper
self._chisquare_result = chisquare_result
self._target_dimension = target_dimension
self._analysed_dimension = analysed_dimension
self._significant_variables = significant_variables
self._target_chisquare_result = target_chisquare_result
self._measure_columns = self._dataframe_helper.get_numeric_columns()
self._chiSquareLevelLimit = GLOBALSETTINGS.CHISQUARELEVELLIMIT
self._num_analysed_variables = num_analysed_variables
self._chiSquareTable = chisquare_result.get_contingency_table()
significant_variables = list(
set(significant_variables) - {analysed_dimension})
if len(significant_variables) <= 20:
if len(significant_variables) <= 3:
self._second_level_dimensions = list(significant_variables)
else:
self._second_level_dimensions = list(significant_variables)[:3]
else:
self._second_level_dimensions = list(significant_variables)[:5]
print(self._second_level_dimensions)
self._appid = appid
self._card1 = NormalCard()
self._targetCards = []
self._base_dir = base_dir
self._binTargetCol = False
self._binAnalyzedCol = False
print("--------Chi-Square Narratives for ", analysed_dimension,
"---------")
if self._dataframe_context.get_custom_analysis_details() != None:
binnedColObj = [
x["colName"]
for x in self._dataframe_context.get_custom_analysis_details()
]
print("analysed_dimension : ", self._analysed_dimension)
if binnedColObj != None and self._target_dimension in binnedColObj:
self._binTargetCol = True
if binnedColObj != None and (
self._analysed_dimension in binnedColObj
or self._analysed_dimension in self._measure_columns):
self._binAnalyzedCol = True
if self._appid == None:
self._generate_narratives()
self._dimensionNode.add_cards([self._card1] + self._targetCards)
self._dimensionNode.set_name("{}".format(analysed_dimension))
elif self._appid == "2":
self._generate_narratives()
self._dimensionNode.add_cards([self._card1])
self._dimensionNode.set_name("{}".format(analysed_dimension))
elif self._appid == "1":
self._generate_narratives()
self._dimensionNode.add_cards([self._card1])
self._dimensionNode.set_name("{}".format(analysed_dimension))
def get_dimension_node(self):
return json.loads(
CommonUtils.convert_python_object_to_json(self._dimensionNode))
def get_dimension_card1(self):
return self._card1
def _generate_narratives(self):
chisquare_result = self._chisquare_result
target_dimension = self._target_dimension
analysed_dimension = self._analysed_dimension
significant_variables = self._significant_variables
num_analysed_variables = self._num_analysed_variables
table = self._chiSquareTable
total = self._chiSquareTable.get_total()
levels = self._chiSquareTable.get_column_two_levels()
level_counts = self._chiSquareTable.get_column_total()
levels_count_sum = sum(level_counts)
levels_percentages = [
old_div(i * 100.0, levels_count_sum) for i in level_counts
]
sorted_levels = sorted(zip(level_counts, levels), reverse=True)
level_differences = [0.0] + [
sorted_levels[i][0] - sorted_levels[i + 1][0]
for i in range(len(sorted_levels) - 1)
]
top_dims = [
j for i, j in
sorted_levels[:level_differences.index(max(level_differences))]
]
top_dims_contribution = sum([
i for i, j in
sorted_levels[:level_differences.index(max(level_differences))]
])
bottom_dim = sorted_levels[-1][1]
bottom_dim_contribution = sorted_levels[-1][0]
bottom_dims = [
y for x, y in sorted_levels if x == bottom_dim_contribution
]
target_levels = self._chiSquareTable.get_column_one_levels()
target_counts = self._chiSquareTable.get_row_total()
sorted_target_levels = sorted(zip(target_counts, target_levels),
reverse=True)
top_target_count, top_target = sorted_target_levels[0]
second_target_count, second_target = sorted_target_levels[1]
top_target_contributions = [
table.get_value(top_target, i) for i in levels
]
sum_top_target = sum(top_target_contributions)
sorted_levels = sorted(zip(top_target_contributions, levels),
reverse=True)
level_differences = [0.0] + [
sorted_levels[i][0] - sorted_levels[i + 1][0]
for i in range(len(sorted_levels) - 1)
]
top_target_top_dims = [
j for i, j in
sorted_levels[:level_differences.index(max(level_differences))]
]
top_target_top_dims_contribution = sum([
i for i, j in
sorted_levels[:level_differences.index(max(level_differences))]
])
top_target_bottom_dim = sorted_levels[-1][1]
top_target_bottom_dim_contribution = sorted_levels[-1][0]
top_target_percentages = [
old_div(i * 100.0, sum_top_target)
for i in top_target_contributions
]
best_top_target_index = top_target_contributions.index(
max(top_target_contributions))
worst_top_target_index = top_target_contributions.index(
min(top_target_contributions))
top_target_differences = [
x - y for x, y in zip(levels_percentages, top_target_percentages)
]
if len(top_target_differences) > 6:
tops = 2
bottoms = -2
elif len(top_target_differences) > 4:
tops = 2
bottoms = -1
else:
tops = 1
bottoms = -1
sorted_ = sorted(enumerate(top_target_differences),
key=lambda x: x[1],
reverse=True)
best_top_difference_indices = [x for x, y in sorted_[:tops]]
worst_top_difference_indices = [x for x, y in sorted_[bottoms:]]
top_target_shares = [
old_div(x * 100.0, y)
for x, y in zip(top_target_contributions, level_counts)
]
max_top_target_shares = max(top_target_shares)
best_top_target_share_index = [
idx for idx, val in enumerate(top_target_shares)
if val == max_top_target_shares
]
level_counts_threshold = old_div(
sum(level_counts) * 0.05, len(level_counts))
min_top_target_shares = min([
x for x, y in zip(top_target_shares, level_counts)
if y >= level_counts_threshold
])
if max_top_target_shares == min_top_target_shares:
worst_top_target_share_index = []
else:
worst_top_target_share_index = [
idx for idx, val in enumerate(top_target_shares)
if val == min_top_target_shares
]
overall_top_percentage = old_div(sum_top_target * 100.0, total)
second_target_contributions = [
table.get_value(second_target, i) for i in levels
]
sum_second_target = sum(second_target_contributions)
sorted_levels = sorted(zip(second_target_contributions, levels),
reverse=True)
level_differences = [0.0] + [
sorted_levels[i][0] - sorted_levels[i + 1][0]
for i in range(len(sorted_levels) - 1)
]
second_target_top_dims = [
j for i, j in
sorted_levels[:level_differences.index(max(level_differences))]
]
second_target_top_dims_contribution = sum([
i for i, j in
sorted_levels[:level_differences.index(max(level_differences))]
])
second_target_bottom_dim = sorted_levels[-1][1]
second_target_bottom_dim_contribution = sorted_levels[-1][0]
second_target_percentages = [
old_div(i * 100.0, sum_second_target)
for i in second_target_contributions
]
best_second_target_index = second_target_contributions.index(
max(second_target_contributions))
worst_second_target_index = second_target_contributions.index(
min(second_target_contributions))
second_target_differences = [
x - y
for x, y in zip(levels_percentages, second_target_percentages)
]
if len(second_target_differences) > 6:
tops = 2
bottoms = -2
elif len(second_target_differences) > 4:
tops = 2
bottoms = -1
else:
tops = 1
bottoms = -1
sorted_ = sorted(enumerate(second_target_differences),
key=lambda x: x[1],
reverse=True)
best_second_difference_indices = [x for x, y in sorted_[:tops]]
worst_second_difference_indices = [x for x, y in sorted_[bottoms:]]
second_target_shares = [
old_div(x * 100.0, y)
for x, y in zip(second_target_contributions, level_counts)
]
max_second_target_shares = max(second_target_shares)
best_second_target_share_index = [
idx for idx, val in enumerate(second_target_shares)
if val == max_second_target_shares
]
level_counts_threshold = old_div(
sum(level_counts) * 0.05, len(level_counts))
if min(second_target_shares) == 0:
min_second_target_shares = min([
x for x, y in zip(second_target_shares, level_counts) if x != 0
])
else:
min_second_target_shares = min([
x for x, y in zip(second_target_shares, level_counts)
if y >= level_counts_threshold
])
# worst_second_target_share_index = second_target_shares.index(min_second_target_shares)
if max_second_target_shares == min_second_target_shares:
worst_second_target_share_index = []
else:
worst_second_target_share_index = [
idx for idx, val in enumerate(second_target_shares)
if val == min_second_target_shares
]
overall_second_percentage = old_div(sum_second_target * 100.0, total)
targetCardDataDict = {}
targetCardDataDict['target'] = target_dimension
targetCardDataDict['colname'] = analysed_dimension
targetCardDataDict['num_significant'] = len(significant_variables)
targetCardDataDict['plural_colname'] = NarrativesUtils.pluralize(
analysed_dimension)
targetCardDataDict["blockSplitter"] = self._blockSplitter
targetCardDataDict["binTargetCol"] = self._binTargetCol
targetCardDataDict["binAnalyzedCol"] = self._binAnalyzedCol
targetCardDataDict['highlightFlag'] = self._highlightFlag
targetCardDataDict['levels'] = levels
data_dict = {}
data_dict[
'best_second_difference'] = best_second_difference_indices ##these changed
data_dict['worst_second_difference'] = worst_second_difference_indices
data_dict['best_top_difference'] = best_top_difference_indices
data_dict['worst_top_difference'] = worst_top_difference_indices
data_dict['levels_percentages'] = levels_percentages
data_dict['top_target_percentages'] = top_target_percentages
data_dict['second_target_percentages'] = second_target_percentages
data_dict['levels'] = levels
data_dict['best_top_share'] = best_top_target_share_index
data_dict['worst_top_share'] = worst_top_target_share_index
data_dict['best_second_share'] = best_second_target_share_index
data_dict['worst_second_share'] = worst_second_target_share_index
data_dict['top_target_shares'] = top_target_shares
data_dict['second_target_shares'] = second_target_shares
data_dict['overall_second'] = overall_second_percentage
data_dict['overall_top'] = overall_top_percentage
data_dict['num_significant'] = len(significant_variables)
data_dict['colname'] = analysed_dimension
data_dict['plural_colname'] = NarrativesUtils.pluralize(
analysed_dimension)
data_dict['target'] = target_dimension
data_dict['top_levels'] = top_dims
data_dict['top_levels_percent'] = round(
old_div(top_dims_contribution * 100.0, total), 1)
data_dict['bottom_level'] = bottom_dim
data_dict['bottom_levels'] = bottom_dims
data_dict['bottom_level_percent'] = round(
old_div(bottom_dim_contribution * 100, sum(level_counts)), 2)
data_dict['second_target'] = second_target
data_dict['second_target_top_dims'] = second_target_top_dims
data_dict['second_target_top_dims_contribution'] = old_div(
second_target_top_dims_contribution * 100.0,
sum(second_target_contributions))
data_dict['second_target_bottom_dim'] = second_target_bottom_dim
data_dict[
'second_target_bottom_dim_contribution'] = second_target_bottom_dim_contribution
data_dict['best_second_target'] = levels[best_second_target_index]
data_dict['best_second_target_count'] = second_target_contributions[
best_second_target_index]
data_dict['best_second_target_percent'] = round(
old_div(
second_target_contributions[best_second_target_index] * 100.0,
sum(second_target_contributions)), 2)
data_dict['worst_second_target'] = levels[worst_second_target_index]
data_dict['worst_second_target_percent'] = round(
old_div(
second_target_contributions[worst_second_target_index] * 100.0,
sum(second_target_contributions)), 2)
data_dict['top_target'] = top_target
data_dict['top_target_top_dims'] = top_target_top_dims
data_dict['top_target_top_dims_contribution'] = old_div(
top_target_top_dims_contribution * 100.0,
sum(top_target_contributions))
data_dict['top_target_bottom_dim'] = top_target_bottom_dim
data_dict[
'top_target_bottom_dim_contribution'] = top_target_bottom_dim_contribution
data_dict['best_top_target'] = levels[best_top_target_index]
data_dict['best_top_target_count'] = top_target_contributions[
best_top_target_index]
data_dict['best_top_target_percent'] = round(
old_div(top_target_contributions[best_top_target_index] * 100.0,
sum(top_target_contributions)), 2)
data_dict['worst_top_target'] = levels[worst_top_target_index]
data_dict['worst_top_target_percent'] = round(
old_div(top_target_contributions[worst_top_target_index] * 100.0,
sum(top_target_contributions)), 2)
data_dict["blockSplitter"] = self._blockSplitter
data_dict["binTargetCol"] = self._binTargetCol
data_dict["binAnalyzedCol"] = self._binAnalyzedCol
data_dict['highlightFlag'] = self._highlightFlag
# print "_"*60
# print "DATA DICT - ", data_dict
# print "_"*60
###############
# CARD1 #
###############
print("self._binTargetCol & self._binAnalyzedCol : ",
self._binTargetCol, self._binAnalyzedCol)
if len(data_dict['worst_second_share']) == 0:
output = NarrativesUtils.block_splitter(
NarrativesUtils.get_template_output(
self._base_dir, 'card1_binned_target_worst_second.html',
data_dict),
self._blockSplitter,
highlightFlag=self._highlightFlag)
else:
if (self._binTargetCol == True & self._binAnalyzedCol == False):
print("Only Target Column is Binned, : ", self._binTargetCol)
output = NarrativesUtils.block_splitter(
NarrativesUtils.get_template_output(
self._base_dir, 'card1_binned_target.html', data_dict),
self._blockSplitter,
highlightFlag=self._highlightFlag)
elif (self._binTargetCol == True & self._binAnalyzedCol == True):
print("Target Column and IV is Binned : ", self._binTargetCol,
self._binAnalyzedCol)
output = NarrativesUtils.block_splitter(
NarrativesUtils.get_template_output(
self._base_dir, 'card1_binned_target_and_IV.html',
data_dict),
self._blockSplitter,
highlightFlag=self._highlightFlag)
else:
output = NarrativesUtils.block_splitter(
NarrativesUtils.get_template_output(
self._base_dir, 'card1.html', data_dict),
self._blockSplitter,
highlightFlag=self._highlightFlag)
targetDimCard1Data = []
targetDimcard1Heading = '<h3>Impact of ' + self._analysed_dimension + ' on ' + self._target_dimension + "</h3>"
toggledata = ToggleData()
targetDimTable1Data = self.generate_card1_table1()
targetDimCard1Table1 = TableData()
targetDimCard1Table1.set_table_type("heatMap")
targetDimCard1Table1.set_table_data(targetDimTable1Data)
toggledata.set_toggleon_data({
"data": {
"tableData": targetDimTable1Data,
"tableType": "heatMap"
},
"dataType": "table"
})
targetDimTable2Data = self.generate_card1_table2()
targetDimCard1Table2 = TableData()
targetDimCard1Table2.set_table_type("normal")
table2Data = targetDimTable2Data["data1"]
table2Data = [
innerList[1:] for innerList in table2Data
if innerList[0].strip() != ""
]
targetDimCard1Table2.set_table_data(table2Data)
toggledata.set_toggleoff_data({
"data": {
"tableData": table2Data,
"tableType": "heatMap"
},
"dataType": "table"
})
targetDimCard1Data.append(HtmlData(data=targetDimcard1Heading))
targetDimCard1Data.append(toggledata)
targetDimCard1Data += output
self._card1.set_card_data(targetDimCard1Data)
self._card1.set_card_name("{}: Relationship with {}".format(
self._analysed_dimension, self._target_dimension))
###############
# CARD2 #
###############
if self._appid == None:
key_factors = ''
num_key_factors = len(self._second_level_dimensions)
if len(self._second_level_dimensions) == 5:
key_factors = ', '.join(
self._second_level_dimensions[:4]
) + ' and ' + self._second_level_dimensions[4]
elif len(self._second_level_dimensions) == 4:
key_factors = ', '.join(
self._second_level_dimensions[:3]
) + ' and ' + self._second_level_dimensions[3]
elif len(self._second_level_dimensions) == 3:
key_factors = ', '.join(
self._second_level_dimensions[:2]
) + ' and ' + self._second_level_dimensions[2]
elif len(self._second_level_dimensions) == 2:
key_factors = ' and '.join(self._second_level_dimensions)
elif len(self._second_level_dimensions) == 1:
key_factors = self._second_level_dimensions[0]
targetCardDataDict['num_key_factors'] = num_key_factors
targetCardDataDict['key_factors'] = key_factors
dict_for_test = {}
for tupleObj in sorted_target_levels[:self._chiSquareLevelLimit]:
targetLevel = tupleObj[1]
targetCardDataDict['random_card2'] = random.randint(1, 100)
targetCardDataDict['random_card4'] = random.randint(1, 100)
second_target_contributions = [
table.get_value(targetLevel, i) for i in levels
]
sum_second_target = sum(second_target_contributions)
sorted_levels = sorted(zip(second_target_contributions,
levels),
reverse=True)
level_differences = [0.0] + [
sorted_levels[i][0] - sorted_levels[i + 1][0]
for i in range(len(sorted_levels) - 1)
]
level_diff_index = level_differences.index(
max(level_differences)) if level_differences.index(
max(level_differences)) > 0 else len(
level_differences
) ##added for pipeline keyerror issue
second_target_top_dims = [
j for i, j in sorted_levels[:level_diff_index]
]
second_target_top_dims_contribution = sum([
i for i, j in sorted_levels[:level_differences.
index(max(level_differences))]
])
second_target_bottom_dim = sorted_levels[-1][1]
second_target_bottom_dim_contribution = sorted_levels[-1][0]
second_target_percentages = [
old_div(i * 100.0, sum_second_target)
for i in second_target_contributions
]
best_second_target_index = second_target_contributions.index(
max(second_target_contributions))
worst_second_target_index = second_target_contributions.index(
min(second_target_contributions))
second_target_differences = [
x - y for x, y in zip(levels_percentages,
second_target_percentages)
]
if len(second_target_differences) > 6:
tops = 2
bottoms = -2
elif len(second_target_differences) > 4:
tops = 2
bottoms = -1
else:
tops = 1
bottoms = -1
sorted_ = sorted(enumerate(second_target_differences),
key=lambda x: x[1],
reverse=True)
best_second_difference_indices = [x for x, y in sorted_[:tops]]
worst_second_difference_indices = [
x for x, y in sorted_[bottoms:]
]
second_target_shares = [
old_div(x * 100.0, y)
for x, y in zip(second_target_contributions, level_counts)
]
max_second_target_shares = max(second_target_shares)
best_second_target_share_index = [
idx for idx, val in enumerate(second_target_shares)
if val == max_second_target_shares
]
level_counts_threshold = old_div(
sum(level_counts) * 0.05, len(level_counts))
min_second_target_shares = min([
x for x, y in zip(second_target_shares, level_counts)
if y >= level_counts_threshold
])
worst_second_target_share_index = [
idx for idx, val in enumerate(second_target_shares)
if val == min_second_target_shares
]
overall_second_percentage = old_div(sum_second_target * 100.0,
total)
# DataFrame for contribution calculation
if self._pandas_flag:
df_second_target = self._data_frame[(
self._data_frame[self._target_dimension] == targetLevel
) & (self._data_frame[self._analysed_dimension] ==
second_target_top_dims[0])][
self._second_level_dimensions]
df_second_dim = self._data_frame[(
self._data_frame[self._analysed_dimension] ==
second_target_top_dims[0]
)][self._second_level_dimensions]
else:
df_second_target = self._data_frame.filter(col(self._target_dimension)==targetLevel).\
filter(col(self._analysed_dimension)==second_target_top_dims[0]).\
select(self._second_level_dimensions).toPandas()
df_second_dim = self._data_frame.filter(col(self._analysed_dimension)==second_target_top_dims[0]).\
select(self._second_level_dimensions).toPandas()
# if self._chisquare_result.get_splits():
# splits = self._chisquare_result.get_splits()
# idx = self._chiSquareTable.get_bin_names(splits).index(second_target_top_dims[0])
# idx1 = self._chiSquareTable.get_bin_names(splits).index(top_target_top_dims[0])
# splits[len(splits)-1] = splits[len(splits)-1]+1
# df_second_target = self._data_frame.filter(col(self._target_dimension)==targetLevel).\
# filter(col(self._analysed_dimension)>=splits[idx]).filter(col(self._analysed_dimension)<splits[idx+1]).\
# select(self._second_level_dimensions).toPandas()
# df_second_dim = self._data_frame.filter(col(self._analysed_dimension)>=splits[idx]).\
# filter(col(self._analysed_dimension)<splits[idx+1]).\
# select(self._second_level_dimensions).toPandas()
# else:
# df_second_target = self._data_frame.filter(col(self._target_dimension)==targetLevel).\
# filter(col(self._analysed_dimension)==second_target_top_dims[0]).\
# select(self._second_level_dimensions).toPandas()
# df_second_dim = self._data_frame.filter(col(self._analysed_dimension)==second_target_top_dims[0]).\
# select(self._second_level_dimensions).toPandas()
# print self._data_frame.select('Sales').show()
distribution_second = []
d_l = []
for d in self._second_level_dimensions:
grouped = df_second_target.groupby(d).agg({d: 'count'})
contributions = df_second_dim.groupby(d).agg({d: 'count'})
contribution_index = list(contributions.index)
contributions_val = contributions[d].tolist()
contributions_list = dict(
list(zip(contribution_index, contributions_val)))
index_list = list(grouped.index)
grouped_list = grouped[d].tolist()
contributions_percent_list = [
round(old_div(y * 100.0, contributions_list[x]), 2)
for x, y in zip(index_list, grouped_list)
]
sum_ = grouped[d].sum()
diffs = [0] + [
grouped_list[i] - grouped_list[i + 1]
for i in range(len(grouped_list) - 1)
]
max_diff = diffs.index(max(diffs))
grouped_dict = dict(list(zip(index_list, grouped_list)))
for val in contribution_index:
if val not in list(grouped_dict.keys()):
grouped_dict[val] = 0
else:
pass
index_list = []
grouped_list = []
contributions_val = []
for key in list(grouped_dict.keys()):
index_list.append(str(key))
grouped_list.append(grouped_dict[key])
contributions_val.append(contributions_list[key])
'''
print "="*70
print "GROUPED - ", grouped
print "INDEX LIST - ", index_list
print "GROUPED LIST - ", grouped_list
print "GROUPED DICT - ", grouped_dict
print "CONTRIBUTIONS - ", contributions
print "CONTRIBUTION INDEX - ", contribution_index
print "CONTRIBUTIONS VAL - ", contributions_val
print "CONTRIBUTIONS LIST - ", contributions_list
print "CONTRIBUTIONS PERCENT LIST - ", contributions_percent_list
print "SUM - ", sum_
print "DIFFS - ", diffs
print "MAX DIFF - ", max_diff
print "="*70
'''
informative_dict = {
"levels": index_list,
"positive_class_contribution": grouped_list,
"positive_plus_others": contributions_val
}
informative_df = pd.DataFrame(informative_dict)
informative_df["percentage_horizontal"] = old_div(
informative_df["positive_class_contribution"] * 100,
informative_df["positive_plus_others"])
informative_df["percentage_vertical"] = old_div(
informative_df["positive_class_contribution"] * 100,
sum_)
informative_df.sort_values(["percentage_vertical"],
inplace=True,
ascending=False)
informative_df = informative_df.reset_index(drop=True)
percentage_vertical_sorted = list(
informative_df["percentage_vertical"])
percentage_horizontal_sorted = list(
informative_df["percentage_horizontal"])
levels_sorted = list(informative_df["levels"])
differences_list = []
for i in range(1, len(percentage_vertical_sorted)):
difference = percentage_vertical_sorted[
i - 1] - percentage_vertical_sorted[i]
differences_list.append(round(difference, 2))
'''
print "-"*70
print "DIFFERENCES LIST - ", differences_list
print "-"*70
'''
index_txt = ''
if differences_list:
if differences_list[0] >= 30:
print("showing 1st case")
index_txt = levels_sorted[0]
max_diff_equivalent = 1
else:
if len(differences_list) >= 2:
if differences_list[1] >= 10:
print("showing 1st and 2nd case")
index_txt = levels_sorted[0] + '(' + str(
round(percentage_vertical_sorted[0], 1)
) + '%)' + ' and ' + levels_sorted[
1] + '(' + str(
round(
percentage_vertical_sorted[1],
1)) + '%)'
max_diff_equivalent = 2
else:
print("showing 3rd case")
index_txt = 'including ' + levels_sorted[
0] + '(' + str(
round(
percentage_vertical_sorted[0],
1)
) + '%)' + ' and ' + levels_sorted[
1] + '(' + str(
round(
percentage_vertical_sorted[
1], 1)) + '%)'
max_diff_equivalent = 3
else:
print("showing 3rd case")
index_txt = 'including ' + levels_sorted[
0] + '(' + str(
round(percentage_vertical_sorted[0], 1)
) + '%)' + ' and ' + levels_sorted[
1] + '(' + str(
round(
percentage_vertical_sorted[1],
1)) + '%)'
max_diff_equivalent = 3
else:
max_diff_equivalent = 0
'''
print "-"*70
print informative_df.head(25)
print "-"*70
'''
distribution_second.append({
'contributions': [
round(i, 2) for i in
percentage_vertical_sorted[:max_diff_equivalent]
],
'levels':
levels_sorted[:max_diff_equivalent],
'variation':
random.randint(1, 100),
'index_txt':
index_txt,
'd':
d,
'contributions_percent':
percentage_horizontal_sorted
})
'''
print "DISTRIBUTION SECOND - ", distribution_second
print "<>"*50
'''
targetCardDataDict['distribution_second'] = distribution_second
targetCardDataDict['second_target'] = targetLevel
targetCardDataDict[
'second_target_top_dims'] = second_target_top_dims
targetCardDataDict[
'second_target_top_dims_contribution'] = old_div(
second_target_top_dims_contribution * 100.0,
sum(second_target_contributions))
targetCardDataDict[
'second_target_bottom_dim'] = second_target_bottom_dim
targetCardDataDict[
'second_target_bottom_dim_contribution'] = second_target_bottom_dim_contribution
targetCardDataDict['best_second_target'] = levels[
best_second_target_index]
targetCardDataDict[
'best_second_target_count'] = second_target_contributions[
best_second_target_index]
targetCardDataDict['best_second_target_percent'] = round(
old_div(
second_target_contributions[best_second_target_index] *
100.0, sum(second_target_contributions)), 2)
targetCardDataDict['worst_second_target'] = levels[
worst_second_target_index]
targetCardDataDict['worst_second_target_percent'] = round(
old_div(
second_target_contributions[worst_second_target_index]
* 100.0, sum(second_target_contributions)), 2)
card2Data = []
targetLevelContributions = [
table.get_value(targetLevel, i) for i in levels
]
impact_target_thershold = old_div(
sum(targetLevelContributions) * 0.02,
len(targetLevelContributions))
card2Heading = '<h3>Key Drivers of ' + self._target_dimension + ' (' + targetLevel + ')' + "</h3>"
chart, bubble = self.generate_distribution_card_chart(
targetLevel, targetLevelContributions, levels,
level_counts, total, impact_target_thershold)
card2ChartData = NormalChartData(data=chart["data"])
"rounding the chartdata values for key drivers tab inside table percentage(table data)"
for d in card2ChartData.get_data():
d['percentage'] = round(d['percentage'], 2)
d_l.append(d)
card2ChartJson = ChartJson()
card2ChartJson.set_data(d_l)
card2ChartJson.set_chart_type("combination")
card2ChartJson.set_types({
"total": "bar",
"percentage": "line"
})
card2ChartJson.set_legend({
"total": "# of " + targetLevel,
"percentage": "% of " + targetLevel
})
card2ChartJson.set_axes({
"x": "key",
"y": "total",
"y2": "percentage"
})
card2ChartJson.set_label_text({
"x": " ",
"y": "Count",
"y2": "Percentage"
})
print("self._binTargetCol & self._binAnalyzedCol : ",
self._binTargetCol, self._binAnalyzedCol)
if (self._binTargetCol == True & self._binAnalyzedCol ==
False):
print("Only Target Column is Binned")
output2 = NarrativesUtils.block_splitter(
NarrativesUtils.get_template_output(
self._base_dir, 'card2_binned_target.html',
targetCardDataDict), self._blockSplitter)
elif (self._binTargetCol == True & self._binAnalyzedCol ==
True):
print("Target Column and IV is Binned")
output2 = NarrativesUtils.block_splitter(
NarrativesUtils.get_template_output(
self._base_dir, 'card2_binned_target_and_IV.html',
targetCardDataDict), self._blockSplitter)
else:
print("In Else, self._binTargetCol should be False : ",
self._binTargetCol)
output2 = NarrativesUtils.block_splitter(
NarrativesUtils.get_template_output(
self._base_dir, 'card2.html', targetCardDataDict),
self._blockSplitter)
card2Data.append(HtmlData(data=card2Heading))
statistical_info_array = [
("Test Type", "Chi-Square"),
("Chi-Square statistic",
str(round(self._chisquare_result.get_stat(), 3))),
("P-Value",
str(round(self._chisquare_result.get_pvalue(), 3))),
("Inference",
"Chi-squared analysis shows a significant association between {} (target) and {}."
.format(self._target_dimension, self._analysed_dimension))
]
statistical_info_array = NarrativesUtils.statistical_info_array_formatter(
statistical_info_array)
card2Data.append(
C3ChartData(data=card2ChartJson,
info=statistical_info_array))
card2Data += output2
card2BubbleData = "<div class='col-md-6 col-xs-12'><h2 class='text-center'><span>{}</span><br /><small>{}</small></h2></div><div class='col-md-6 col-xs-12'><h2 class='text-center'><span>{}</span><br /><small>{}</small></h2></div>".format(
bubble[0]["value"], bubble[0]["text"], bubble[1]["value"],
bubble[1]["text"])
card2Data.append(HtmlData(data=card2BubbleData))
targetCard = NormalCard()
targetCard.set_card_data(card2Data)
targetCard.set_card_name("{} : Distribution of {}".format(
self._analysed_dimension, targetLevel))
self._targetCards.append(targetCard)
dict_for_test[targetLevel] = targetCardDataDict
out = {'data_dict': data_dict, 'target_dict': dict_for_test}
return out
# def generate_card2_narratives(self):
def generate_distribution_card_chart(self, __target,
__target_contributions, levels,
levels_count, total, thershold):
chart = {}
label = {'total': '# of ' + __target, 'percentage': '% of ' + __target}
label_text = {
'x': self._analysed_dimension,
'y': '# of ' + __target,
'y2': '% of ' + __target,
}
data = {}
data['total'] = dict(list(zip(levels, __target_contributions)))
__target_percentages = [
old_div(x * 100.0, y)
for x, y in zip(__target_contributions, levels_count)
]
data['percentage'] = dict(list(zip(levels, __target_percentages)))
chartData = []
for val in zip(levels, __target_contributions, __target_percentages):
chartData.append({
"key": val[0],
"total": val[1],
"percentage": val[2]
})
# c3_data = [levels,__target_contributions,__target_percentages]
chart_data = {'label': label, 'data': chartData}
bubble_data1 = {}
bubble_data2 = {}
bubble_data1['value'] = str(
round(
old_div(
max(__target_contributions) * 100.0,
sum(__target_contributions)), 1)) + '%'
m_index = __target_contributions.index(max(__target_contributions))
bubble_data1[
'text'] = 'Overall ' + __target + ' comes from ' + levels[m_index]
intial = -1
for k, v, i in zip(__target_contributions, __target_percentages,
list(range(len(__target_contributions)))):
if k > thershold:
if intial < v:
intial = v
bubble_data2['value'] = str(round(intial)) + '%'
#m_index = __target_percentages.index(i)
bubble_data2['text'] = levels[
i] + ' has the highest rate of ' + __target
bubble_data = [bubble_data1, bubble_data2]
return chart_data, bubble_data
def generate_card1_table1(self):
table_percent_by_column = self._chiSquareTable.table_percent_by_column
column_two_values = self._chiSquareTable.column_two_values
header_row = [self._analysed_dimension
] + self._chiSquareTable.get_column_one_levels()
all_columns = [column_two_values] + table_percent_by_column
other_rows = list(zip(*all_columns))
other_rows = [list(tup) for tup in other_rows]
table_data = [header_row] + other_rows
return table_data
def generate_card1_table2(self):
table = self._chiSquareTable.table
table_percent = self._chiSquareTable.table_percent
table_percent_by_row = self._chiSquareTable.table_percent_by_row
table_percent_by_column = self._chiSquareTable.table_percent_by_column
target_levels = self._chiSquareTable.get_column_one_levels()
dim_levels = self._chiSquareTable.get_column_two_levels()
header1 = [self._analysed_dimension] + target_levels + ['Total']
header = ['State', 'Active', 'Churn', 'Total'] #TODO remove
data = []
data1 = [['Tag'] + header1]
for idx, lvl in enumerate(dim_levels):
first_row = ['Tag'] + header
col_2_vals = list(zip(*table))[idx]
data2 = ['bold'] + [lvl] + list(col_2_vals) + [sum(col_2_vals)]
dict_ = dict(list(zip(first_row, data2)))
data.append(dict_)
data1.append(data2)
col_2_vals = list(zip(*table_percent_by_column))[idx]
data2 = [''] + ['As % within ' + self._analysed_dimension
] + list(col_2_vals) + [100.0]
dict_ = dict(list(zip(first_row, data2)))
data.append(dict_)
data1.append(data2)
col_2_vals = list(zip(*table_percent_by_row))[idx]
col_2_vals1 = list(zip(*table_percent))[idx]
data2 = [''] + [
'As % within ' + self._target_dimension
] + list(col_2_vals) + [round(sum(col_2_vals1), 2)]
dict_ = dict(list(zip(first_row, data2)))
data.append(dict_)
data1.append(data2)
# col_2_vals = zip(*table_percent)[idx]
data2 = [''] + ['As % of Total'] + list(col_2_vals1) + [
round(sum(col_2_vals1), 2)
]
dict_ = dict(list(zip(first_row, data2)))
data.append(dict_)
data1.append(data2)
out = {
'header': header,
'header1': header1,
'data': data,
'label': self._analysed_dimension,
'data1': data1
}
return out