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)
def Predict(self):
self._scriptWeightDict = self._dataframe_context.get_ml_model_prediction_weight(
)
self._scriptStages = {
"initialization": {
"summary": "Initialized the Random Forest Scripts",
"weight": 2
},
"prediction": {
"summary": "Random Forest Model Prediction Finished",
"weight": 2
},
"frequency": {
"summary": "descriptive analysis finished",
"weight": 2
},
"chisquare": {
"summary": "chi Square analysis finished",
"weight": 4
},
"completion": {
"summary": "all analysis finished",
"weight": 4
},
}
self._completionStatus += old_div(
self._scriptWeightDict[self._analysisName]["total"] *
self._scriptStages["initialization"]["weight"], 10)
progressMessage = CommonUtils.create_progress_message_object(self._analysisName,\
"initialization",\
"info",\
self._scriptStages["initialization"]["summary"],\
self._completionStatus,\
self._completionStatus)
CommonUtils.save_progress_message(self._messageURL,
progressMessage,
ignore=self._ignoreMsg)
self._dataframe_context.update_completion_status(
self._completionStatus)
# Match with the level_counts and then clean the data
dataSanity = True
level_counts_train = self._dataframe_context.get_level_count_dict()
cat_cols = self._dataframe_helper.get_string_columns()
# level_counts_score = CommonUtils.get_level_count_dict(self._data_frame,cat_cols,self._dataframe_context.get_column_separator(),output_type="dict")
# if level_counts_train != {}:
# for key in level_counts_train:
# if key in level_counts_score:
# if level_counts_train[key] != level_counts_score[key]:
# dataSanity = False
# else:
# dataSanity = False
categorical_columns = self._dataframe_helper.get_string_columns()
uid_col = self._dataframe_context.get_uid_column()
if self._metaParser.check_column_isin_ignored_suggestion(uid_col):
categorical_columns = list(set(categorical_columns) - {uid_col})
allDateCols = self._dataframe_context.get_date_columns()
categorical_columns = list(set(categorical_columns) - set(allDateCols))
numerical_columns = self._dataframe_helper.get_numeric_columns()
result_column = self._dataframe_context.get_result_column()
test_data_path = self._dataframe_context.get_input_file()
if self._mlEnv == "spark":
pass
elif self._mlEnv == "sklearn":
score_data_path = self._dataframe_context.get_score_path(
) + "/data.csv"
if score_data_path.startswith("file"):
score_data_path = score_data_path[7:]
trained_model_path = self._dataframe_context.get_model_path()
trained_model_path += "/" + self._dataframe_context.get_model_for_scoring(
) + ".pkl"
if trained_model_path.startswith("file"):
trained_model_path = trained_model_path[7:]
score_summary_path = self._dataframe_context.get_score_path(
) + "/Summary/summary.json"
if score_summary_path.startswith("file"):
score_summary_path = score_summary_path[7:]
trained_model = joblib.load(trained_model_path)
# pandas_df = self._data_frame.toPandas()
df = self._data_frame.toPandas()
model_columns = self._dataframe_context.get_model_features()
pandas_df = MLUtils.create_dummy_columns(
df, [x for x in categorical_columns if x != result_column])
pandas_df = MLUtils.fill_missing_columns(pandas_df, model_columns,
result_column)
if uid_col:
pandas_df = pandas_df[[
x for x in pandas_df.columns if x != uid_col
]]
y_score = trained_model.predict(pandas_df)
y_prob = trained_model.predict_proba(pandas_df)
y_prob = MLUtils.calculate_predicted_probability(y_prob)
y_prob = list([round(x, 2) for x in y_prob])
score = {
"predicted_class": y_score,
"predicted_probability": y_prob
}
df["predicted_class"] = score["predicted_class"]
labelMappingDict = self._dataframe_context.get_label_map()
df["predicted_class"] = df["predicted_class"].apply(
lambda x: labelMappingDict[x] if x != None else "NA")
df["predicted_probability"] = score["predicted_probability"]
self._score_summary[
"prediction_split"] = MLUtils.calculate_scored_probability_stats(
df)
self._score_summary["result_column"] = result_column
if result_column in df.columns:
df.drop(result_column, axis=1, inplace=True)
df = df.rename(index=str, columns={"predicted_class": result_column})
df.to_csv(score_data_path, header=True, index=False)
uidCol = self._dataframe_context.get_uid_column()
if uidCol == None:
uidCols = self._metaParser.get_suggested_uid_columns()
if len(uidCols) > 0:
uidCol = uidCols[0]
uidTableData = []
predictedClasses = list(df[result_column].unique())
if uidCol:
if uidCol in df.columns:
for level in predictedClasses:
levelDf = df[df[result_column] == level]
levelDf = levelDf[[
uidCol, "predicted_probability", result_column
]]
levelDf.sort_values(by="predicted_probability",
ascending=False,
inplace=True)
levelDf["predicted_probability"] = levelDf[
"predicted_probability"].apply(
lambda x: humanize.apnumber(x * 100) + "%"
if x * 100 >= 10 else str(int(x * 100)) + "%")
uidTableData.append(levelDf[:5])
uidTableData = pd.concat(uidTableData)
uidTableData = [list(arr) for arr in list(uidTableData.values)]
uidTableData = [[uidCol, "Probability", result_column]
] + uidTableData
uidTable = TableData()
uidTable.set_table_width(25)
uidTable.set_table_data(uidTableData)
uidTable.set_table_type("normalHideColumn")
self._result_setter.set_unique_identifier_table(
json.loads(
CommonUtils.convert_python_object_to_json(uidTable)))
self._completionStatus += old_div(
self._scriptWeightDict[self._analysisName]["total"] *
self._scriptStages["prediction"]["weight"], 10)
progressMessage = CommonUtils.create_progress_message_object(self._analysisName,\
"prediction",\
"info",\
self._scriptStages["prediction"]["summary"],\
self._completionStatus,\
self._completionStatus)
CommonUtils.save_progress_message(self._messageURL,
progressMessage,
ignore=self._ignoreMsg)
self._dataframe_context.update_completion_status(
self._completionStatus)
# CommonUtils.write_to_file(score_summary_path,json.dumps({"scoreSummary":self._score_summary}))
print("STARTING DIMENSION ANALYSIS ...")
columns_to_keep = []
columns_to_drop = []
# considercolumnstype = self._dataframe_context.get_score_consider_columns_type()
# considercolumns = self._dataframe_context.get_score_consider_columns()
# if considercolumnstype != None:
# if considercolumns != None:
# if considercolumnstype == ["excluding"]:
# columns_to_drop = considercolumns
# elif considercolumnstype == ["including"]:
# columns_to_keep = considercolumns
columns_to_keep = self._dataframe_context.get_score_consider_columns()
if len(columns_to_keep) > 0:
columns_to_drop = list(set(df.columns) - set(columns_to_keep))
else:
columns_to_drop += ["predicted_probability"]
columns_to_drop = [
x for x in columns_to_drop
if x in df.columns and x != result_column
]
print("columns_to_drop", columns_to_drop)
df.drop(columns_to_drop, axis=1, inplace=True)
resultColLevelCount = dict(df[result_column].value_counts())
# self._metaParser.update_level_counts(result_column,resultColLevelCount)
self._metaParser.update_column_dict(
result_column, {
"LevelCount": resultColLevelCount,
"numberOfUniqueValues": len(list(resultColLevelCount.keys()))
})
self._dataframe_context.set_story_on_scored_data(True)
SQLctx = SQLContext(sparkContext=self._spark.sparkContext,
sparkSession=self._spark)
spark_scored_df = SQLctx.createDataFrame(df)
# spark_scored_df.write.csv(score_data_path+"/data",mode="overwrite",header=True)
# TODO update metadata for the newly created dataframe
self._dataframe_context.update_consider_columns(columns_to_keep)
df_helper = DataFrameHelper(spark_scored_df, self._dataframe_context,
self._metaParser)
df_helper.set_params()
spark_scored_df = df_helper.get_data_frame()
# try:
# fs = time.time()
# narratives_file = self._dataframe_context.get_score_path()+"/narratives/FreqDimension/data.json"
# if narratives_file.startswith("file"):
# narratives_file = narratives_file[7:]
# result_file = self._dataframe_context.get_score_path()+"/results/FreqDimension/data.json"
# if result_file.startswith("file"):
# result_file = result_file[7:]
# init_freq_dim = FreqDimensions(df, df_helper, self._dataframe_context,scriptWeight=self._scriptWeightDict,analysisName=self._analysisName)
# df_freq_dimension_obj = init_freq_dim.test_all(dimension_columns=[result_column])
# df_freq_dimension_result = CommonUtils.as_dict(df_freq_dimension_obj)
# narratives_obj = DimensionColumnNarrative(result_column, df_helper, self._dataframe_context, df_freq_dimension_obj,self._result_setter,self._prediction_narrative,scriptWeight=self._scriptWeightDict,analysisName=self._analysisName)
# narratives = CommonUtils.as_dict(narratives_obj)
#
# print "Frequency Analysis Done in ", time.time() - fs, " seconds."
# self._completionStatus += self._scriptWeightDict[self._analysisName]["total"]*self._scriptStages["frequency"]["weight"]/10
# progressMessage = CommonUtils.create_progress_message_object(self._analysisName,\
# "frequency",\
# "info",\
# self._scriptStages["frequency"]["summary"],\
# self._completionStatus,\
# self._completionStatus)
# CommonUtils.save_progress_message(self._messageURL,progressMessage,ignore=self._ignoreMsg)
# self._dataframe_context.update_completion_status(self._completionStatus)
# print "Frequency ",self._completionStatus
# except:
# print "Frequency Analysis Failed "
#
# try:
# fs = time.time()
# narratives_file = self._dataframe_context.get_score_path()+"/narratives/ChiSquare/data.json"
# if narratives_file.startswith("file"):
# narratives_file = narratives_file[7:]
# result_file = self._dataframe_context.get_score_path()+"/results/ChiSquare/data.json"
# if result_file.startswith("file"):
# result_file = result_file[7:]
# init_chisquare_obj = ChiSquare(df, df_helper, self._dataframe_context,scriptWeight=self._scriptWeightDict,analysisName=self._analysisName)
# df_chisquare_obj = init_chisquare_obj.test_all(dimension_columns= [result_column])
# df_chisquare_result = CommonUtils.as_dict(df_chisquare_obj)
# chisquare_narratives = CommonUtils.as_dict(ChiSquareNarratives(df_helper, df_chisquare_obj, self._dataframe_context,df,self._prediction_narrative,self._result_setter,scriptWeight=self._scriptWeightDict,analysisName=self._analysisName))
# except:
# print "ChiSquare Analysis Failed "
if len(predictedClasses) >= 2:
try:
fs = time.time()
df_decision_tree_obj = DecisionTrees(
spark_scored_df,
df_helper,
self._dataframe_context,
self._spark,
self._metaParser,
scriptWeight=self._scriptWeightDict,
analysisName=self._analysisName).test_all(
dimension_columns=[result_column])
narratives_obj = CommonUtils.as_dict(
DecisionTreeNarrative(result_column,
df_decision_tree_obj,
self._dataframe_helper,
self._dataframe_context,
self._metaParser,
self._result_setter,
story_narrative=None,
analysisName=self._analysisName,
scriptWeight=self._scriptWeightDict))
print(narratives_obj)
except:
print("DecisionTree Analysis Failed ")
else:
data_dict = {
"npred": len(predictedClasses),
"nactual": len(list(labelMappingDict.values()))
}
if data_dict["nactual"] > 2:
levelCountDict[predictedClasses[0]] = resultColLevelCount[
predictedClasses[0]]
levelCountDict["Others"] = sum([
v for k, v in list(resultColLevelCount.items())
if k != predictedClasses[0]
])
else:
levelCountDict = resultColLevelCount
otherClass = list(
set(labelMappingDict.values()) - set(predictedClasses))[0]
levelCountDict[otherClass] = 0
print(levelCountDict)
total = float(
sum([x for x in list(levelCountDict.values()) if x != None]))
levelCountTuple = [({
"name":
k,
"count":
v,
"percentage":
humanize.apnumber(old_div(v * 100, total)) +
"%" if old_div(v * 100, total) >= 10 else
str(int(old_div(v * 100, total))) + "%"
}) for k, v in list(levelCountDict.items()) if v != None]
levelCountTuple = sorted(levelCountTuple,
key=lambda x: x["count"],
reverse=True)
data_dict["blockSplitter"] = "|~NEWBLOCK~|"
data_dict["targetcol"] = result_column
data_dict["nlevel"] = len(list(levelCountDict.keys()))
data_dict["topLevel"] = levelCountTuple[0]
data_dict["secondLevel"] = levelCountTuple[1]
maincardSummary = NarrativesUtils.get_template_output(
"/apps/", 'scorewithoutdtree.html', data_dict)
main_card = NormalCard()
main_card_data = []
main_card_narrative = NarrativesUtils.block_splitter(
maincardSummary, "|~NEWBLOCK~|")
main_card_data += main_card_narrative
chartData = NormalChartData([levelCountDict]).get_data()
chartJson = ChartJson(data=chartData)
chartJson.set_title(result_column)
chartJson.set_chart_type("donut")
mainCardChart = C3ChartData(data=chartJson)
mainCardChart.set_width_percent(33)
main_card_data.append(mainCardChart)
uidTable = self._result_setter.get_unique_identifier_table()
if uidTable != None:
main_card_data.append(uidTable)
main_card.set_card_data(main_card_data)
main_card.set_card_name(
"Predicting Key Drivers of {}".format(result_column))
self._result_setter.set_score_dtree_cards([main_card], {})
def Predict(self):
self._scriptWeightDict = self._dataframe_context.get_ml_model_prediction_weight(
)
self._scriptStages = {
"initialization": {
"summary": "Initialized the Naive Bayes Scripts",
"weight": 2
},
"prediction": {
"summary": "Spark ML Naive Bayes Model Prediction Finished",
"weight": 2
},
"frequency": {
"summary": "descriptive analysis finished",
"weight": 2
},
"chisquare": {
"summary": "chi Square analysis finished",
"weight": 4
},
"completion": {
"summary": "all analysis finished",
"weight": 4
},
}
self._completionStatus += self._scriptWeightDict[self._analysisName][
"total"] * self._scriptStages["initialization"]["weight"] / 10
progressMessage = CommonUtils.create_progress_message_object(self._analysisName,\
"initialization",\
"info",\
self._scriptStages["initialization"]["summary"],\
self._completionStatus,\
self._completionStatus)
CommonUtils.save_progress_message(self._messageURL, progressMessage)
self._dataframe_context.update_completion_status(
self._completionStatus)
SQLctx = SQLContext(sparkContext=self._spark.sparkContext,
sparkSession=self._spark)
dataSanity = True
level_counts_train = self._dataframe_context.get_level_count_dict()
categorical_columns = self._dataframe_helper.get_string_columns()
numerical_columns = self._dataframe_helper.get_numeric_columns()
time_dimension_columns = self._dataframe_helper.get_timestamp_columns()
result_column = self._dataframe_context.get_result_column()
categorical_columns = [
x for x in categorical_columns if x != result_column
]
level_counts_score = CommonUtils.get_level_count_dict(
self._data_frame,
categorical_columns,
self._dataframe_context.get_column_separator(),
output_type="dict",
dataType="spark")
for key in level_counts_train:
if key in level_counts_score:
if level_counts_train[key] != level_counts_score[key]:
dataSanity = False
else:
dataSanity = False
test_data_path = self._dataframe_context.get_input_file()
score_data_path = self._dataframe_context.get_score_path(
) + "/data.csv"
trained_model_path = self._dataframe_context.get_model_path()
trained_model_path = "/".join(
trained_model_path.split("/")[:-1]
) + "/" + self._slug + "/" + self._dataframe_context.get_model_for_scoring(
)
# score_summary_path = self._dataframe_context.get_score_path()+"/Summary/summary.json"
pipelineModel = MLUtils.load_pipeline(trained_model_path)
df = self._data_frame
transformed = pipelineModel.transform(df)
label_indexer_dict = MLUtils.read_string_indexer_mapping(
trained_model_path, SQLctx)
prediction_to_levels = udf(lambda x: label_indexer_dict[x],
StringType())
transformed = transformed.withColumn(
result_column, prediction_to_levels(transformed.prediction))
if "probability" in transformed.columns:
probability_dataframe = transformed.select(
[result_column, "probability"]).toPandas()
probability_dataframe = probability_dataframe.rename(
index=str, columns={result_column: "predicted_class"})
probability_dataframe[
"predicted_probability"] = probability_dataframe[
"probability"].apply(lambda x: max(x))
self._score_summary[
"prediction_split"] = MLUtils.calculate_scored_probability_stats(
probability_dataframe)
self._score_summary["result_column"] = result_column
scored_dataframe = transformed.select(
categorical_columns + time_dimension_columns +
numerical_columns + [result_column, "probability"]).toPandas()
scored_dataframe['predicted_probability'] = probability_dataframe[
"predicted_probability"].values
# scored_dataframe = scored_dataframe.rename(index=str, columns={"predicted_probability": "probability"})
else:
self._score_summary["prediction_split"] = []
self._score_summary["result_column"] = result_column
scored_dataframe = transformed.select(categorical_columns +
time_dimension_columns +
numerical_columns +
[result_column]).toPandas()
labelMappingDict = self._dataframe_context.get_label_map()
if score_data_path.startswith("file"):
score_data_path = score_data_path[7:]
scored_dataframe.to_csv(score_data_path, header=True, index=False)
uidCol = self._dataframe_context.get_uid_column()
if uidCol == None:
uidCols = self._metaParser.get_suggested_uid_columns()
if len(uidCols) > 0:
uidCol = uidCols[0]
uidTableData = []
predictedClasses = list(scored_dataframe[result_column].unique())
if uidCol:
if uidCol in df.columns:
for level in predictedClasses:
levelDf = scored_dataframe[scored_dataframe[result_column]
== level]
levelDf = levelDf[[
uidCol, "predicted_probability", result_column
]]
levelDf.sort_values(by="predicted_probability",
ascending=False,
inplace=True)
levelDf["predicted_probability"] = levelDf[
"predicted_probability"].apply(
lambda x: humanize.apnumber(x * 100) + "%"
if x * 100 >= 10 else str(int(x * 100)) + "%")
uidTableData.append(levelDf[:5])
uidTableData = pd.concat(uidTableData)
uidTableData = [list(arr) for arr in list(uidTableData.values)]
uidTableData = [[uidCol, "Probability", result_column]
] + uidTableData
uidTable = TableData()
uidTable.set_table_width(25)
uidTable.set_table_data(uidTableData)
uidTable.set_table_type("normalHideColumn")
self._result_setter.set_unique_identifier_table(
json.loads(
CommonUtils.convert_python_object_to_json(uidTable)))
self._completionStatus += self._scriptWeightDict[self._analysisName][
"total"] * self._scriptStages["prediction"]["weight"] / 10
progressMessage = CommonUtils.create_progress_message_object(self._analysisName,\
"prediction",\
"info",\
self._scriptStages["prediction"]["summary"],\
self._completionStatus,\
self._completionStatus)
CommonUtils.save_progress_message(self._messageURL, progressMessage)
self._dataframe_context.update_completion_status(
self._completionStatus)
print("STARTING DIMENSION ANALYSIS ...")
columns_to_keep = []
columns_to_drop = []
columns_to_keep = self._dataframe_context.get_score_consider_columns()
if len(columns_to_keep) > 0:
columns_to_drop = list(set(df.columns) - set(columns_to_keep))
else:
columns_to_drop += ["predicted_probability"]
scored_df = transformed.select(categorical_columns +
time_dimension_columns +
numerical_columns + [result_column])
columns_to_drop = [
x for x in columns_to_drop if x in scored_df.columns
]
modified_df = scored_df.select(
[x for x in scored_df.columns if x not in columns_to_drop])
resultColLevelCount = dict(
modified_df.groupby(result_column).count().collect())
self._metaParser.update_column_dict(
result_column, {
"LevelCount": resultColLevelCount,
"numberOfUniqueValues": len(resultColLevelCount.keys())
})
self._dataframe_context.set_story_on_scored_data(True)
self._dataframe_context.update_consider_columns(columns_to_keep)
df_helper = DataFrameHelper(modified_df, self._dataframe_context,
self._metaParser)
df_helper.set_params()
spark_scored_df = df_helper.get_data_frame()
if len(predictedClasses) >= 2:
try:
fs = time.time()
df_decision_tree_obj = DecisionTrees(
spark_scored_df,
df_helper,
self._dataframe_context,
self._spark,
self._metaParser,
scriptWeight=self._scriptWeightDict,
analysisName=self._analysisName).test_all(
dimension_columns=[result_column])
narratives_obj = CommonUtils.as_dict(
DecisionTreeNarrative(result_column,
df_decision_tree_obj,
self._dataframe_helper,
self._dataframe_context,
self._metaParser,
self._result_setter,
story_narrative=None,
analysisName=self._analysisName,
scriptWeight=self._scriptWeightDict))
print(narratives_obj)
except Exception as e:
print("DecisionTree Analysis Failed ", str(e))
else:
data_dict = {
"npred": len(predictedClasses),
"nactual": len(labelMappingDict.values())
}
if data_dict["nactual"] > 2:
levelCountDict[predictedClasses[0]] = resultColLevelCount[
predictedClasses[0]]
levelCountDict["Others"] = sum([
v for k, v in resultColLevelCount.items()
if k != predictedClasses[0]
])
else:
levelCountDict = resultColLevelCount
otherClass = list(
set(labelMappingDict.values()) - set(predictedClasses))[0]
levelCountDict[otherClass] = 0
print(levelCountDict)
total = float(
sum([x for x in levelCountDict.values() if x != None]))
levelCountTuple = [({
"name":
k,
"count":
v,
"percentage":
humanize.apnumber(v * 100 / total) + "%"
}) for k, v in levelCountDict.items() if v != None]
levelCountTuple = sorted(levelCountTuple,
key=lambda x: x["count"],
reverse=True)
data_dict["blockSplitter"] = "|~NEWBLOCK~|"
data_dict["targetcol"] = result_column
data_dict["nlevel"] = len(levelCountDict.keys())
data_dict["topLevel"] = levelCountTuple[0]
data_dict["secondLevel"] = levelCountTuple[1]
maincardSummary = NarrativesUtils.get_template_output(
"/apps/", 'scorewithoutdtree.html', data_dict)
main_card = NormalCard()
main_card_data = []
main_card_narrative = NarrativesUtils.block_splitter(
maincardSummary, "|~NEWBLOCK~|")
main_card_data += main_card_narrative
chartData = NormalChartData([levelCountDict]).get_data()
chartJson = ChartJson(data=chartData)
chartJson.set_title(result_column)
chartJson.set_chart_type("donut")
mainCardChart = C3ChartData(data=chartJson)
mainCardChart.set_width_percent(33)
main_card_data.append(mainCardChart)
uidTable = self._result_setter.get_unique_identifier_table()
if uidTable != None:
main_card_data.append(uidTable)
main_card.set_card_data(main_card_data)
main_card.set_card_name(
"Predicting Key Drivers of {}".format(result_column))
self._result_setter.set_score_dtree_cards([main_card], {})
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_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_summary(self):
data_dict = {}
rules_dict = self._table
data_dict["blockSplitter"] = self._blockSplitter
data_dict["targetcol"] = self._colname
groups = rules_dict.keys()
probabilityCutoff = 75
probabilityGroups = [{
"probability": probabilityCutoff,
"count": 0,
"range": [probabilityCutoff, 100]
}, {
"probability": probabilityCutoff - 1,
"count": 0,
"range": [0, probabilityCutoff - 1]
}]
tableArray = [[
"Prediction Rule", "Probability", "Prediction", "Freq", "group",
"richRules"
]]
dropdownData = []
chartDict = {}
targetLevel = self._dataframe_context.get_target_level_for_model()
probabilityArrayAll = []
self._completionStatus = self._dataframe_context.get_completion_status(
)
progressMessage = CommonUtils.create_progress_message_object(
self._analysisName,
"custom",
"info",
"Generating Prediction rules",
self._completionStatus,
self._completionStatus,
display=True)
CommonUtils.save_progress_message(self._messageURL,
progressMessage,
ignore=False)
self._dataframe_context.update_completion_status(
self._completionStatus)
targetValues = [x for x in rules_dict.keys() if x == targetLevel
] + [x for x in rules_dict.keys() if x != targetLevel]
for idx, target in enumerate(targetValues):
if idx == 0:
if self._dataframe_context.get_story_on_scored_data() != True:
dropdownData.append({
"displayName": target,
"name": target,
"selected": True,
"id": idx + 1
})
else:
dropdownData.append({
"displayName":
"{} : {}".format(self._colname, target),
"name":
target,
"selected":
True,
"id":
idx + 1
})
else:
if self._dataframe_context.get_story_on_scored_data() != True:
dropdownData.append({
"displayName": target,
"name": target,
"selected": False,
"id": idx + 1
})
else:
dropdownData.append({
"displayName":
"{} : {}".format(self._colname, target),
"name":
target,
"selected":
False,
"id":
idx + 1
})
rulesArray = rules_dict[target]
probabilityArray = [
round(x, 2) for x in self.success_percent[target]
]
probabilityArrayAll += probabilityArray
groupArray = [
"strong" if x >= probabilityCutoff else "mixed"
for x in probabilityArray
]
for idx2, obj in enumerate(probabilityGroups):
grpCount = len([
x for x in probabilityArray
if x >= obj["range"][0] and x <= obj["range"][1]
])
obj["count"] += grpCount
probabilityGroups[idx2] = obj
predictionArray = [target] * len(rulesArray)
freqArray = self.total_predictions[target]
chartDict[target] = sum(freqArray)
success = self.successful_predictions[target]
success_percent = self.success_percent[target]
richRulesArray = []
crudeRuleArray = []
analysisType = self._dataframe_context.get_analysis_type()
targetCol = self._dataframe_context.get_result_column()
binFlag = False
if self._dataframe_context.get_custom_analysis_details() != None:
binnedColObj = [
x["colName"] for x in
self._dataframe_context.get_custom_analysis_details()
]
if binnedColObj != None and targetCol in binnedColObj:
binFlag = True
for idx2, crudeRule in enumerate(rulesArray):
richRule, crudeRule = NarrativesUtils.generate_rules(
self._colname,
target,
crudeRule,
freqArray[idx2],
success[idx2],
success_percent[idx2],
analysisType,
binFlag=binFlag)
richRulesArray.append(richRule)
crudeRuleArray.append(crudeRule)
probabilityArray = map(
lambda x: humanize.apnumber(x) + "%"
if x >= 10 else str(int(x)) + "%", probabilityArray)
# targetArray = zip(richRulesArray,probabilityArray,predictionArray,freqArray,groupArray)
targetArray = zip(crudeRuleArray, probabilityArray,
predictionArray, freqArray, groupArray,
richRulesArray)
targetArray = [list(x) for x in targetArray]
tableArray += targetArray
donutChartMaxLevel = 10
if self._dataframe_context.get_story_on_scored_data() == True:
chartDict = {}
probabilityRangeForChart = GLOBALSETTINGS.PROBABILITY_RANGE_FOR_DONUT_CHART
chartDict = dict(
zip(probabilityRangeForChart.keys(),
[0] * len(probabilityRangeForChart)))
for val in probabilityArrayAll:
for grps, grpRange in probabilityRangeForChart.items():
if val > grpRange[0] and val <= grpRange[1]:
chartDict[grps] = chartDict[grps] + 1
chartDict = {k: v for k, v in chartDict.items() if v != 0}
else:
chartDict = dict([(k, sum(v))
for k, v in self.total_predictions.items()])
chartDict = {k: v for k, v in chartDict.items() if v != 0}
if len(chartDict) > donutChartMaxLevel:
chartDict = NarrativesUtils.restructure_donut_chart_data(
chartDict, nLevels=donutChartMaxLevel)
chartData = NormalChartData([chartDict]).get_data()
chartJson = ChartJson(data=chartData)
chartJson.set_title(self._colname)
chartJson.set_chart_type("donut")
mainCardChart = C3ChartData(data=chartJson)
mainCardChart.set_width_percent(45)
# mainCardChart = {"dataType": "c3Chart","widthPercent":33 ,"data": {"data": [chartDict],"title":self._colname,"axes":{},"label_text":{},"legend":{},"yAxisNumberFormat": ".2s","types":None,"axisRotation":False, "chart_type": "donut"}}
dropdownDict = {
"dataType": "dropdown",
"label": "Showing prediction rules for",
"data": dropdownData
}
data_dict["probabilityGroups"] = probabilityGroups
if self._dataframe_context.get_story_on_scored_data() != True:
maincardSummary = NarrativesUtils.get_template_output(self._base_dir,\
'decisiontreesummary.html',data_dict)
else:
predictedLevelcountArray = [(x[2], x[3]) for x in tableArray[1:]]
predictedLevelCountDict = {}
# predictedLevelcountDict = defaultdict(predictedLevelcountArray)
for val in predictedLevelcountArray:
predictedLevelCountDict.setdefault(val[0], []).append(val[1])
levelCountDict = {}
for k, v in predictedLevelCountDict.items():
levelCountDict[k] = sum(v)
# levelCountDict = self._metaParser.get_unique_level_dict(self._colname)
total = float(
sum([x for x in levelCountDict.values() if x != None]))
levelCountTuple = [{
"name": k,
"count": v,
"percentage": round(v * 100 / total, 2)
} for k, v in levelCountDict.items() if v != None]
percentageArray = [x["percentage"] for x in levelCountTuple]
percentageArray = NarrativesUtils.ret_smart_round(percentageArray)
levelCountTuple = [{
"name": obj["name"],
"count": obj["count"],
"percentage": str(percentageArray[idx]) + "%"
} for idx, obj in enumerate(levelCountTuple)]
data_dict["nlevel"] = len(levelCountDict)
print "levelCountTuple", levelCountTuple
print "levelCountDict", levelCountDict
if targetLevel in levelCountDict:
data_dict["topLevel"] = [
x for x in levelCountTuple if x["name"] == targetLevel
][0]
if len(levelCountTuple) > 1:
data_dict["secondLevel"] = max([
x for x in levelCountTuple if x["name"] != targetLevel
],
key=lambda x: x["count"])
else:
data_dict["secondLevel"] = None
else:
data_dict["topLevel"] = levelCountTuple[0]
if len(levelCountTuple) > 1:
data_dict["secondLevel"] = levelCountTuple[1]
else:
data_dict["secondLevel"] = None
print data_dict
maincardSummary = NarrativesUtils.get_template_output(
self._base_dir, 'decisiontreescore.html', data_dict)
main_card = NormalCard()
main_card_data = []
main_card_narrative = NarrativesUtils.block_splitter(
maincardSummary, self._blockSplitter)
main_card_data += main_card_narrative
main_card_data.append(mainCardChart)
main_card_data.append(dropdownDict)
main_card_table = TableData()
if self._dataframe_context.get_story_on_scored_data() == True:
main_card_table.set_table_width(75)
main_card_table.set_table_data(tableArray)
main_card_table.set_table_type("popupDecisionTreeTable")
main_card_data.append(main_card_table)
uidTable = self._result_setter.get_unique_identifier_table()
if uidTable != None:
main_card_data.append(uidTable)
else:
main_card_table.set_table_width(100)
main_card.set_card_data(main_card_data)
main_card.set_card_name("Predicting Key Drivers of {}".format(
self._colname))
self._decisionTreeNode.add_a_card(main_card)
def _generate_summary(self):
data_dict = {}
rules_dict = self._table
data_dict["blockSplitter"] = self._blockSplitter
data_dict["targetcol"] = self._colname
groups = rules_dict.keys()
probabilityCutoff = 75
probabilityGroups = [{
"probability": probabilityCutoff,
"count": 0,
"range": [probabilityCutoff, 100]
}, {
"probability": probabilityCutoff - 1,
"count": 0,
"range": [0, probabilityCutoff - 1]
}]
tableArray = [[
"Prediction Rule", "Probability", "Prediction", "Freq", "group",
"richRules"
]]
dropdownData = []
chartDict = {}
self._completionStatus = self._dataframe_context.get_completion_status(
)
progressMessage = CommonUtils.create_progress_message_object(
self._analysisName,
"custom",
"info",
"Generating Prediction rules",
self._completionStatus,
self._completionStatus,
display=True)
CommonUtils.save_progress_message(self._messageURL,
progressMessage,
ignore=False)
for idx, target in enumerate(rules_dict.keys()):
targetToDisplayInTable = target.split(":")[0].strip()
if idx == 0:
dropdownData.append({
"displayName": target,
"name": targetToDisplayInTable,
"searchTerm": targetToDisplayInTable,
"selected": True,
"id": idx + 1
})
else:
dropdownData.append({
"displayName": target,
"name": targetToDisplayInTable,
"searchTerm": targetToDisplayInTable,
"selected": False,
"id": idx + 1
})
rulesArray = rules_dict[target]
probabilityArray = [
round(x, 2) for x in self.success_percent[target]
]
groupArray = [
"strong" if x >= probabilityCutoff else "mixed"
for x in probabilityArray
]
for idx2, obj in enumerate(probabilityGroups):
grpCount = len([
x for x in probabilityArray
if x >= obj["range"][0] and x <= obj["range"][1]
])
obj["count"] += grpCount
probabilityGroups[idx2] = obj
predictionArray = [targetToDisplayInTable] * len(rulesArray)
freqArray = self.total_predictions[target]
chartDict[target] = sum(freqArray)
success = self.successful_predictions[target]
success_percent = self.success_percent[target]
richRulesArray = []
crudeRuleArray = []
analysisType = self._dataframe_context.get_analysis_type()
targetCol = self._dataframe_context.get_result_column()
binFlag = False
if self._dataframe_context.get_custom_analysis_details() != None:
binnedColObj = [
x["colName"] for x in
self._dataframe_context.get_custom_analysis_details()
]
if binnedColObj != None and targetCol in binnedColObj:
binFlag = True
for idx2, crudeRule in enumerate(rulesArray):
richRule, crudeRule = NarrativesUtils.generate_rules(
self._colname,
target,
crudeRule,
freqArray[idx2],
success[idx2],
success_percent[idx2],
analysisType,
binFlag=binFlag)
richRulesArray.append(richRule)
crudeRuleArray.append(crudeRule)
probabilityArray = map(
lambda x: humanize.apnumber(x) + "%"
if x >= 10 else str(int(x)) + "%", probabilityArray)
# targetArray = zip(rulesArray,probabilityArray,predictionArray,freqArray,groupArray)
targetArray = zip(crudeRuleArray, probabilityArray,
predictionArray, freqArray, groupArray,
richRulesArray)
targetArray = [list(x) for x in targetArray]
tableArray += targetArray
donutChartMaxLevel = 10
if len(chartDict) > donutChartMaxLevel:
chartDict = NarrativesUtils.restructure_donut_chart_data(
chartDict, nLevels=donutChartMaxLevel)
chartData = NormalChartData([chartDict]).get_data()
chartJson = ChartJson(data=chartData)
chartJson.set_title(self._colname)
chartJson.set_chart_type("donut")
mainCardChart = C3ChartData(data=chartJson)
mainCardChart.set_width_percent(45)
# mainCardChart = {"dataType": "c3Chart","widthPercent":33 ,"data": {"data": [chartDict],"title":self._colname,"axes":{},"label_text":{},"legend":{},"yAxisNumberFormat": ".2s","types":None,"axisRotation":False, "chart_type": "donut"}}
dropdownDict = {
"dataType": "dropdown",
"label": "Showing prediction rules for",
"data": dropdownData
}
data_dict["probabilityGroups"] = probabilityGroups
maincardSummary = NarrativesUtils.get_template_output(self._base_dir,\
'decisiontreesummary.html',data_dict)
main_card = NormalCard()
main_card_data = []
main_card_narrative = NarrativesUtils.block_splitter(
maincardSummary, self._blockSplitter)
main_card_data += main_card_narrative
main_card_data.append(mainCardChart)
main_card_data.append(dropdownDict)
main_card_table = TableData()
main_card_table.set_table_data(tableArray)
main_card_table.set_table_type("popupDecisionTreeTable")
main_card_data.append(main_card_table)
main_card.set_card_data(main_card_data)
main_card.set_card_name("Predicting Key Drivers of {}".format(
self._colname))
self._decisionTreeNode.add_a_card(main_card)