def Run(self):
df_anova_obj = OneWayAnova(self._data_frame, self._dataframe_helper, self._dataframe_context).test_all(measure_columns=(self._dataframe_context.get_result_column(),))
df_anova_result = CommonUtils.as_dict(df_anova_obj)
#print 'RESULT: %s' % (json.dumps(df_anova_result, indent=2))
DataWriter.write_dict_as_json(self._spark, {'RESULT': json.dumps(df_anova_result)}, self._dataframe_context.get_result_file()+'OneWayAnova/')
#print 'Written Anova Result'
anova_narratives = CommonUtils.as_dict(AnovaNarratives(len(self._dataframe_helper.get_string_columns()), df_anova_obj, self._dataframe_helper))
#print "Narratives: %s" % (json.dumps(anova_narratives, indent=2))
DataWriter.write_dict_as_json(self._spark, anova_narratives, self._dataframe_context.get_narratives_file()+'OneWayAnova/')
def Run(self):
fs = time.time()
regression_result_obj = LinearRegression(self._data_frame, self._dataframe_helper, self._dataframe_context,self._metaParser,self._spark).fit(self._dataframe_context.get_result_column())
regression_result = CommonUtils.as_dict(regression_result_obj)
print "time taken for regression ",time.time()-fs,"seconds"
# print 'Regression result: %s' % (json.dumps(regression_result, indent=2))
# DataWriter.write_dict_as_json(self._spark, regression_result, self._dataframe_context.get_result_file()+'Regression/')
# regression_narratives_obj = LinearRegressionNarrative(len(self._dataframe_helper.get_numeric_columns()),regression_result_obj, self._correlations,self._dataframe_helper)
# regression_narratives = CommonUtils.as_dict(regression_narratives_obj)
regression_narratives_obj = RegressionNarrative(self._dataframe_helper,self._dataframe_context,self._result_setter,self._spark,regression_result_obj,self._correlations,self._story_narrative,self._metaParser)
regression_narratives = CommonUtils.as_dict(regression_narratives_obj.narratives)
def Run(self):
trend_narratives_obj = TimeSeriesNarrative(self._dataframe_helper,
self._dataframe_context,
self._result_setter,
self._spark,
self._story_narrative,
self._metaParser)
trend_narratives = CommonUtils.as_dict(trend_narratives_obj)
def Run(self):
correlations_obj = Correlation(
self._data_frame, self._dataframe_helper,
self._dataframe_context).correlations_for_one_column(
self._dataframe_context.get_result_column())
correlations_result = CommonUtils.as_dict(correlations_obj)
# print 'RESULT: %s' % (json.dumps(correlations_result, indent=2))
# DataWriter.write_dict_as_json(self._spark, correlations_result, self._dataframe_context.get_result_file()+'Correlation/')
return correlations_obj
def Run(self):
df_freq_dimension_obj = FreqDimensions(
self._data_frame, self._dataframe_helper,
self._dataframe_context).test_all(dimension_columns=(
self._dataframe_context.get_result_column(), ))
# df_freq_dimension_result = CommonUtils.as_dict(df_freq_dimension_obj)
# print 'RESULT: %s' % (json.dumps(df_freq_dimension_result, indent=2))
# DataWriter.write_dict_as_json(self._spark, df_freq_dimension_result, self._dataframe_context.get_result_file()+'FreqDimension/')
# Narratives
narratives_obj = DimensionColumnNarrative(
self._data_frame, self._dataframe_context.get_result_column(),
self._dataframe_helper, self._dataframe_context,
df_freq_dimension_obj, self._story_narrative, self._result_setter)
narratives = CommonUtils.as_dict(narratives_obj)
def Run(self):
df_chisquare_obj = ChiSquare(
self._data_frame, self._dataframe_helper, self._dataframe_context,
self._metaParser).test_all(dimension_columns=(
self._dataframe_context.get_result_column(), ))
# df_chisquare_result = CommonUtils.as_dict(df_chisquare_obj)
# print 'RESULT: %s' % (json.dumps(df_chisquare_result, indent=2))
# DataWriter.write_dict_as_json(self._spark, df_chisquare_result, self._dataframe_context.get_result_file()+'ChiSquare/')
# Narratives
# print self._data_frame.select('Sales').show()
if df_chisquare_obj.get_result() != {}:
chisquare_narratives = CommonUtils.as_dict(
ChiSquareNarratives(self._dataframe_helper, df_chisquare_obj,
self._spark, self._dataframe_context,
self._data_frame, self._story_narrative,
self._result_setter))
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 Run(self):
print("executive summary script called")
executive_summary_obj = ExecutiveSummaryNarrative(
self._dataframe_helper, self._dataframe_context,
self._result_setter, self._spark)
executive_summary = CommonUtils.as_dict(executive_summary_obj)
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 Predict(self):
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(
) + "/ScoredData/data.csv"
trained_model_path = self._dataframe_context.get_model_path()
if trained_model_path.endswith(".pkl"):
trained_model_path = "/".join(
trained_model_path.split("/")[:-1]) + "/model"
pipeline_path = "/".join(
trained_model_path.split("/")[:-1]) + "/pipeline"
score_summary_path = self._dataframe_context.get_score_path(
) + "/Summary/summary.json"
pipelineModel = MLUtils.load_pipeline(pipeline_path)
if self._classifier == "OneVsRest":
trained_model = MLUtils.load_one_vs_rest_model(trained_model_path)
elif self._classifier == "lr":
trained_model = MLUtils.load_logistic_model(trained_model_path)
df = self._data_frame
indexed = pipelineModel.transform(df)
transformed = trained_model.transform(indexed)
label_indexer_dict = MLUtils.read_string_indexer_mapping(
pipeline_path, SQLctx)
prediction_to_levels = udf(lambda x: label_indexer_dict[x],
StringType())
transformed = transformed.withColumn(
result_column, prediction_to_levels(transformed.prediction))
# udf_to_calculate_probability = udf(lambda x:max(x[0]))
# transformed = transformed.withColumn("predicted_probability",udf_to_calculate_probability(transformed.probability))
# print transformed.select("predicted_probability").show(5)
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 = 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()
if score_data_path.startswith("file"):
score_data_path = score_data_path[7:]
scored_dataframe.to_csv(score_data_path, header=True, index=False)
# print json.dumps({"scoreSummary":self._score_summary},indent=2)
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
if len(columns_to_keep) > 0:
columns_to_drop = list(set(df.columns) - set(columns_to_keep))
# spark_scored_df = transformed.select(categorical_columns+time_dimension_columns+numerical_columns+[result_column])
scored_df = transformed.select(categorical_columns +
time_dimension_columns +
numerical_columns + [result_column])
SQLctx = SQLContext(sparkContext=self._spark.sparkContext,
sparkSession=self._spark)
spark_scored_df = SQLctx.createDataFrame(scored_df.toPandas())
columns_to_drop = [
x for x in columns_to_drop if x in spark_scored_df.columns
]
modified_df = spark_scored_df.select(
[x for x in spark_scored_df.columns if x not in columns_to_drop])
df_helper = DataFrameHelper(modified_df, self._dataframe_context)
df_helper.set_params()
df = df_helper.get_data_frame()
try:
fs = time.time()
narratives_file = self._dataframe_context.get_score_path(
) + "/narratives/FreqDimension/data.json"
result_file = self._dataframe_context.get_score_path(
) + "/results/FreqDimension/data.json"
df_freq_dimension_obj = FreqDimensions(
spark_scored_df, df_helper, self._dataframe_context).test_all(
dimension_columns=[result_column])
df_freq_dimension_result = CommonUtils.as_dict(
df_freq_dimension_obj)
CommonUtils.write_to_file(result_file,
json.dumps(df_freq_dimension_result))
narratives_obj = DimensionColumnNarrative(result_column, df_helper,
self._dataframe_context,
df_freq_dimension_obj)
narratives = CommonUtils.as_dict(narratives_obj)
CommonUtils.write_to_file(narratives_file, json.dumps(narratives))
print "Frequency Analysis Done in ", time.time() - fs, " seconds."
except:
print "Frequency Analysis Failed "
try:
fs = time.time()
narratives_file = self._dataframe_context.get_score_path(
) + "/narratives/ChiSquare/data.json"
result_file = self._dataframe_context.get_score_path(
) + "/results/ChiSquare/data.json"
df_chisquare_obj = ChiSquare(df, df_helper,
self._dataframe_context).test_all(
dimension_columns=[result_column])
df_chisquare_result = CommonUtils.as_dict(df_chisquare_obj)
# print 'RESULT: %s' % (json.dumps(df_chisquare_result, indent=2))
CommonUtils.write_to_file(result_file,
json.dumps(df_chisquare_result))
chisquare_narratives = CommonUtils.as_dict(
ChiSquareNarratives(df_helper, df_chisquare_obj,
self._dataframe_context, df))
# print 'Narrarives: %s' %(json.dumps(chisquare_narratives, indent=2))
CommonUtils.write_to_file(narratives_file,
json.dumps(chisquare_narratives))
print "ChiSquare Analysis Done in ", time.time() - fs, " seconds."
except:
print "ChiSquare Analysis Failed "
def __init__(self,
column_name,
decision_tree_rules,
df_helper,
df_context,
meta_parser,
result_setter,
story_narrative=None,
analysisName=None,
scriptWeight=None):
self._story_narrative = story_narrative
self._metaParser = meta_parser
self._dataframe_context = df_context
self._ignoreMsg = self._dataframe_context.get_message_ignore()
self._result_setter = result_setter
self._blockSplitter = GLOBALSETTINGS.BLOCKSPLITTER
self._column_name = column_name.lower()
self._colname = column_name
self._capitalized_column_name = "%s%s" % (column_name[0].upper(),
column_name[1:])
self._decision_rules_dict = decision_tree_rules.get_decision_rules()
self._decision_tree_json = CommonUtils.as_dict(decision_tree_rules)
self._decision_tree_raw = self._decision_rules_dict
# self._decision_tree_raw = {"tree":{"children":None}}
# self._decision_tree_raw['tree']["children"] = self._decision_tree_json['tree']["children"]
self._table = decision_tree_rules.get_table()
self._new_table = {}
self.successful_predictions = decision_tree_rules.get_success()
self.total_predictions = decision_tree_rules.get_total()
self.success_percent = decision_tree_rules.get_success_percent()
self._important_vars = decision_tree_rules.get_significant_vars()
self._target_distribution = decision_tree_rules.get_target_contributions(
)
self._get_new_table()
self._df_helper = df_helper
self.subheader = None
#self.table = {}
self.dropdownComment = None
self.dropdownValues = None
self._base_dir = "/decisiontree/"
self._completionStatus = self._dataframe_context.get_completion_status(
)
if analysisName == None:
self._analysisName = self._dataframe_context.get_analysis_name()
else:
self._analysisName = analysisName
self._messageURL = self._dataframe_context.get_message_url()
if scriptWeight == None:
self._scriptWeightDict = self._dataframe_context.get_dimension_analysis_weight(
)
else:
self._scriptWeightDict = scriptWeight
self._scriptStages = {
"dtreeNarrativeStart": {
"summary": "Started the Decision Tree Narratives",
"weight": 0
},
"dtreeNarrativeEnd": {
"summary": "Narratives for Decision Tree Finished",
"weight": 10
},
}
self._completionStatus += self._scriptWeightDict[
self._analysisName]["narratives"] * self._scriptStages[
"dtreeNarrativeStart"]["weight"] / 10
progressMessage = CommonUtils.create_progress_message_object(self._analysisName,\
"dtreeNarrativeStart",\
"info",\
self._scriptStages["dtreeNarrativeStart"]["summary"],\
self._completionStatus,\
self._completionStatus)
CommonUtils.save_progress_message(self._messageURL,
progressMessage,
ignore=self._ignoreMsg)
self._dataframe_context.update_completion_status(
self._completionStatus)
self._decisionTreeNode = NarrativesTree()
self._decisionTreeNode.set_name("Prediction")
self._generate_narratives()
# self._story_narrative.add_a_node(self._decisionTreeNode)
self._result_setter.set_decision_tree_node(self._decisionTreeNode)
self._result_setter.set_score_dtree_cards(
json.loads(
CommonUtils.convert_python_object_to_json(
self._decisionTreeNode.get_all_cards())))
self._completionStatus = self._dataframe_context.get_completion_status(
)
self._completionStatus += self._scriptWeightDict[
self._analysisName]["narratives"] * self._scriptStages[
"dtreeNarrativeEnd"]["weight"] / 10
progressMessage = CommonUtils.create_progress_message_object(self._analysisName,\
"dtreeNarrativeEnd",\
"info",\
self._scriptStages["dtreeNarrativeEnd"]["summary"],\
self._completionStatus,\
self._completionStatus)
CommonUtils.save_progress_message(self._messageURL,
progressMessage,
ignore=self._ignoreMsg)
self._dataframe_context.update_completion_status(
self._completionStatus)
def Run(self):
binner = Binner(self._data_frame, self._dataframe_helper)
#histogram_data = CommonUtils.as_dict(binner.get_bins_for_all_measure_columns())
histogram_data = CommonUtils.as_dict(
binner.get_bins(self._dataframe_context.get_result_column()))
