def Predict(self):
self._scriptWeightDict = self._dataframe_context.get_ml_model_prediction_weight()
self._scriptStages = {
"initialization":{
"summary":"Initialized the Decision Tree Regression Scripts",
"weight":2
},
"predictionStart":{
"summary":"Decision Tree Regression Model Prediction Started",
"weight":2
},
"predictionFinished":{
"summary":"Decision Tree Regression Model Prediction Finished",
"weight":6
}
}
CommonUtils.create_update_and_save_progress_message(self._dataframe_context,self._scriptWeightDict,self._scriptStages,self._slug,"initialization","info",display=True,emptyBin=False,customMsg=None,weightKey="total")
SQLctx = SQLContext(sparkContext=self._spark.sparkContext, sparkSession=self._spark)
dataSanity = True
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":
score_data_path = self._dataframe_context.get_score_path()+"/data.csv"
trained_model_path = "file://" + self._dataframe_context.get_model_path()
trained_model_path += "/model"
pipeline_path = "/".join(trained_model_path.split("/")[:-1])+"/pipeline"
print "trained_model_path",trained_model_path
print "pipeline_path",pipeline_path
print "score_data_path",score_data_path
pipelineModel = MLUtils.load_pipeline(pipeline_path)
trained_model = MLUtils.load_dtree_regresssion_pyspark_model(trained_model_path)
df = self._data_frame
indexed = pipelineModel.transform(df)
transformed = trained_model.transform(indexed)
if result_column in transformed.columns:
transformed = transformed.withColumnRenamed(result_column,"originalLabel")
transformed = transformed.withColumnRenamed("prediction",result_column)
pandas_scored_df = transformed.select(list(set(self._data_frame.columns+[result_column]))).toPandas()
if score_data_path.startswith("file"):
score_data_path = score_data_path[7:]
pandas_scored_df.to_csv(score_data_path,header=True,index=False)
print "STARTING Measure 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"]
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
spark_scored_df = transformed.select(list(set(columns_to_keep+[result_column])))
elif self._mlEnv == "sklearn":
CommonUtils.create_update_and_save_progress_message(self._dataframe_context,self._scriptWeightDict,self._scriptStages,self._slug,"predictionStart","info",display=True,emptyBin=False,customMsg=None,weightKey="total")
score_data_path = self._dataframe_context.get_score_path()+"/data.csv"
trained_model_path = "file://" + self._dataframe_context.get_model_path()
trained_model_path += "/"+self._dataframe_context.get_model_for_scoring()+".pkl"
print "trained_model_path",trained_model_path
print "score_data_path",score_data_path
if trained_model_path.startswith("file"):
trained_model_path = trained_model_path[7:]
trained_model = joblib.load(trained_model_path)
model_columns = self._dataframe_context.get_model_features()
print "model_columns",model_columns
df = self._data_frame.toPandas()
# pandas_df = MLUtils.factorize_columns(df,[x for x in categorical_columns if x != result_column])
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)
scoreKpiArray = MLUtils.get_scored_data_summary(y_score)
kpiCard = NormalCard()
kpiCardData = [KpiData(data=x) for x in scoreKpiArray]
kpiCard.set_card_data(kpiCardData)
kpiCard.set_cente_alignment(True)
print CommonUtils.convert_python_object_to_json(kpiCard)
self._result_setter.set_kpi_card_regression_score(kpiCard)
pandas_df[result_column] = y_score
df[result_column] = y_score
df.to_csv(score_data_path,header=True,index=False)
CommonUtils.create_update_and_save_progress_message(self._dataframe_context,self._scriptWeightDict,self._scriptStages,self._slug,"predictionFinished","info",display=True,emptyBin=False,customMsg=None,weightKey="total")
print "STARTING Measure 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"]
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
pandas_scored_df = df[list(set(columns_to_keep+[result_column]))]
spark_scored_df = SQLctx.createDataFrame(pandas_scored_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)
print spark_scored_df.printSchema()
df_helper = DataFrameHelper(spark_scored_df, self._dataframe_context,self._metaParser)
df_helper.set_params()
df = df_helper.get_data_frame()
# self._dataframe_context.set_dont_send_message(True)
try:
fs = time.time()
descr_stats_obj = DescriptiveStatsScript(df, df_helper, self._dataframe_context, self._result_setter, self._spark,self._prediction_narrative,scriptWeight=self._scriptWeightDict,analysisName="Descriptive analysis")
descr_stats_obj.Run()
print "DescriptiveStats Analysis Done in ", time.time() - fs, " seconds."
except:
print "Frequency Analysis Failed "
# try:
# fs = time.time()
# df_helper.fill_na_dimension_nulls()
# df = df_helper.get_data_frame()
# dt_reg = DecisionTreeRegressionScript(df, df_helper, self._dataframe_context, self._result_setter, self._spark,self._prediction_narrative,self._metaParser,scriptWeight=self._scriptWeightDict,analysisName="Predictive modeling")
# dt_reg.Run()
# print "DecisionTrees Analysis Done in ", time.time() - fs, " seconds."
# except:
# print "DTREE FAILED"
try:
fs = time.time()
two_way_obj = TwoWayAnovaScript(df, df_helper, self._dataframe_context, self._result_setter, self._spark,self._prediction_narrative,self._metaParser,scriptWeight=self._scriptWeightDict,analysisName="Measure vs. Dimension")
two_way_obj.Run()
print "OneWayAnova Analysis Done in ", time.time() - fs, " seconds."
except:
print "Anova Analysis Failed"
def Predict(self):
self._scriptWeightDict = self._dataframe_context.get_ml_model_prediction_weight(
)
self._scriptStages = {
"initialization": {
"summary": "Initialized The Neural Network (PyTorch) Scripts",
"weight": 2
},
"predictionStart": {
"summary": "Neural Network (PyTorch) Prediction Started",
"weight": 2
},
"predictionFinished": {
"summary": "Neural Network (PyTorch) Prediction Finished",
"weight": 6
}
}
CommonUtils.create_update_and_save_progress_message(
self._dataframe_context,
self._scriptWeightDict,
self._scriptStages,
self._slug,
"initialization",
"info",
display=True,
emptyBin=False,
customMsg=None,
weightKey="total")
SQLctx = SQLContext(sparkContext=self._spark.sparkContext,
sparkSession=self._spark)
dataSanity = True
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":
CommonUtils.create_update_and_save_progress_message(
self._dataframe_context,
self._scriptWeightDict,
self._scriptStages,
self._slug,
"predictionStart",
"info",
display=True,
emptyBin=False,
customMsg=None,
weightKey="total")
score_data_path = self._dataframe_context.get_score_path(
) + "/data.csv"
trained_model_path = "file://" + self._dataframe_context.get_model_path(
)
trained_model_path += "/" + self._dataframe_context.get_model_for_scoring(
) + ".pt"
print("trained_model_path", trained_model_path)
print("score_data_path", score_data_path)
if trained_model_path.startswith("file"):
trained_model_path = trained_model_path[7:]
#trained_model = joblib.load(trained_model_path)
trained_model = torch.load(trained_model_path,
map_location=torch.device('cpu'))
model_columns = self._dataframe_context.get_model_features()
print("model_columns", model_columns)
try:
df = self._data_frame.toPandas()
except:
df = self._data_frame
# pandas_df = MLUtils.factorize_columns(df,[x for x in categorical_columns if x != result_column])
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
]]
test_df = np.stack(
[pandas_df[col].values for col in pandas_df.columns], 1)
tensored_test_df = torch.tensor(test_df, dtype=torch.float)
outputs_test_df_tensored = trained_model(tensored_test_df.float())
y_score_mid = outputs_test_df_tensored.tolist()
y_score = [x[0] for x in y_score_mid]
scoreKpiArray = MLUtils.get_scored_data_summary(y_score)
kpiCard = NormalCard()
kpiCardData = [KpiData(data=x) for x in scoreKpiArray]
kpiCard.set_card_data(kpiCardData)
kpiCard.set_cente_alignment(True)
print(CommonUtils.convert_python_object_to_json(kpiCard))
self._result_setter.set_kpi_card_regression_score(kpiCard)
pandas_df[result_column] = y_score
df[result_column] = y_score
df.to_csv(score_data_path, header=True, index=False)
CommonUtils.create_update_and_save_progress_message(
self._dataframe_context,
self._scriptWeightDict,
self._scriptStages,
self._slug,
"predictionFinished",
"info",
display=True,
emptyBin=False,
customMsg=None,
weightKey="total")
print("STARTING Measure 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"]
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)
pandas_scored_df = df[list(set(columns_to_keep + [result_column]))]
spark_scored_df = SQLctx.createDataFrame(pandas_scored_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)
print(spark_scored_df.printSchema())
df_helper = DataFrameHelper(spark_scored_df, self._dataframe_context,
self._metaParser)
df_helper.set_params()
df = df_helper.get_data_frame()
# self._dataframe_context.set_dont_send_message(True)
try:
fs = time.time()
descr_stats_obj = DescriptiveStatsScript(
df,
df_helper,
self._dataframe_context,
self._result_setter,
self._spark,
self._prediction_narrative,
scriptWeight=self._scriptWeightDict,
analysisName="Descriptive analysis")
descr_stats_obj.Run()
print("DescriptiveStats Analysis Done in ",
time.time() - fs, " seconds.")
except:
print("Frequency Analysis Failed ")
# try:
# fs = time.time()
# df_helper.fill_na_dimension_nulls()
# df = df_helper.get_data_frame()
# dt_reg = DecisionTreeRegressionScript(df, df_helper, self._dataframe_context, self._result_setter, self._spark,self._prediction_narrative,self._metaParser,scriptWeight=self._scriptWeightDict,analysisName="Predictive modeling")
# dt_reg.Run()
# print "DecisionTrees Analysis Done in ", time.time() - fs, " seconds."
# except:
# print "DTREE FAILED"
try:
fs = time.time()
two_way_obj = TwoWayAnovaScript(
df,
df_helper,
self._dataframe_context,
self._result_setter,
self._spark,
self._prediction_narrative,
self._metaParser,
scriptWeight=self._scriptWeightDict,
analysisName="Measure vs. Dimension")
two_way_obj.Run()
print("OneWayAnova Analysis Done in ",
time.time() - fs, " seconds.")
except:
print("Anova Analysis Failed")
