PK>mlvajra/__init__.py"""A Last weapon to save Data scientist""" __version__='0.1.4.3' __name__='mlvajra' import mlvajra from mlvajra import preprocessing from mlvajra import data from mlvajra import evaluation from mlvajra import explanations from mlvajra import model from mlvajra import visualization from mlvajra import Annotation from mlvajra import activelearning from mlvajra import deploy from mlvajra import experiment __all__=['mlvajra','data','preprocessing','evaluation','explanations','model', 'visualization','Annotation','experiment','activelearning'] PK2 Nk[IAAmlvajra/__version__.py"""A Last weapon to save Data scientist""" __version__='0.1.2' PKҴNmlvajra/Annotation/__init__.pyPK No&D11mlvajra/Annotation/textclf.py# -*- coding: utf-8 -*- """ Created on Thu Jun 14 12:04:33 2018 @author: gurunath.lv """ try : import base64 import datetime import io import dash from dash.dependencies import Input, Output import dash_core_components as dcc import dash_html_components as html import dash_table_experiments as dt import plotly.graph_objs as go import numpy as np import pandas as pd import json # from tf_universal_sent_emb import get_similar_records # from spacy_text_classifier_cnn import train_cnn_for_given_label,predict import glob import os # from custom_classifier import customKNN,ParagraphVectors # from dashboard import Dashboard from flask import Flask import flask #import glob from sklearn.pipeline import Pipeline import pickle from lime.lime_text import LimeTextExplainer from sklearn.metrics.pairwise import cosine_similarity from sklearn.feature_extraction.text import TfidfVectorizer except ImportError as e: print("some packages are not installed -to use this textclf Annotator \ ! please install relevant libraries",e) server = Flask(__name__) app=dash.Dash(name = __name__, server = server) if not os.path.exists('tmp'): os.mkdir('tmp') DIRECTORY_PATH=r'tmp\\' #app = dash.Dash() app.scripts.config.serve_locally = True app.config['suppress_callback_exceptions']=True # custom_dush=Dashboard() #prodapt=html.Div(html.Img(src='http://www.prodapt.com/wp-content/uploads/logo_prodapt.png')), #vs=html.H1('vs') #reinfer=html.Div(html.Img(src='https://d1qb2nb5cznatu.cloudfront.net/startups/i/703763-82fa920eed7d56e7cdcee1b1d9a30b14-medium_jpg.jpg?buster=1440002957')), #logo=custom_dush.three_columns_grid(prodapt,vs,reinfer) def transform_using_tfidf(text_series): tfidf=TfidfVectorizer(stop_words='english') array=tfidf.fit_transform(text_series.tolist()).toarray() return array,tfidf def similarity_measure(inp_sent,array,tfidf,top_n): inp_vec=tfidf.transform([inp_sent]).toarray() cs=cosine_similarity(inp_vec,array) top_match_index=np.flip(np.argsort(cs,axis=1)[:,-top_n:],axis=1) return top_match_index def get_similar_records(inp_sent,total_text,top_n=10): array,tfidf=transform_using_tfidf(total_text) top_match_index=similarity_measure(inp_sent,array,tfidf,top_n) return total_text.iloc[top_match_index.ravel()] app.layout = html.Div([ # logo, dcc.Upload( id='upload-data', children=html.Div([ 'Drag and Drop or ', html.A('Select Files') ]), style={ 'width': '100%', 'height': '60px', 'lineHeight': '60px', 'borderWidth': '1px', 'borderStyle': 'dashed', 'borderRadius': '5px', 'textAlign': 'center', 'margin': '10px' }, # Allow multiple files to be uploaded multiple=True ), html.Div(id='output-data-upload'), html.Div(dt.DataTable(rows=[{}]), style={'display': 'none'}), dcc.Input(id='user-input-for-similarity', value='Enter the sentence', type='text', style={'width': '49%','align':'center'}), html.Div(id='similar-docs'), html.Br(), html.H3('Training dataset'), html.Div(id='output'), html.Button('Train',id='train-button'), html.Br(), dcc.Input(id='user-input-for-prediction', value='Enter the sentence to predict', type='text', style={'width': '49%','align':'center'}), html.H1(id='train-output'), # html.Button('Del data',id='delete-button'), html.H1(id='del-output'), dcc.Graph(id='predict-output'), html.Br(), dcc.Link('Why ML made this Prediction !!!!', href='/explain'), # html.Div([ # html.Pre(id='output', className='two columns'), # html.Div( # dcc.Graph( # id='graph', # style={ # 'overflow-x': 'wordwrap' # } # ), # className='ten columns' # ) # ], className='row') ]) def parse_contents(contents, file_name, date): content_type, content_string = contents.split(',') decoded = base64.b64decode(content_string) global df global filename try: if 'csv' in file_name: # Assume that the user uploaded a CSV file df = pd.read_csv( io.StringIO(decoded.decode('utf8'))) elif 'xls' in file_name: # Assume that the user uploaded an excel file df = pd.read_excel(io.BytesIO(decoded)) except Exception as e: print(e) return html.Div([ 'There was an error processing this file.' ]) """ pass similar contents df not file uploaded df To be filled """ # df.to_csv(r'{}'.format(filename),index=False) filename=file_name return html.Div([ html.H5(filename), html.H6(datetime.datetime.fromtimestamp(date)), # Use the DataTable prototype component: # github.com/plotly/dash-table-experiments dt.DataTable(rows=df.to_dict('records'),id='edit-table'), html.Hr(), # horizontal line # For debugging, display the raw contents provided by the web browser # html.Div('Raw Content'), # html.Pre(contents[0:200] + '...', style={ # 'whiteSpace': 'pre-wrap', # 'wordBreak': 'break-all' # }) ]) @app.callback( Output(component_id='similar-docs', component_property='children'), [Input(component_id='user-input-for-similarity', component_property='value')]) def get_similar_docs(sent): print('similar docs called ',sent) # path=glob.glob(r'*.csv') # df=pd.read_csv(path[0],encoding='ISO-8859-1') global similar_df similar_series=get_similar_records(sent,df[df.columns[0]]) similar_df=pd.DataFrame(columns=['Similar_sentences','labels']) similar_df['Similar_sentences']=similar_series print('check',similar_df.head()) # similar_df.to_csv return html.Div(dt.DataTable(rows=similar_df.to_dict('records'),id='edit-table-similar'),) def train_custom_classifier(similar_df,filename): texts, labels =similar_df.iloc[:,0].values,similar_df.iloc[:,1].values print(type(labels),type(texts),labels) if glob.glob(r'{}{}_knn_centroid.pkl'.format(DIRECTORY_PATH,filename)): dict_=pickle.load(open(glob.glob(r'{}*{}_knn_centroid.pkl'.format(DIRECTORY_PATH,filename))[0],'rb')) pipe=Pipeline(steps=[('pv',ParagraphVectors(filename=filename)),('knn',customKNN(label_to_vect_dict=dict_))]) label_encoding=pickle.load(open(glob.glob(r'{}*{}_label_encoding.pkl'.format(DIRECTORY_PATH,filename))[0],'rb')) for idx,lab in enumerate(set(labels)): label_encoding[idx+len(label_encoding)]=lab else: label_encoding=dict() pipe=Pipeline(steps=[('pv',ParagraphVectors(filename=filename)),('knn',customKNN())]) for idx,lab in enumerate(set(labels)): label_encoding[idx]=lab look_up=dict() for k,v in label_encoding.items(): look_up[v]=k pipe.fit(texts,pd.Series(labels).map(look_up)) dict_=pipe.named_steps.knn.get_centroid() pickle.dump(dict_,open(r'{}{}_knn_centroid.pkl'.format(DIRECTORY_PATH,filename),'wb')) pickle.dump(label_encoding,open(r'{}{}_label_encoding.pkl'.format(DIRECTORY_PATH,filename),'wb')) #train_custom_classifier(user_story,filename) def explain_prediction(sent,pipe,filename): # vect=transform_inp_sent_to_vect(sent) label_encoding=pickle.load(open(glob.glob(r'{}{}_label_encoding.pkl'.format(DIRECTORY_PATH,filename))[0],'rb')) labels=list(label_encoding.values()) explainer = LimeTextExplainer(class_names=labels) exp = explainer.explain_instance(sent, pipe.predict_proba,labels=labels) return exp.save_to_file(r'{}explanation.html'.format(DIRECTORY_PATH)) def predict_custom_classifier(sent_list,filename): print('inside fn',filename) dict_=pickle.load(open(glob.glob(r'{}{}_knn_centroid.pkl'.format(DIRECTORY_PATH,filename))[0],'rb')) pipe=Pipeline(steps=[('pv',ParagraphVectors(filename=filename)),('knn',customKNN(label_to_vect_dict=dict_))]) label_encoding=pickle.load(open(glob.glob(r'{}{}_label_encoding.pkl'.format(DIRECTORY_PATH,filename))[0],'rb')) # pred_dict=label_encoding.copy() pred=[] for sent in sent_list: explain_prediction(sent,pipe,filename) pred.append(pipe.predict_proba(sent)) return pred,list(label_encoding.values()) @app.callback(Output('output-data-upload', 'children'), [Input('upload-data', 'contents'), Input('upload-data', 'filename'), Input('upload-data', 'last_modified')]) def update_output(list_of_contents, list_of_names, list_of_dates): if list_of_contents is not None: children = [ parse_contents(c, n, d) for c, n, d in zip(list_of_contents, list_of_names, list_of_dates)] return children def prediction_bar_chart(xaxis,labels): trace1 = go.Bar( x = xaxis, y = labels, text= labels, marker=dict( color='rgb(158,202,225)', line=dict( color='rgb(8,48,107)', width=1.5, ) ), orientation='h', ) layout = go.Layout( title='probabilities', xaxis=dict(title='probability',), # yaxis=dict(title='priority') ) fig=go.Figure(data=[trace1],layout=layout) return fig @app.callback( Output('train-output', 'children'), [Input('train-button', 'n_clicks')]) def call_spacy_model_training(n_clicks): # print('button clicks:',n_clicks) if n_clicks!=None: # path=glob.glob(r'train.csv') # dff=pd.read_csv(path[0],encoding='ISO-8859-1') # filename=path[0].split('.')[0].split('_')[0] # filename=path[0].split('\\')[-1].split('_train_data')[0] train_custom_classifier(similar_df,filename) return 'custom classifier trained' @app.server.route('/explain') def upload_file(): return flask.send_from_directory(DIRECTORY_PATH,'explanation.html') #@app.callback( # Output('del-output', 'children'), # [Input('delete-button', 'n_clicks')]) #def delete_data_tmp_folder(n): # print('delete:',n) # path=glob.glob(r'D:\Testing_frameworks\Testcase-Vmops\Insight\src\features\tmp\*.csv') # os.remove(path[0]) # return 'data deleted' @app.callback( Output('predict-output', 'figure'), [Input('user-input-for-prediction', component_property='value')]) def predict_cat(sent): print(sent) # path=glob.glob(r'D:\Testing_frameworks\Testcase-Vmops\Insight\src\features\tmp\*.csv') # filename=path[0].split('\\')[-1].split('.')[0] pred,labels=predict_custom_classifier([sent],filename) # print(dict_) print(pred,labels) return prediction_bar_chart(list(np.absolute(pred[0].ravel())),labels) #json.dumps(predict([sent],filename)[0],indent=2) def generate_table(dataframe, max_rows=10): return ( # Header [html.Tr([html.Th(col) for col in dataframe.columns])] + # Body [html.Tr([ html.Td(dataframe.iloc[i][col]) for col in dataframe.columns ]) for i in range(min(len(dataframe), max_rows))] ) @app.callback( Output('output', 'children'), [Input('edit-table-similar', 'rows')]) def update_selected_row_indices(rows): # path=glob.glob(r'D:\Testing_frameworks\Testcase-Vmops\Insight\src\features\tmp\*.csv') # filename=path[0].split('\\')[-1].split('.')[0] df=pd.DataFrame(rows) # print(df) global similar_df similar_df=df # df.to_csv(r'D:\Testing_frameworks\Testcase-Vmops\Insight\src\features\tmp\{}_train_data.csv'.format(filename),index=False) return generate_table(df) app.css.append_css({ 'external_url': 'https://codepen.io/chriddyp/pen/bWLwgP.css' }) app.css.append_css({"external_url": "https://codepen.io/chriddyp/pen/brPBPO.css"}) if __name__ == '__main__': app.run_server(debug=True,port =8050)PK NDjj mlvajra/Annotation/timeseries.pytry : import dash from dash.dependencies import Output, Event,Input import dash_core_components as dcc import dash_html_components as html import plotly from plotly import tools import random import plotly.graph_objs as go from collections import deque import json import pandas as pd import csv #from datetime import datetime import numpy as np from datetime import timedelta from datetime import datetime as dt except ImportError as e: print("some packages are not installed ",e) #class DataGenerator: # def __init__(self, df,cont_names=['BerPreFecMax','PhaseCorrectionAve','PmdMin','Qmin','SoPmdAve']): # self.df = df # self.cont_names=cont_names # # def __iter__(self): # self.n=0 # return self # # def __next__(self): # if self.n > self.df.shape[0]: # raise StopIteration # # # return self.df.loc[self.n,'@timestamp'],self.df.loc[self.n,self.cont_names].tolist() #def DataGenerator(meric_list): # for i in data[['@timestamp']+meric_list].iterrows(): # yield i[1] # ##raw_data=pd.read_feather(r'D:\windstream_official\Anomaly_detection\data\processed\raw_df_feather') # #get_data1=((x,y) for x,y in zip(data['@timestamp'],data['BerPreFecMax'])) #get_data2=((x,y) for x,y in zip(data['@timestamp'],data['PhaseCorrectionAve'])) #get_data3=((x,y) for x,y in zip(data['@timestamp'],data['PmdMin'])) #get_data4=((x,y) for x,y in zip(data['@timestamp'],data['Qmin'])) #get_data5=((x,y) for x,y in zip(data['@timestamp'],data['SoPmdAve'])) # # #get_data1=iter(get_data1) #get_data2=iter(get_data2) #get_data3=iter(get_data3) #get_data4=iter(get_data4) #get_data5=iter(get_data5) ochctp=pd.read_csv(r'D:\windstream_official\h_OCHCTP.csv') metric=['BerPreFecMax','PhaseCorrectionAve','PmdMin','Qmin','SoPmdAve'] ochctp.columns=['@timestamp','device']+metric ochctp['@timestamp']=pd.to_datetime(ochctp['@timestamp']) ochctp=ochctp.sort_values(by='@timestamp') ochctp.set_index(ochctp['@timestamp'],inplace=True) ochctp['date']=(ochctp['@timestamp'].dt.date).astype('str') device_list=ochctp['device'].unique() dropoptions=[{'value':dev,'label':dev} for dev in device_list ] global df df=pd.DataFrame(columns=metric) external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] stop_n_click=0 app = dash.Dash(__name__) app.config['suppress_callback_exceptions']=True app.css.config.serve_locally = True app.scripts.config.serve_locally = True app.layout = html.Div([ html.Div(html.H2('Time series Annotator Tool')), html.Div([html.Div(dcc.DatePickerRange(id='date-picker-range',start_date=ochctp.index[-10].date(),end_date=ochctp.index[-1].date())), html.Div(id='date-picker-output'),]), html.Div(dcc.Dropdown(id='device-list',options=dropoptions,className='two column',value='11-L1-9'),className='row'), html.Button('Show',id='Show-button'), dcc.Dropdown(id='anomly-or-not', options=[ {'label': 'Anomaly', 'value': 'anomaly'}, {'label': 'Not Anomaly', 'value': 'not_anomaly'}, ], value='not_anomaly' ), html.Div(id='m1-div',children=dcc.Graph(id='m1',)), html.Div(id='m2-div',children=dcc.Graph(id='m2',)), html.Div(id='m3-div',children=dcc.Graph(id='m3',)), html.Div(id='m4-div',children=dcc.Graph(id='m4',)), html.Div(id='m5-div',children=dcc.Graph(id='m5',)), html.Div([html.Div([dcc.Graph(id='m1', animate=True, style={"position": "relative", "width": "800px", "height": "800px"},className='five column'), dcc.Graph(id='m2', animate=True, style={"position": "relative", "width": "800px", "height": "800px"},className='five column')],className='row'), html.Div([dcc.Graph(id='m3', animate=True, style={"position": "relative", "width": "800px", "height": "800px"},className='five column'), dcc.Graph(id='m4', animate=True, style={"position": "relative", "width": "800px", "height": "800px"},className='five column')],className='row'), dcc.Graph(id='m5', animate=True, style={"position": "relative", "width": "800px", "height": "800px"},className='five column'),],className='column'), ## dcc.Interval( # id='graph-update1', # interval=1*1000 # ), # dcc.Interval( # id='graph-update2', # interval=1*1000 # ), # dcc.Interval( # id='graph-update3', # interval=1*1000 # ), # dcc.Interval( # id='graph-update4', # interval=1*1000 # ), # dcc.Interval( # id='graph-update', # interval=1*1000 # ), # html.Button('Stop', id='Stop-button'), ] ) #def line_plot(X,Y,name=None,anom=None): # trace = plotly.graph_objs.Scatter( # x=list(X), # y=list(Y1), # name='Scatter', # mode= 'lines+markers' # ) # if anom: # return {'data': [trace,anom],} # # print('range',min(X)-timedelta(hours=15),max(X)+timedelta(hours=15)) # return {'data': [trace],'layout' : go.Layout(xaxis=dict(range=[min(X)-timedelta(hours=15),max(X)+timedelta(hours=15)]), # yaxis=dict(range=[range_dict[name]['min'],range_dict[name]['max']]),)} # ## {'data': [trace],'layout' : go.Layout(xaxis=dict(range=[min(X)-timedelta(hours=1),max(X)+timedelta(hours=1)]), ## yaxis=dict(range=[range_dict[name]['min'],range_dict[name]['max']])} #def get_graphs(X1,Y1,Y2,Y3,Y4,Y5,anom): # trace1 = go.Scatter(x=X1, y=Y1) # trace2 = go.Scatter(x=X1, y=Y2) # trace3 = go.Scatter(x=X1, y=Y3) # trace4 = go.Scatter(x=X1, y=Y4) # trace4 = go.Scatter(x=X1, y=Y5) # # fig = tools.make_subplots(rows=3, cols=2, subplot_titles=('Plot 1', 'Plot 2', # 'Plot 3', 'Plot 4')) # fig.append_trace([trace1,anom], 1, 1) # fig.append_trace(trace2, 1, 2) # fig.append_trace(trace3, 2, 1) # fig.append_trace(trace4, 2, 2) # fig.append_trace(trace4, 3, 1) # # fig['layout']['xaxis1'].update(title='xaxis 1 title') # fig['layout']['xaxis2'].update(title='xaxis 2 title', ) # fig['layout']['xaxis3'].update(title='xaxis 3 title', showgrid=False) # fig['layout']['xaxis4'].update(title='xaxis 4 title', ) # # fig['layout']['yaxis1'].update(title='yaxis 1 title') # fig['layout']['yaxis2'].update(title='yaxis 2 title', ) # fig['layout']['yaxis3'].update(title='yaxis 3 title', showgrid=False) # fig['layout']['yaxis4'].update(title='yaxis 4 title') # # fig['layout'].update(title='Customizing Subplot Axes') # return fig #fields=['Type','X','Y','Anom_point'] #with open(r'anomaly1.csv', 'a') as f: # writer = csv.writer(f) # writer.writerow(fields) ##@app.callback( ## Output('selected-data', 'children'), ## [Input('basic-interactions', 'selectedData')]) ##def display_selected_data(selectedData): ## return json.dumps(selectedData, indent=2) #def save_data(anom,X1,Y1,x): # with open(r'anomaly1.csv', 'a') as f: # writer = csv.writer(f) # # writer.writerow([f"{anom,X1,Y1,x}"]) # #q_to_list=lambda x:list(x) # #d1,d2='2018-02-07' ,'2018-02-16' # #start_date = dt.strptime(d1, '%Y-%m-%d') #end_date = dt.strptime(d1, '%Y-%m-%d') #start_date_string = start_date.strftime('%B %d, %Y') @app.callback(Output('date-picker-output', 'children'), [Input('date-picker-range', 'start_date'), Input('date-picker-range', 'end_date')]) def update_df(start_date,end_date): global df df=ochctp[(ochctp['date']>start_date) & (ochctp['date']<=end_date)] # df=ochctp[ochctp["date"].isin(pd.date_range(start_date,end_date))] print('updated',df.shape) return html.H2('Date Range from {} to {}'.format(start_date,end_date)) @app.callback(Output('m1-div', 'children'), [Input('m1','clickData'), Input('anomly-or-not','value'), Input('device-list','value'), Input('Show-button', 'n_clicks'), ]) def update_plot1(clickData,anom_or_not,device,n_clicks,): if n_clicks!=None: # print('clicks',n_clicks) # print(ochctp.shape) # if True: # if (1+n_clicks)%2==0: # print('exec') global df # print(start_date,end_date) # print(pd.date_range(start_date,end_date)) # df=ochctp[(ochctp['date']>start_date) & (ochctp['date']<=end_date)] # df=ochctp[ochctp["date"].isin(pd.date_range(start_date,end_date))] print(df.shape) print('clickdata',clickData) df=df[df['device']==device] print(df.shape) # print(start_date,end_date) metric_series=df[metric[0]] if clickData!=None: # print(json.dumps(clickData, indent=2),anom) Xa=[i['x'] for i in clickData['points']] Ya=[i['y'] for i in clickData['points']] anom=plotly.graph_objs.Scatter( x=Xa, y=Ya, name='Anomaly', mode='markers', marker={'size': 12} ) trace = plotly.graph_objs.Scatter( x=metric_series.index, y=metric_series.values, name=name1, mode= 'lines+markers' ) return dcc.Graph(id='m1',figure={'data': [trace,anom],'layout' : go.Layout(title=name1,xaxis=dict(range=[min(metric_series.index)-timedelta(hours=5), max(metric_series.index)+timedelta(hours=5)]), yaxis=dict(range=[min(metric_series.values),max(metric_series.values)]))}) else : trace = plotly.graph_objs.Scatter( x=metric_series.index, y=metric_series.values, name=name2, mode= 'lines+markers' ) # print('guru') # # if anom: # # return {'data': [trace,anom],} # # name='SoPmdAve' # print('range',min(X5)-timedelta(hours=15),max(X)+timedelta(hours=15)) # print('yrange',min(Y5),max(Y5),min(list(Y5)[-10:]),max(list(Y5)[-10:])) # print('len',len(Y5)) return dcc.Graph(id='m1',figure={'data': [trace],'layout' : go.Layout(title=name1,xaxis=dict(range=[min(metric_series.index)-timedelta(hours=5), max(metric_series.index)+timedelta(hours=5)]), yaxis=dict(range=[min(metric_series.values),max(metric_series.values)]))}) #@app.callback(Output('m1-div', 'children'), # [Input('m1','clickData'), # Input('anomly-or-not','value'), # Input('device-list','value'), # Input('date-picker-range', 'start_date'), # Input('date-picker-range', 'end_date')]) # #def update_plot1(clickData,anom_or_not,device,start_date,end_date): # df=ochctp[(ochctp['date'] > start_date) & (ochctp['date'] <= end_date)] # ## df=ochctp[ochctp["date"].isin(pd.date_range(start_date,end_date))] # print('clickdata',clickData) # df=df[df['device']==device] # print(df.shape) # print(start_date,end_date) # metric_series=df[metric[0]] # if clickData!=None: # # print(json.dumps(clickData, indent=2),anom) # Xa=[i['x'] for i in clickData['points']] # Ya=[i['y'] for i in clickData['points']] # anom=plotly.graph_objs.Scatter( # x=Xa, # y=Ya, # name='Anomaly', # mode='markers', # marker={'size': 12} # ) # trace = plotly.graph_objs.Scatter( # x=metric_series.index, # y=metric_series.values, # name=name1, # mode= 'lines+markers' # ) # # return dcc.Graph(id='m1',figure={'data': [trace,anom],'layout' : go.Layout(title=name1,xaxis=dict(range=[min(metric_series.index)-timedelta(hours=5), # max(metric_series.index)+timedelta(hours=5)]), # yaxis=dict(range=[min(metric_series.values),max(metric_series.values)]))}) # else : # trace = plotly.graph_objs.Scatter( # x=metric_series.index, # y=metric_series.values, # name=name2, # mode= 'lines+markers' # ) ## print('guru') ## # if anom: ## # return {'data': [trace,anom],} ## # name='SoPmdAve' ## print('range',min(X5)-timedelta(hours=15),max(X)+timedelta(hours=15)) ## print('yrange',min(Y5),max(Y5),min(list(Y5)[-10:]),max(list(Y5)[-10:])) ## print('len',len(Y5)) # return dcc.Graph(id='m1',figure={'data': [trace],'layout' : go.Layout(title=name1,xaxis=dict(range=[min(metric_series.index)-timedelta(hours=5), # max(metric_series.index)+timedelta(hours=5)]), # yaxis=dict(range=[min(metric_series.values),max(metric_series.values)]))}) # @app.callback(Output('m2-div', 'children'), [Input('m2','clickData'), Input('anomly-or-not','value'), Input('device-list','value'), Input('Show-button', 'n_clicks')]) def update_plot2(clickData,anom_or_not,device,n_clicks): if n_clicks!=None: global df df=df[df['device']==device] print(df.shape) # print(start_date,end_date) metric_series=df[metric[1]] if clickData!=None: # print(json.dumps(clickData, indent=2),anom) Xa=[i['x'] for i in clickData['points']] Ya=[i['y'] for i in clickData['points']] anom=plotly.graph_objs.Scatter( x=Xa, y=Ya, name='Anomaly', mode='markers', marker={'size': 12} ) trace = plotly.graph_objs.Scatter( x=metric_series.index, y=metric_series.values, name=name1, mode= 'lines+markers' ) return dcc.Graph(id='m2',figure={'data': [trace,anom],'layout' : go.Layout(title=name1,xaxis=dict(range=[min(metric_series.index)-timedelta(hours=5), max(metric_series.index)+timedelta(hours=5)]), yaxis=dict(range=[min(metric_series.values),max(metric_series.values)]))}) else : trace = plotly.graph_objs.Scatter( x=metric_series.index, y=metric_series.values, name=name2, mode= 'lines+markers' ) # print('guru') # # if anom: # # return {'data': [trace,anom],} # # name='SoPmdAve' # print('range',min(X5)-timedelta(hours=15),max(X)+timedelta(hours=15)) # print('yrange',min(Y5),max(Y5),min(list(Y5)[-10:]),max(list(Y5)[-10:])) # print('len',len(Y5)) return dcc.Graph(id='m2',figure={'data': [trace],'layout' : go.Layout(title=name1,xaxis=dict(range=[min(metric_series.index)-timedelta(hours=5), max(metric_series.index)+timedelta(hours=5)]), yaxis=dict(range=[min(metric_series.values),max(metric_series.values)]))}) @app.callback(Output('m3-div', 'children'), [Input('m3','clickData'), Input('anomly-or-not','value'), Input('device-list','value'), Input('Show-button', 'n_clicks')]) def update_plot3(clickData,anom_or_not,device,n_clicks): if n_clicks!=None: # if (1+n_clicks)%2==0: # df=ochctp[(ochctp['date'] > start_date) & (ochctp['date'] <= end_date)] global df # df=ochctp[ochctp["date"].isin(pd.date_range(start_date,end_date))] print('clickdata',clickData) df=df[df['device']==device] print(df.shape) # print(start_date,end_date) metric_series=df[metric[2]] if clickData!=None: # print(json.dumps(clickData, indent=2),anom) Xa=[i['x'] for i in clickData['points']] Ya=[i['y'] for i in clickData['points']] anom=plotly.graph_objs.Scatter( x=Xa, y=Ya, name='Anomaly', mode='markers', marker={'size': 12} ) trace = plotly.graph_objs.Scatter( x=metric_series.index, y=metric_series.values, name=name1, mode= 'lines+markers' ) return dcc.Graph(id='m3',figure={'data': [trace,anom],'layout' : go.Layout(title=name1,xaxis=dict(range=[min(metric_series.index)-timedelta(hours=5), max(metric_series.index)+timedelta(hours=5)]), yaxis=dict(range=[min(metric_series.values),max(metric_series.values)]))}) else : trace = plotly.graph_objs.Scatter( x=metric_series.index, y=metric_series.values, name=name2, mode= 'lines+markers' ) # print('guru') # # if anom: # # return {'data': [trace,anom],} # # name='SoPmdAve' # print('range',min(X5)-timedelta(hours=15),max(X)+timedelta(hours=15)) # print('yrange',min(Y5),max(Y5),min(list(Y5)[-10:]),max(list(Y5)[-10:])) # print('len',len(Y5)) return dcc.Graph(id='m3',figure={'data': [trace],'layout' : go.Layout(title=name1,xaxis=dict(range=[min(metric_series.index)-timedelta(hours=5), max(metric_series.index)+timedelta(hours=5)]), yaxis=dict(range=[min(metric_series.values),max(metric_series.values)]))}) @app.callback(Output('m4-div', 'children'), [Input('m4','clickData'), Input('anomly-or-not','value'), Input('device-list','value'), Input('Show-button', 'n_clicks')]) def update_plot4(clickData,anom_or_not,device,n_clicks): if n_clicks!=None: # if (1+n_clicks)%2==0: # df=ochctp[(ochctp['date'] > start_date) & (ochctp['date'] <= end_date)] global df # df=ochctp[ochctp["date"].isin(pd.date_range(start_date,end_date))] print('clickdata',clickData) df=df[df['device']==device] print(df.shape) # print(start_date,end_date) metric_series=df[metric[3]] if clickData!=None: # print(json.dumps(clickData, indent=2),anom) Xa=[i['x'] for i in clickData['points']] Ya=[i['y'] for i in clickData['points']] anom=plotly.graph_objs.Scatter( x=Xa, y=Ya, name='Anomaly', mode='markers', marker={'size': 12} ) trace = plotly.graph_objs.Scatter( x=metric_series.index, y=metric_series.values, name=name1, mode= 'lines+markers' ) return dcc.Graph(id='m4',figure={'data': [trace,anom],'layout' : go.Layout(title=name1,xaxis=dict(range=[min(metric_series.index)-timedelta(hours=5), max(metric_series.index)+timedelta(hours=5)]), yaxis=dict(range=[min(metric_series.values),max(metric_series.values)]))}) else : trace = plotly.graph_objs.Scatter( x=metric_series.index, y=metric_series.values, name=name2, mode= 'lines+markers' ) # print('guru') # # if anom: # # return {'data': [trace,anom],} # # name='SoPmdAve' # print('range',min(X5)-timedelta(hours=15),max(X)+timedelta(hours=15)) # print('yrange',min(Y5),max(Y5),min(list(Y5)[-10:]),max(list(Y5)[-10:])) # print('len',len(Y5)) return dcc.Graph(id='m4',figure={'data': [trace],'layout' : go.Layout(title=name1,xaxis=dict(range=[min(metric_series.index)-timedelta(hours=5), max(metric_series.index)+timedelta(hours=5)]), yaxis=dict(range=[min(metric_series.values),max(metric_series.values)]))}) @app.callback(Output('m5-div', 'children'), [Input('m5','clickData'), Input('anomly-or-not','value'), Input('device-list','value'), Input('Show-button', 'n_clicks')]) def update_plot5(clickData,anom_or_not,device,n_clicks): if n_clicks!=None: # if (1+n_clicks)%2==0: # df=ochctp[(ochctp['date'] > start_date) & (ochctp['date'] <= end_date)] global df # df=ochctp[ochctp["date"].isin(pd.date_range(start_date,end_date))] print('clickdata',clickData) df=df[df['device']==device] print(df.shape) # print(start_date,end_date) metric_series=df[metric[4]] if clickData!=None: # print(json.dumps(clickData, indent=2),anom) Xa=[i['x'] for i in clickData['points']] Ya=[i['y'] for i in clickData['points']] anom=plotly.graph_objs.Scatter( x=Xa, y=Ya, name='Anomaly', mode='markers', marker={'size': 12} ) trace = plotly.graph_objs.Scatter( x=metric_series.index, y=metric_series.values, name=name1, mode= 'lines+markers' ) return dcc.Graph(id='m5',figure={'data': [trace,anom],'layout' : go.Layout(title=name1,xaxis=dict(range=[min(metric_series.index)-timedelta(hours=5), max(metric_series.index)+timedelta(hours=5)]), yaxis=dict(range=[min(metric_series.values),max(metric_series.values)]))}) else : trace = plotly.graph_objs.Scatter( x=metric_series.index, y=metric_series.values, name=name2, mode= 'lines+markers' ) # print('guru') # # if anom: # # return {'data': [trace,anom],} # # name='SoPmdAve' # print('range',min(X5)-timedelta(hours=15),max(X)+timedelta(hours=15)) # print('yrange',min(Y5),max(Y5),min(list(Y5)[-10:]),max(list(Y5)[-10:])) # print('len',len(Y5)) return dcc.Graph(id='m5',figure={'data': [trace],'layout' : go.Layout(title=name1,xaxis=dict(range=[min(metric_series.index)-timedelta(hours=5), max(metric_series.index)+timedelta(hours=5)]), yaxis=dict(range=[min(metric_series.values),max(metric_series.values)]))}) app.css.append_css({ 'external_url': ['https://codepen.io/chriddyp/pen/bWLwgP.css', 'https://cdnjs.cloudflare.com/ajax/libs/vis/4.20.1/vis.min.css',] }) if __name__ == '__main__': # data=pd.read_feather(r'D:\windstream_official\Anomaly_detection\data\processed\13L110') name1,name2,name3,name4,name5=metric app.run_server(debug=True) PKQN,  *mlvajra/Annotation/dashboard/app_layout.pytry: import dash from dash.dependencies import Input, Output import dash_core_components as dcc import dash_html_components as html import dash_table_experiments as dt import dash_table import plotly.graph_objs as go from flask import Flask import flask import os except ImportError as e: print("some packages are not installed consider installing the packages",e) server = Flask(__name__) app=dash.Dash(name = __name__, server = server) if not os.path.exists('tmp'): os.mkdir('tmp') DIRECTORY_PATH=r'tmp\\' app.scripts.config.serve_locally = True app.config['suppress_callback_exceptions']=True app.layout = html.Div([ # logo, dash_table.DataTable(), html.H2("Annotator"), html.H4("converting unsupervised data into supervised dataset using Annotaion (weekly supervsion)"), dcc.Tab(), dcc.Upload( id='upload-data', children=html.Div([ 'Drag and Drop or ', html.A('Select Files') ]), style={ 'width': '100%', 'height': '60px', 'lineHeight': '60px', 'borderWidth': '1px', 'borderStyle': 'dashed', 'borderRadius': '5px', 'textAlign': 'center', 'margin': '10px' }, # Allow multiple files to be uploaded multiple=True ), html.Div(id='output-data-upload'), html.Div(dt.DataTable(rows=[{}]), style={'display': 'none'}), dcc.Input(id='user-input-for-similarity', value='Enter the sentence', type='text', style={'width': '49%','align':'center'}), html.Div(id='similar-docs'), html.Br(), html.Button('Save Dataset',id='save-dataset',), #html.H3('Training dataset'), html.Div(id='output'), # html.Button('Train',id='train-button'), # html.Br(), # dcc.Input(id='user-input-for-prediction', # value='Enter the sentence to predict', type='text', # style={'width': '49%','align':'center'}), # html.H1(id='train-output'), # # html.Button('Del data',id='delete-button'), # html.H1(id='del-output'), # dcc.Graph(id='predict-output'), # html.Br(), # dcc.Link('Why ML made this Prediction !!!!', href='/explain'), # html.Div([ # html.Pre(id='output', className='two columns'), # html.Div( # dcc.Graph( # id='graph', # style={ # 'overflow-x': 'wordwrap' # } # ), # className='ten columns' # ) # ], className='row') ]) PKfN*2k 'mlvajra/Annotation/dashboard/backend.pyfrom app_layout import app from app_layout import * try: import base64 import datetime import io import pandas as pd from sklearn.pipeline import Pipeline import pickle from lime.lime_text import LimeTextExplainer from sklearn.metrics.pairwise import cosine_similarity from sklearn.feature_extraction.text import TfidfVectorizer import numpy as np except ImportError as e: print("some packages are not installed consider installing the packages",e) def transform_using_tfidf(text_series): tfidf=TfidfVectorizer(stop_words='english') array=tfidf.fit_transform(text_series.tolist()).toarray() return array,tfidf def similarity_measure(inp_sent,array,tfidf,top_n): inp_vec=tfidf.transform([inp_sent]).toarray() cs=cosine_similarity(inp_vec,array) top_match_index=np.flip(np.argsort(cs,axis=1)[:,-top_n:],axis=1) return top_match_index def get_similar_records(inp_sent,total_text,top_n=10): array,tfidf=transform_using_tfidf(total_text) top_match_index=similarity_measure(inp_sent,array,tfidf,top_n) return total_text.iloc[top_match_index.ravel()] def get_simialar_df(sent,df): global similar_df similar_series=get_similar_records(sent,df[df.columns[0]]) similar_df=pd.DataFrame(columns=['Similar_sentences','labels']) similar_df['Similar_sentences']=similar_series print('check',similar_df.head()) return similar_df def return_datable(df): #print(df.head()) #dt.DataTable table=dash_table.DataTable( id='table', columns=[{"name": i, "id": i} for i in df.columns], data=df.to_dict("rows"), ) print(table) return table def parse_contents(contents, file_name, date): content_type, content_string = contents.split(',') decoded = base64.b64decode(content_string) global df global filename try: if 'csv' in file_name: # Assume that the user uploaded a CSV file df = pd.read_csv( io.StringIO(decoded.decode('utf8'))) elif 'xls' in file_name: # Assume that the user uploaded an excel file df = pd.read_excel(io.BytesIO(decoded)) except Exception as e: print(e) return html.Div([ 'There was an error processing this file.' ]) """ pass similar contents df not file uploaded df To be filled """ # df.to_csv(r'{}'.format(filename),index=False) filename=file_name return df # html.Div([ # html.H5(filename), # html.H6(datetime.datetime.fromtimestamp(date)), # # Use the DataTable prototype component: # # github.com/plotly/dash-table-experiments # return_datable(df), # #dt.DataTable(rows=df.to_dict('records'),id='edit-table'), # html.Hr(), # horizontal line # # For debugging, display the raw contents provided by the web browser # # html.Div('Raw Content'), # # html.Pre(contents[0:200] + '...', style={ # # 'whiteSpace': 'pre-wrap', # # 'wordBreak': 'break-all' # # }) # ]) PKjNN`)mlvajra/Annotation/dashboard/callbacks.pyfrom app_layout import * from backend import * @app.callback(Output('output-data-upload', 'children'), [Input('upload-data', 'contents'), Input('upload-data', 'filename'), Input('upload-data', 'last_modified')]) def update_output(list_of_contents, list_of_names, list_of_dates): if list_of_contents is not None: children = [ parse_contents(c, n, d) for c, n, d in zip(list_of_contents, list_of_names, list_of_dates)] global df df=children[0] #print(df.head()) return return_datable(df) @app.callback( Output(component_id='similar-docs', component_property='children'), [Input(component_id='user-input-for-similarity', component_property='value')]) def get_similar_docs(sent): print('similar docs called ',sent) # path=glob.glob(r'*.csv') # df=pd.read_csv(path[0],encoding='ISO-8859-1') similar_df=get_simialar_df(sent,df) # similar_df.to_csv return html.Div(dt.DataTable(rows=similar_df.to_dict('records'),id='edit-table-similar'),) @app.callback( Output(component_id='output', component_property='children'), [Input(component_id='save-dataset', component_property='n_clicks')]) def save_dataset(n_clicks): filename="annotated.csv" if n_clicks!=None: similar_df.to_csv(DIRECTORY_PATH+'{}'.format(filename)) app.css.append_css({ 'external_url': 'https://codepen.io/chriddyp/pen/bWLwgP.css' }) app.css.append_css({"external_url": "https://codepen.io/chriddyp/pen/brPBPO.css"}) if __name__ == '__main__': app.run_server(debug=True,port =8050)PKxN qq3mlvajra/Annotation/dataPrograming/textAnnotation.pyx # https://arxiv.org/pdf/1605.07723.pdf #https://arxiv.org/pdf/1711.10160.pdf from snorkel import * PKNNM n1mlvajra/Annotation/dataPrograming/tsAnnotation.py""" TODO: Authoring Different Labeling functions to annotate timeseries data For example: 3-standarddeviation =============================================================== Comparing different Labeling functions /merging different Labeling functions to achieve better classification accuracy Example: helper functions to visualize Distribuitions of different generated Label functions =============================================================== Using Active learning to collect Data Labels (can be used for both Regression /classifications) """ import pandas as pd from sklearn.cluster import KMeans from sklearn.ensemble import IsolationForest from sklearn.neighbors import LocalOutlierFactor from sklearn.svm import OneClassSVM from sklearn.covariance import EllipticEnvelope import numpy as np annotation_config={} def stdAnnotation(df,features,alpha=3,label_column_name='std_label'): df[label_column_name]=np.zeros(df.shape[0],dtype='int') means=df[features].mean() for col,mean in zip(means.index,means.values): print(col,mean) df[label_column_name][(df[col]-mean-alpha*df[col].std())>0]=1 print('Class Count:',df[label_column_name].value_counts()) return df def naAnnotation(X): return pd.isnull(X) def LocalOutlierFactorAnnotation(X,outliers_fraction=0.1,**kwargs): lof=LocalOutlierFactor( n_neighbors=35, contamination=outliers_fraction) return lof.fit_predict(X) def OneClassSVMAnnotation(X,outliers_fraction=0.1,**kawargs): osvm=OneClassSVM(nu=outliers_fraction, kernel="rbf", gamma=0.1) osvm.fit_predict(X) def kmeansAnnotation(): km=KMeans() return km.fit_predict(X) def isolationForestAnnotation(X,outliers_fraction=0.1,**kwargs): isoforest=IsolationForest(behaviour='new', contamination=outliers_fraction, random_state=42,**kwargs) return isoforest.fit(X).predict(X) def EllipticEnvelopeAnnotation(X,outliers_fraction=0.1,**kwargs): ee=EllipticEnvelope(contamination=outliers_fraction) return ee.fit_predict(X) def activeLearningAnnotation(): pass PKN2Mmlvajra/activelearning/AL.pyfrom modAL.models import ActiveLearner,Committee,CommitteeRegressor from modAL.uncertainty import (classifier_entropy,classifier_margin, classifier_uncertainty,uncertainty_sampling, entropy_sampling,entropy) from modAL.disagreement import (consensus_entropy,consensus_entropy_sampling, KL_max_disagreement,max_disagreement_sampling, max_std_sampling,vote_entropy,vote_entropy_sampling) from modAL.expected_error import expected_error_reduction from collections import namedtuple learners=['ActiveLearner','Committee','CommitteeRegressor'] queryStrategies=['classifier_entropy','classifier_margin', 'classifier_uncertainty','uncertainty_sampling', 'entropy_sampling','entropy','expected_error_reduction'] consensus_list=['consensus_entropy','consensus_entropy_sampling', 'KL_max_disagreement','max_disagreement_sampling', 'max_std_sampling','vote_entropy','vote_entropy_sampling'] learner=namedtuple('learner',learners) queryStrategy=namedtuple('queryStrategy',queryStrategies) consensus=namedtuple('consensus',consensus_list) #Learners learner.ActiveLearner=ActiveLearner learner.Committee=Committee learner.CommitteeRegressor=CommitteeRegressor #qs queryStrategy.classifier_entropy=classifier_entropy queryStrategy.classifier_margin=classifier_margin queryStrategy.classifier_uncertainty=classifier_uncertainty queryStrategy.entropy=entropy queryStrategy.entropy_sampling=entropy_sampling queryStrategy.expected_error_reduction=expected_error_reduction queryStrategy.uncertainty_sampling=uncertainty_sampling #consensus consensus.consensus_entropy=consensus_entropy consensus.consensus_entropy_sampling=consensus_entropy_sampling consensus.KL_max_disagreement=KL_max_disagreement consensus.max_disagreement_sampling=max_disagreement_sampling consensus.max_std_sampling=max_std_sampling consensus.vote_entropy=vote_entropy consensus.vote_entropy_sampling=vote_entropy_sampling __all__=['learner','queryStrategy','consensus'] PK)NRff"mlvajra/activelearning/__init__.pyfrom .AL import learner,queryStrategy,consensus __all__=['learner','queryStrategy','consensus'] PKvN6@ ``mlvajra/data/README.md## Reading and writing data utilities for different data stores (DBs) and data sources (streaming jobs) Adding helper functions to read and write from different data sources # adding new Data stores Class Definition consists of simple api pattern and can be extended if needed (suggestions welcomed) ``` class Utils(object): def __init__(self,config:configparser.ConfigParser)->None: self.config=config def readData(self,optimus:Any,path:str=None,**kargs)->DataFrame: raise NotImplementedError def writeData(self,df:DataFrame,path:str=None,**kargs)->None: raise NotImplementedError ``` inhereting this utility class and writing your DB specific writing /reading data functions TODO: Adding support to read /write from data streams (spark/kafka) PKWN''mlvajra/data/__init__.py""" DATA READ/WRITE /Collection """ PKWNw "mlvajra/data/preprocess_config.ini[LOGS] path=/opt/apps/DTNX/logs/ [FillMissing] cont_columns=BerPreFecMax,PhaseCorrectionAve,PmdMin,Qmin,SoPmdAve inpCol=BerPreFecMax,PhaseCorrectionAve,PmdMin,Qmin,SoPmdAve outCol=BerPreFecMax_imputed,PhaseCorrectionAve_imputed,PmdMin_imputed,Qmin_imputed,SoPmdAve_imputed append_for_outcol=imputed strategy=mean path=fillmissing_ch6y_ochctp_30 statspath=/opt/apps/DTNX/models/ [FillMissingCategoryWise] inpCol=BerPreFecMax,PhaseCorrectionAve,PmdMin,Qmin,SoPmdAve outCol=BerPreFecMax_imputed,PhaseCorrectionAve_imputed,PmdMin_imputed,Qmin_imputed,SoPmdAve_imputed append_for_outcol=imputed strategy=mean category=module path=/opt/apps/DTNX/preprocessing_models/fillmissing_per_device_ch6y_ochctp_30 [Categorify] inpCol=module outCol=module_category path=/opt/apps/DTNX/preprocessing_models/category_ch6y_ochctp [vectorizer] inpCol=BerPreFecMax_imputed,PhaseCorrectionAve_imputed,PmdMin_imputed,Qmin_imputed,SoPmdAve_imputed outCol=vectorized_features [Normalize] cont_columns=BerPreFecMax,PhaseCorrectionAve,PmdMin,Qmin,SoPmdAve inpCol=vectorized_features outCol=features_normal path=normalizer_ch6y_ochctp_30 statspath=/opt/apps/DTNX/models/ [Scale] inpCol=vectorized_features outCol=features_scaled withStd=True withMean=False path=scalar_ch6y_ochctp_30 [ScaleCategoryWise] inpCol=vectorized_features outCol=features_scaled withStd=True withMean=False category=module path=/opt/apps/DTNX/preprocessing_models/scalar_per_device_ch6y_ochctp_30 ; path= ; csv_path=actuals1.csv ; hive_sql= [writeData] parquet_path=preprocessed_actuals_parquet_test csv_path=preprocessed_actuals.csv csv_inpCol=features_scaled csv_outCol=BerPreFecMax,PhaseCorrectionAve,PmdMin,Qmin,SoPmdAve [HiveUtils] columns_to_write= vector_column=vector_out, table_name=dummy1 read_sql=select * from coeus.performance_metrics limit 10 [CSVUtils] read_header=True inferschema=True read_path=/opt/apps/DTNX/data/performance_2019-01-17_hr_8.csv write_path=/opt/apps/DTNX/data/example_performance_2019-01-17_hr_8.csv csv_inpCol=features_scaled csv_outCol=BerPreFecMax,PhaseCorrectionAve,PmdMin,Qmin,SoPmdAve columns_to_write=ts,nodeid,section,module vector_column=features_scaled [optimusCsvUtils] read_header=True inferschema=True read_path=/opt/apps/DTNX/prod_to_test/data/full_perf2019-01-20_hr_4.csv write_path=/opt/apps/DTNX/stgdata/data/ex_test.csv mode=append write_header=True [optimusParquetUtils] read_path= write_path= mode= [ParquetUtils] read_parquet_path=actuals_parquet_test write_parquet_path=preprocessed_actuals_parquet_test [transformData] _30_DEVICES=13-L1-9,13-L1-5,13-L1-6,13-L1-10,13-L1-3,13-L1-4,13-L1-8,13-L1-2,13-L1-7,13-L1-1,10-L1-9,10-L1-3,10-L1-1,10-L1-5,10-L1-7,10-L1-4,10-L1-10,10-L1-6,10-L1-8,10-L1-2,11-L1-7,11-L1-3,11-L1-8,11-L1-9,11-L1-1,11-L1-6,11-L1-5,11-L1-2,11-L1-4,11-L1-10 reqcolumns=BerPreFecMax,PhaseCorrectionAve,PmdMin,Qmin,SoPmdAve index_columns=nodeid,section,ts,module pivot_column=measure value_column=val [csv_to_tempdf] tempdbname=pivottable sql=SELECT nodeid, section, module, ts, nodename,max(case when measure='Qmin' then val end )Qmin,max(case when measure='PmdMin' then val end )PmdMin,max(case when measure='BerPreFecMax' then val end )BerPreFecMax,max(case when measure='PhaseCorrectionAve' then val end )PhaseCorrectionAve,max(case when measure='SoPmdAve' then val end )SoPmdAve FROM pivottable where measure in ('BerPreFecMax','PhaseCorrectionAve','PmdMin','Qmin','SoPmdAve') group by nodeid, section, module, ts,nodenamePKNhVmlvajra/data/relationalDB.py from sqlalchemy.engine import create_engine from sqlalchemy import inspect from sqlalchemy import * from sqlalchemy.schema import * import pandas as pd class sqlDB(object): def __init__(self,uri='sqlite:///default.db',**kargs): self.engine=create_engine(uri,**kargs) def readData(self,sql): df=pd.read_sql(sql,self.engine) return df def writeData(self,df,tablename=None): df.to_sql(tablename,self.engine) PKHNݿ " "mlvajra/data/sparkutils.pytry: from optimus import Optimus from pyspark.sql import DataFrame,SparkSession import configparser import os import logging import pygogo as gogo import sys import types from typing import Any from functools import reduce except ImportError as e: print("Some packages are not installed ",e) log_format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s' formatter = logging.Formatter(log_format) config = configparser.ConfigParser() config.read('preprocess_config.ini') op =Optimus() __all__=['Utils','optimusCsvUtils','optimusParquetUtils','HiveUtils'] class Utils(object): def __init__(self,config:configparser.ConfigParser)->None: self.config=config def readData(self,optimus:Any,path:str=None,**kargs)->DataFrame: raise NotImplementedError def writeData(self,df:DataFrame,path:str=None,**kargs)->None: raise NotImplementedError class optimusJDBC(Utils): __name__='optimusJDBC' def __init__(self,config): try : import jaydebeapi except ImportError: raise Exception("jaydebeapi not installed") self.config=config self.logger = gogo.Gogo( 'readWriteUtils.fmt', low_hdlr=gogo.handlers.file_hdlr('{}/{}.log'.format(self.config['LOGS']['path'], self.__name__)), low_formatter=formatter, high_level='error', high_formatter=formatter).logger self.conn=jaydebeapi.connect(self.config[self.__name__]['classname'], self.config[self.__name__]['jdbc_url'], [self.config[self.__name__]['username'],self.config[self.__name__]['password']], self.config[self.__name__]['jar_path']) def readData(self,optimus,sql): pdf=pd.read_sql(sql,self.conn) sdf=optimus.spark.createDataFrame(pdf) return sdf def push_to_hive(self,df,tablename,columns): q1 = "INSERT INTO {} ".format(tablename) + str(tuple(columns)).replace("""'""", "") + " VALUES " for ind, row in df.iterrows(): #print(q1) tmp = str(tuple(row.values.tolist()))+',' q1+=tmp q1 = q1[:-1] #print(q1) curr=self.conn.cursor() curr.execute(q1) def writeData(self,sdf,tablenames=None,columns=None): pdf=sdf.toPandas() if not columns: columns=self.config[self.__name__]['stg_columns'].split(',') if not tablenames: tablenames=self.config[self.__name__]['writetablename'] #pdf.to_sql(self.config[self.__name__]['writetablename'],self.conn, # schema=self.config[self.__name__]['writetablename'], # if_exists='append',index=False,index_label=None) print(tablenames,columns) if tablenames=='coeusapp.ml_performance_metrics_prestg': df=pd.DataFrame() df[columns]=pdf[columns] df['module_part']=pdf['nodename'] columns=columns+['module_part'] else: print('else') df=pdf for i in range(0,len(df),10000): print(i) tmp=df[i:i+10000] self.push_to_hive(tmp,tablenames,columns) class optimusCsvUtils(Utils): __name__='optimusCsvUtils' def __init__(self, config:configparser.ConfigParser)->None: self.config=config self.logger = gogo.Gogo( 'readWriteUtils.fmt', low_hdlr=gogo.handlers.file_hdlr('{}/{}.log'.format(self.config['LOGS']['path'], self.__name__)), low_formatter=formatter, high_level='error', high_formatter=formatter).logger #super().__init__(config, logger) def readData(self,optimus:Optimus,path:str=None,**kargs)->DataFrame: try: if not path: path=self.config[self.__name__]['read_path'] df=optimus.load.csv(path=path,header=self.config[self.__name__]['read_header'], infer_schema=self.config[self.__name__]['inferschema']) self.logger.info('{}-Data read from CSV -{}'.format(self.__name__,path)) return df except Exception as e: self.logger.error('Error occured {} Reading data- {}'.format(self.__name__,e)) def writeData(self,df:DataFrame,path:str=None,**kargs)->None: try: if not path: path=self.config[self.__name__]['write_path'] df.save.csv(path=path, header=self.config[self.__name__]['write_header'], mode=self.config[self.__name__]['mode']) self.logger.info('{}-Data written to the CSV {} -'.format(self.__name__,path)) except Exception as e: self.logger.error('Error occured in {} Writing data- {}'.format(self.__name__,e)) class optimusParquetUtils(Utils): __name__='optimusParquetUtils' def __init__(self, config:configparser.ConfigParser)->None: self.config=config self.logger = gogo.Gogo( 'readWriteUtils.fmt', low_hdlr=gogo.handlers.file_hdlr('{}/{}.log'.format(self.config['LOGS']['path'], self.__name__)), low_formatter=formatter, high_level='error', high_formatter=formatter).logger def readData(self,optimus:Optimus,path:str=None,**kwargs)->DataFrame: try: if not path: path=self.config[self.__name__]['read_path'] df=optimus.load.parquet(path=path,**kwargs) self.logger.info('{}-Data read from CSV -'.format(self.__name__)) return df except Exception as e: self.logger.error('Error occured {} Reading data- {}'.format(self.__name__,e)) def writeData(self,df:DataFrame,path:str=None,**kwargs)->None: try: if not path: path=self.config[self.__name__]['write_path'] df.save.parquet(path=path,mode=self.config[self.__name__]['mode']) self.logger.info('{}-Data written to the CSV {} -'.format(self.__name__,self.config[self.__name__]['write_path'])) except Exception as e: self.logger.error('Error occured in {} Writing data- {}'.format(self.__name__,e)) class HiveUtils(Utils): __name__='HiveUtils' def __init__(self, config:configparser.ConfigParser)->None: self.config=config self.logger = gogo.Gogo( 'readWriteUtils.fmt', low_hdlr=gogo.handlers.file_hdlr('/opt/apps/DTNX/logs/{}.log'.format(self.__name__)), low_formatter=formatter, high_level='error', high_formatter=formatter).logger def readData(self,sparkSession:SparkSession,**kwargs)->DataFrame: try: df=sparkSession.sql(self.config[self.__name__]['read_sql'],**kwargs) self.logger.info('{}-Data read from Hive -'.format(self.__name__)) return df except Exception as e: self.logger.error('Error occured {} Reading data- {}'.format(self.__name__,e)) def writeData(self,df:DataFrame,**kargs)->None: try: columns=[df[col] for col in self.config[self.__name__]['columns_to_write']] df=df.select(*columns,df[self.config[self.__name__]['vector_column']].cast("string")) 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mlvajra/deploy/clipper.pytry : from clipper_admin import ClipperConnection, DockerContainerManager from clipper_admin.deployers import python as python_deployer from clipper_admin.deployers import pytorch from clipper_admin.deployers import pyspark from clipper_admin.deployers import tensorflow except ImportError as e: print("install clipper_admin to have this deployment functionalities") from enum import IntEnum models= IntEnum('models', 'PYTHON PYTORCH PYSPARK TENSORFLOW') __all__=['mlDeploy'] class mlDeploy(object): def __init__(self,**kwargs): self.conn = ClipperConnection(DockerContainerManager()) self.conn.start_clipper() def create_application(self,app_name,input_type, default_output="default_output from model",slo_micros=100000): self.app_name=app_name self.input_type=input_type self.conn.register_application(self.app_name,input_type,default_output,slo_micros) def deploy_model(self,model_name,func_to_deploy, version,pkgs_to_install, model_type=models.PYTHON, model=None,sess=None,sc=None): self.model_name=model_name if model_type=='PYTHON': python_deployer.deploy_python_closure(self.conn, name=self.model_name, version=version, input_type=self.input_type, func=func_to_deploy, pkgs_to_install=pkgs_to_install) if model_type=='PYTORCH': pytorch.deploy_pytorch_model(self.conn, name=self.model_name, version=version, input_type=self.input_type, func=func_to_deploy,pytorch_model=model) if model_type=='TENSORFLOW': tensorflow.deploy_tensorflow_model(self.conn, name=self.model_name, version=version, input_type=self.input_type, func=func_to_deploy,tf_sess =sess) if model_type=='PYSPARK': pyspark.deploy_pyspark_model(self.conn, name=self.model_name, version=version, input_type=self.input_type, func=func_to_deploy,pyspark_model=model,sc=sc) def link_app_and_model(self): self.conn.link_model_to_app(self.app_name,self.model_name) def stop_service(self): self.conn.stop_all() def model_roll_back(self,version): self.conn.set_model_version(version) def model_replicas(self,num_replicas): self.conn.set_num_replicas(num_replicas) def get_address(self): return self.conn.get_query_addr() PKHNf̸11mlvajra/deploy/tfx.pyimport tensorflow_data_validation as tfdv import tensorflow_estimator import tensorflow_metadata import tensorflow_model_analysis import tensorflow_transform __author__='Gurunath' from datetime import timedelta import os import airflow from airflow import DAG from airflow.operators.bash_operator import BashOperator from airflow.operators.email_operator import EmailOperator from airflow.operators.dummy_operator import DummyOperator from airflow.operators.bash_operator import BashOperator from airflow.utils.trigger_rule import TriggerRule from airflow.operators.python_operator import PythonOperator,BranchPythonOperator from datetime import datetime from scripts import (SPARK_PULL_SERVICE,SPARK_PUSH_SERVICE,PRED_PREPROCESSING_SERVICE, SPARK_PREDICTION,SPARK_TABLE_SUMMARY,SPARK_TRAINING,TRAIN_PREPROCESSING_SERVICE) from airflow.sensors.training_plugin import TrainingSensor from airflow.sensors.time_sensor import TimeSensor from airflow.utils import timezone # These args will get passed on to each operator ["sun","mon","tues","wed","thu","fri","sat"] # You can override them on a per-task basis during operator initialization default_args = { 'owner': 'Prodapt_mlteam', 'depends_on_past': False, 'start_date': '2019-04-05', 'email': ['n9979623@windstream.com'], 'email_on_failure': True, 'email_on_retry': False, 'retries': 2, 'retry_delay': timedelta(minutes=5), # 'queue': 'bash_queue', # 'pool': 'backfill', # 'priority_weight': 10, # 'end_date': datetime(2016, 1, 1), # 'wait_for_downstream': False, # 'dag': dag, #'adhoc':False,3417 # 'sla': timedelta(hours=2), # 'execution_timeout': timedelta(seconds=300), # 'on_failure_callback': some_function, # 'on_success_callback': some_other_function, # 'on_retry_callback': another_function, # 'trigger_rule': u'all_success' } SUMMARY_TRIGGER_HOUR=23 TRAINING_TRIGGER_HOUR=23 # TRAINING_TRIGGER_DAY=0 #TODO:sunday nodename='CHCGILDTO6Y' nodeid='MA6813400180' section='h_OCHCTP' dag = DAG( 'CHCGILDTO6Y_test', default_args=default_args, description='gets the data and uses spark to fetch the data from hive tables ,preprocess and put the data in another hive tables', schedule_interval='@hourly', catchup=True, ) def sg_trigger_or_not(): if timezone.utcnow().time().hour==SUMMARY_TRIGGER_HOUR: return "kickoff_summaryGen" else : return "SummaryGen_skipped" def training_trigger_or_not(): if (timezone.utcnow().day==TRAINING_TRIGGER_DAY) and (timezone.utcnow().time().hour==TRAINING_TRIGGER_HOUR): return "TRAIN_PREPROCESSING" else : return "PRED_PREPROCESSING" with dag: END_TIME="{{macros.dateutil.parser.parse(ts).replace(minute=0,second=0).strftime('%Y-%m-%d %H:%M:%S')}}" START_TIME="{{(macros.dateutil.parser.parse(ts)-macros.timedelta(hours=1)).replace(minute=0,second=0).strftime('%Y-%m-%d %H:%M:%S')}}" TRAIN_START_TIME="{{(macros.dateutil.parser.parse(ts)-macros.timedelta(hours=2400)).replace(minute=0,second=0).strftime('%Y-%m-%d %H:%M:%S')}}" #starttime="{{macros.datetime.strptime((macros.dateutil.parser.parse(ts)-macros.timedelta(hours=1)).replace(minute=0,second=0).strftime('%Y%m%d%H%M%S')}}" #endtime="{{datetime.strptime(macros.dateutil.parser.parse(ts).replace(minute=0,second=0).strftime('%Y%m%d%H%M%S')}}" starttime="{{(macros.dateutil.parser.parse(ts)-macros.timedelta(hours=1)).replace(minute=0,second=0).strftime('%Y%m%d%H%M%S')}}" endtime="{{macros.dateutil.parser.parse(ts).replace(minute=0,second=0).strftime('%Y%m%d%H%M%S')}}" # -1 for starttime and -9 from summary service TmpStartTime ="{{(macros.dateutil.parser.parse(ts)-macros.timedelta(hours=10)).replace(minute=0,second=0).strftime('%Y-%m-%d %H:%M:%S')}}" # (datetime.datetime.strptime(StartTime, '%Y-%m-%d %H:%M:%S') # -datetime.timedelta(hours=9)).strftime('%Y-%m-%d %H:%M:%S') # PULLHQL="SELECT nodeid, section, module, ts, measure, val, nodename \ # FROM dnapm.performance_metrics\ # where nodename='CHCGILDTO6Y'\ # and section='h_OCHCTP'\ # and measure in ('BerPreFecMax','PhaseCorrectionAve','PmdMin','Qmin','SoPmdAve')\ # and module in ('13-L1-9','13-L1-5','13-L1-6','13-L1-10','13-L1-3','13-L1-4','13-L1-8','13-L1-2','13-L1-7','13-L1-1',\ # '10-L1-9','10-L1-3','10-L1-1','10-L1-5','10-L1-7','10-L1-4','10-L1-10','10-L1-6','10-L1-8','10-L1-2',\ # '11-L1-7','11-L1-3','11-L1-8','11-L1-9','11-L1-1','11-L1-6','11-L1-5','11-L1-2','11-L1-4','11-L1-10')\ # and ts>='{}' and ts<'{}'".format(START_TIME,END_TIME) # PUSHHQL="insert into table coeusapp.ml_performance_metrics_prestg (nodeid, section, module,ts,measure,val,type)\ # select nodeid,trim(section),trim(module),ts,trim(measure),val,type from coeusapp.ml_performance_metrics \ # where type='actual' and unix_timestamp(ts) >=unix_timestamp('{}') and unix_timestamp(ts)>> y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2] >>> y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2] >>> clfExp=clasificationExperiement() >>> cm=clfExp.warp_train_method(y_actu,y_pred) >>> print(cm) """ def __init__(self): pass def wrap_train_method(self,y_true,y_predict,save_as_report=None): cm=classification_metrics.ConfusionMatrix(actual_vector=y_true,predict_vector=y_predict) if save_as_report: cm.save_html(save_as_report+'cm.html') cm.save_csv(save_as_report+'cm.csv') cm.save_stat(save_as_report+'cm') cm.save_obj(save_as_report+'cm') return cm class regressionExperiement(BaseExperiment): def __init__(self): pass def wrap_train_method(self,y_true,y_predict,save_as_report=None): experiment_dict={} for metric in['mean_absolute_error','mean_squared_error', 'mean_squared_log_error','median_absolute_error','r2_score']: experiment_dict[metric]=regression_metrics.__dict__[metric](y_true,y_predict) if save_as_report: import json json.dump(experiment_dict,open(save_as_report+'reg_metrics.json','w')) return experiment_dict if __name__=='__main__': y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2] y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2] clfExp=clasificationExperiement() cm=clfExp.wrap_train_method(y_actu,y_pred) print(cm) PKpN'\\mlvajra/experiment/__init__.pyfrom .Experiement import clasificationExperiement,regressionExperiement,BaseExperiment PKWNu٩ mlvajra/explanations/__init__.py""" explanations """ PK7NW!mlvajra/explanations/globalExp.pyfrom sklearn.ensemble.partial_dependence import partial_dependence,plot_partial_dependence from eli5.sklearn import PermutationImportance __all__=['PermutationImportance','partial_dependence','plot_partial_dependence']PKJNj(A A mlvajra/explanations/localExp.pyimport shap import numpy as np import pandas as pd import enum from typing import Callable from lime.lime_tabular import LimeTabularExplainer from sklearn.preprocessing import MinMaxScaler try: import tensorflow as tf from sklearn.base import BaseEstimator import catboost import xgboost except ImportError as e: print(e) finally: print("Above Modules are not available -use other supported packages") class Explain(object): def __init__(self,path:str,modelloader:Callable): self.modelloader=modelloader def explain(self,exptype): pass def applytransform(self,): pass def exportExplanation(self,): pass class LimeExp(Explain): def __init__(self,config,df=None): self.config=config self.sc=MinMaxScaler() if not isinstance(df,pd.DataFrame): df=pd.read_csv('../temp_data/val.csv.tar.gz') features=self.sc.fit_transform(df[config['columns']].dropna()) self.explainer=LimeTabularExplainer(features, feature_names=self.config['columns'], class_names=['not anomaly','anomaly'], discretize_continuous=True) #pickle.dump(self.explainer,open('{}/limeexp.pkl'.format(self.DIR))) def explain(self,array:np.array,model): #self.explainer=pickle.load(open('{}/limeexp.pkl'.format(self.DIR))) response,contrib=[],[] for instance in array: exp = self.explainer.explain_instance(instance,model.predict_proba, num_features=5,num_samples=1000) dict_=dict(exp.as_list()) response.append(",".join(list(dict_.keys()))) contrib.append(",".join(list(map(str,dict_.values())))) return response,contrib class ShapExp(Explain): def __init__(self,config,df=None,model=None): self.config=config if not isinstance(df,pd.DataFrame): df=pd.read_csv('../temp_data/val.csv.tar.gz') if not model: import tensorflow as tf model=tf.keras.models.load_model(config['model_path']) self.explainer = shap.KernelExplainer(model.predict, df.loc[:10000,config['columns']]) def explain(self,array): shap_list=[] for instance in array: shap_values = self.explainer.shap_values(instance, nsamples=50) shap_list.append(np.mean(np.array(shap_values),axis=0)) return [self.config['columns']]*len(shap_list),np.array(shap_list) # if __name__=='__main__': # CONFIG={} # she=ShapExp(CONFIG) # #s,a=she.explain(X.iloc[:10].values) PKWON1[[mlvajra/model/DSL.py""" Command Line Interface ====================== Ludwig provides six command line interface entry points - train - predict - experiment - visualize - collect_weights - collect_activations They are described in detail below. train ----- This command lets you train a model from your data. You can call it with: ``` ludwig train [options] ``` or with ``` python -m ludwig.train [options] ``` from within Ludwig's main directory. These are the available arguments: ``` usage: ludwig train [options] This script trains a model. optional arguments: -h, --help show this help message and exit --output_directory OUTPUT_DIRECTORY directory that contains the results --experiment_name EXPERIMENT_NAME experiment name --model_name MODEL_NAME name for the model --data_csv DATA_CSV input data CSV file. If it has a split column, it will be used for splitting (0: train, 1: validation, 2: test), otherwise the dataset will be randomly split --data_train_csv DATA_TRAIN_CSV input train data CSV file --data_validation_csv DATA_VALIDATION_CSV input validation data CSV file --data_test_csv DATA_TEST_CSV input test data CSV file --data_hdf5 DATA_HDF5 input data HDF5 file. It is an intermediate preprocess version of the input CSV created the first time a CSV file is used in the same directory with the same name and a hdf5 extension --data_train_hdf5 DATA_TRAIN_HDF5 input train data HDF5 file. It is an intermediate preprocess version of the input CSV created the first time a CSV file is used in the same directory with the same name and a hdf5 extension --data_validation_hdf5 DATA_VALIDATION_HDF5 input validation data HDF5 file. It is an intermediate preprocess version of the input CSV created the first time a CSV file is used in the same directory with the same name and a hdf5 extension --data_test_hdf5 DATA_TEST_HDF5 input test data HDF5 file. It is an intermediate preprocess version of the input CSV created the first time a CSV file is used in the same directory with the same name and a hdf5 extension --train_set_metadata_json TRAIN_SET_METADATA_JSON input metadata JSON file. It is an intermediate preprocess file containing the mappings of the input CSV created the first time a CSV file is used in the same directory with the same name and a json extension -sspi, --skip_save_processed_input skips saving intermediate HDF5 and JSON files -md MODEL_DEFINITION, --model_definition MODEL_DEFINITION model definition -mdf MODEL_DEFINITION_FILE, --model_definition_file MODEL_DEFINITION_FILE YAML file describing the model. Ignores --model_hyperparameters -mlp MODEL_LOAD_PATH, --model_load_path MODEL_LOAD_PATH path of a pretrained model to load as initialization -mrp MODEL_RESUME_PATH, --model_resume_path MODEL_RESUME_PATH path of a the model directory to resume training of -ssm, --skip_save_model disables saving weights each time the model imrpoves. By default Ludwig saves weights after each epoch the validation measure imrpvoes, but if the model is really big that can be time consuming if you do not want to keep the weights and just find out what performance can a model get with a set of hyperparameters, use this parameter to skip it. -ssp, --skip_save_progress disables saving weights after each epoch. By default ludwig saves weights after each epoch for enabling resuming of training, but if the model is really big that can be time consuming and will save twice as much space, use this parameter to skip it. -ssl, --skip_save_log disables saving TensorBoard logs. By default Ludwig saves logs for the TensorBoard, but if it is not needed turning it off can slightly increase the overall speed. -rs RANDOM_SEED, --random_seed RANDOM_SEED a random seed that is going to be used anywhere there is a call to a random number generator: data splitting, parameter initialization and training set shuffling -g GPUS [GPUS ...], --gpus GPUS [GPUS ...] list of gpus to use -gf GPU_FRACTION, --gpu_fraction GPU_FRACTION fraction of gpu memory to initialize the process with -uh, --use_horovod uses horovod for distributed training -dbg, --debug enables debugging mode -l {critical,error,warning,info,debug,notset}, --logging_level {critical,error,warning,info,debug,notset} the level of logging to use ``` When Ludwig trains a model it creates two intermediate files, one HDF5 and one JSON. The HDF5 file contains the data mapped to numpy ndarrays, while the JSON file contains the mappings from the values in the tensors to their original labels. For instance, for a categorical feature with 3 possible values, the HDF5 file will contain integers from 0 to 3 (with 0 being a `` category), while the JSON file will contain a `idx2str` list containing all tokens (`[, label_1, label_2, label_3]`), a `str2idx` dictionary (`{"": 0, "label_1": 1, "label_2": 2, "label_3": 3}`) and a `str2freq` dictionary (`{"": 0, "label_1": 93, "label_2": 55, "label_3": 24}`). The reason to have those intermediate files is two-fold: on one hand, if you are going to train your model again Ludwig will try to load them instead of recomputing all tensors, which saves a consistent amount of time, and on the other hand when you want to use your model to predict, data has to be mapped to tensors in exactly the same way it was mapped during training, so you'll be required to load the JSON metadata file in the `predict` command. The way this works is: the first time you provide a CSV (`--data_csv`), the HDF5 and JSON files are created, from the second time on Ludwig will load them instead of the CSV even if you specify the CSV (it looks in the same directory for files names in the same way but with a different extension), finally you can directly specify the HDF5 and JSON files (`--data_hdf5` and `--metadata_json`). As the mapping from raw data to tensors depends on the type of feature that you specify in your model definition, if you change type (for instance from `sequential` to `text`) you also have to redo the preprocessing, which is achieved by deleting the HDF5 and JSON files. Alternatively you can skip saving the HDF5 and JSON files specifying `--skip_save_processed_input`. Splitting between train, validation and test set can be done in several ways. This allows for a few possible input data scenarios: - one single CSV file is provided (`-data_csv`). In this case if the csv contains a `split` column with values `0` for training, `1` for validation and `2` for test, this split will be used. If you want to ignore the split column and perform a random split, use a `force_split` argument in the model definition. In the case when there is no split column, a random `70-20-10` split will be performed. You can set the percentages and specify if you want stratified sampling in the model definition preprocessing section. - you can provide separate train, validation and test CSVs (`--data_train_csv`, `--data_validation_csv`, `--data_test_csv`). - the HDF5 and JSON file indications specified in the case of a single CSV file apply also in the multiple files case (`--data_train_hdf5`, `--data_validation_hdf5`, `--data_test_hdf5`), with the only difference that you need to specify only one JSON file (`--metadata_json`) instead of three. The validation set is optional, but if absent the training wil continue until the end of the training epochs, while when there's a validation set the default behavior is to perform early stopping after the validation measure does not improve for a a certain amount of epochs. The test set is optional too. Other optional arguments are `--output_directory`, `--experiment_name` and `--model name`. By default the output directory is `./results`. That directory will contain a directory named `[experiment_name]_[model_name]_0` if model name and experiment name are specified. If the same combination of experiment and model name is used again, the integer at the end of the name wil be increased. If neither of them is specified the directory will be named `run_0`. The directory will contain - `description.json` - a file containing a description of the training process with all the information to reproduce it. - `training_statistics.json` which contains records of all measures and losses for each epoch. - `model` - a directory containing model hyperparameters, weights, checkpoints and logs (for TensorBoard). The model definition can be provided either as a string (`--model_definition`) or as YAML file (`--model_definition_file`). Details on how to write your model definition are provided in the [Model Definition](#model-definition) section. During training Ludwig saves two sets of weights for the model, one that is the weights at the end of the epoch where the best performance on the validation measure was achieved and one that is the weights at the end of the latest epoch. The reason for keeping the second set is to be able to resume training in case the training process gets interrupted somehow. To resume training using the latest weights and the whole history of progress so far you have to specify the `--model_resume_path` argument. You can avoid saving the latest weights and the overall progress so far by using the argument `--skip_save_progress`, but you will not be able to resume it afterwards. Another available option is to load a previously trained model as an initialization for a new training process. In this case Ludwig will start a new training process, without knowing any progress of the previous model, no training statistics, nor the number of epochs the model has been trained on so far. It's not resuming training, just initializing training with a previously trained model with the same model definition, and it is accomplished through the `--model_load_path` argument. You can specify a random sed to be used by the python environment, python random package, numpy and TensorFlow with the `--random_seed` argument. This is useful for reproducibility. Be aware that due to asynchronicity in the TensorFlow GPU execution, when training on GPU results may not be reproducible. You can manage which GPUs on your machine are used with the `--gpus` argument, which accepts a string identical to the format of `CUDA_VISIBLE_DEVICES` environment variable, namely a list of integers separated by comma. You can also specify the fraction of the GPU memory that will be initially assigned to TensorFlow with `--gpu_fraction`. By default it is 1.0, but you can set it, for instance, to 0.2 to use only 1/5 of the available memory. If TensorFlow will need more GPU memory it will try to increase this amount. Finally the `--logging_level` argument lets you set the amount of logging that you want to see during training and the `--debug` argument turns on TensorFlow's `tfdbg`. Be careful when doing so, as it will help in catching errors, in particular infs and NaNs but it will consume much more memory. Example: ``` ludwig train --data_csv reuters-allcats.csv --model_definition "{input_features: [{name: text, type: text, encoder: parallel_cnn, level: word}], output_features: [{name: class, type: category}]}" ``` predict ------- This command lets you use a previously trained model to predict on new data. You can call it with: ``` ludwig predict [options] ``` or with ``` python -m ludwig.predict [options] ``` from within Ludwig's main directory. These are the available arguments: ``` usage: ludwig predict [options] This script loads a pretrained model and uses it to predict. optional arguments: -h, --help show this help message and exit --data_csv DATA_CSV input data CSV file. If it has a split column, it will be used for splitting (0: train, 1: validation, 2: test), otherwise the dataset will be randomly split --data_hdf5 DATA_HDF5 input data HDF5 file. It is an intermediate preprocess version of the input CSV created the first time a CSV file is used in the same directory with the same name and a hdf5 extension --train_set_metadata_json TRAIN_SET_METADATA_JSON input metadata JSON file. It is an intermediate preprocess file containing the mappings of the input CSV created the first time a CSV file is used in the same directory with the same name and a json extension -s {training,validation,test,full}, --split {training,validation,test,full} the split to test the model on -m MODEL_PATH, --model_path MODEL_PATH model to load -od OUTPUT_DIRECTORY, --output_directory OUTPUT_DIRECTORY directory that contains the results -ssuo, --skip_save_unprocessed_output skips saving intermediate NPY output files -bs BATCH_SIZE, --batch_size BATCH_SIZE size of batches -op, --only_predictions skip metrics calculation -g GPUS, --gpus GPUS list of gpu to use -gf GPU_FRACTION, --gpu_fraction GPU_FRACTION fraction of gpu memory to initialize the process with -uh, --use_horovod uses horovod for distributed training -dbg, --debug enables debugging mode -l {critical,error,warning,info,debug,notset}, --logging_level {critical,error,warning,info,debug,notset} the level of logging to use ``` The same distinction between CSV files and HDF5 / JSON files explained in the [train](#train) section also applies here. In either case, the JSON metadata file obtained during training is needed in order to map the new data into tensors. If the new data contains a split column, you can specify which split to use to calculate the predictions with the `--split` argument. By default it's `full` which means all the splits will be used. A model to load is needed, and you can specify its path with the `--model_path` argument. If you trained a model previously and got the results in, for instance, `./results/experiment_run_0`, you have to specify `./results/experiment_run_0/model` for using it to predict. You can specify an output directory with the argument `--output-directory`, by default it will be `./result_0`, with increasing numbers if a directory with the same name is present. The directory will contain a prediction CSV file and a probability CSV file for each output feature, together with raw NPY files containing raw tensors and a `predict_statistics.json` file containing all prediction statistics. By specifying the argument `--only_prediction` you will not get the statistics. This parameter is needed in the case your data does not contain ground truth output values, and thus computing the prediction statistics would be impossible. If you receive an error regarding a missing output feature column in your data, you probably forgot to specify this argument. You can moreover specify not to save the raw NPY output files with the argument `skip_save_unprocessed_output`. A specific batch size for speeding up the prediction can be specified using the argument `--batch_size`. Finally the `--logging_level`, `--debug` and `--gpus` related arguments behave exactly like described in the train command section. Example: ``` ludwig predict --data_csv reuters-allcats.csv --model_path results/experiment_run_0/model/ ``` experiment ---------- This command combines training and prediction into a single handy command. You can call it with: ``` ludwig experiment [options] ``` or with ``` python -m ludwig.experiment [options] ``` from within Ludwig's main directory. These are the available arguments: ``` usage: ludwig experiment [options] This script trains and tests a model. optional arguments: -h, --help show this help message and exit --output_directory OUTPUT_DIRECTORY directory that contains the results --experiment_name EXPERIMENT_NAME experiment name --model_name MODEL_NAME name for the model --data_csv DATA_CSV input data CSV file. If it has a split column, it will be used for splitting (0: train, 1: validation, 2: test), otherwise the dataset will be randomly split --data_train_csv DATA_TRAIN_CSV input train data CSV file --data_validation_csv DATA_VALIDATION_CSV input validation data CSV file --data_test_csv DATA_TEST_CSV input test data CSV file --data_hdf5 DATA_HDF5 input data HDF5 file. It is an intermediate preprocess version of the input CSV created the first time a CSV file is used in the same directory with the same name and a hdf5 extension --data_train_hdf5 DATA_TRAIN_HDF5 input train data HDF5 file. It is an intermediate preprocess version of the input CSV created the first time a CSV file is used in the same directory with the same name and a hdf5 extension --data_validation_hdf5 DATA_VALIDATION_HDF5 input validation data HDF5 file. It is an intermediate preprocess version of the input CSV created the first time a CSV file is used in the same directory with the same name and a hdf5 extension --data_test_hdf5 DATA_TEST_HDF5 input test data HDF5 file. It is an intermediate preprocess version of the input CSV created the first time a CSV file is used in the same directory with the same name and a hdf5 extension --train_set_metadata_json TRAIN_SET_METADATA_JSON input train set metadata JSON file. It is an intermediate preprocess file containing the mappings of the input CSV created the first time a CSV file is used in the same directory with the same name and a json extension -sspi, --skip_save_processed_input skips saving intermediate HDF5 and JSON files -ssuo, --skip_save_unprocessed_output skips saving intermediate NPY output files -md MODEL_DEFINITION, --model_definition MODEL_DEFINITION model definition -mdf MODEL_DEFINITION_FILE, --model_definition_file MODEL_DEFINITION_FILE YAML file describing the model. Ignores --model_hyperparameters -mlp MODEL_LOAD_PATH, --model_load_path MODEL_LOAD_PATH path of a pretrained model to load as initialization -mrp MODEL_RESUME_PATH, --model_resume_path MODEL_RESUME_PATH path of a the model directory to resume training of -ssp SKIP_SAVE_PROGRESS_WEIGHTS, --skip_save_progress SKIP_SAVE_PROGRESS_WEIGHTS disables saving weights after each epoch. By default Ludwig saves weights after each epoch for enabling resuming of training, but if the model is really big that can be time consuming and will use twice as much storage space, use this parameter to skip it. -rs RANDOM_SEED, --random_seed RANDOM_SEED a random seed that is going to be used anywhere there is a call to a random number generator: data splitting, parameter initialization and training set shuffling -g GPUS [GPUS ...], --gpus GPUS [GPUS ...] list of gpus to use -gf GPU_FRACTION, --gpu_fraction GPU_FRACTION fraction of gpu memory to initialize the process with -dbg, --debug enables debugging mode -l {critical,error,warning,info,debug,notset}, --logging_level {critical,error,warning,info,debug,notset} the level of logging to use ``` The parameters combine parameters from both [train](#train) and [predict](#predict) so please refer to those sections for an in depth explanation. The output directory will contain the outputs both commands produce. Example: ``` ludwig experiment --data_csv reuters-allcats.csv --model_definition "{input_features: [{name: text, type: text, encoder: parallel_cnn, level: word}], output_features: [{name: class, type: category}]}" ``` visualize --------- This command lets you visualize training and prediction statistics, alongside with comparing different models performances and predictions. You can call it with: ``` ludwig visualize [options] ``` or with ``` python -m ludwig.visualize [options] ``` from within Ludwig's main directory. These are the available arguments: ``` usage: ludwig visualize [options] This script analyzes results and shows some nice plots. optional arguments: -h, --help show this help message and exit -d DATA_CSV, --data_csv DATA_CSV raw data file -g GROUND_TRUTH, --ground_truth GROUND_TRUTH ground truth file -gm GROUND_TRUTH_METADATA, --ground_truth_metadata GROUND_TRUTH_METADATA input metadata JSON file -v {compare_performance,compare_classifiers_performance_from_prob,compare_classifiers_performance_from_pred,compare_classifiers_performance_changing_k,compare_classifiers_performance_subset,compare_classifiers_predictions,compare_classifiers_predictions_distribution,confidence_thresholding,confidence_thresholding_2thresholds_3d,confidence_thresholding_data_vs_acc,confidence_thresholding_2thresholds_2d,confidence_thresholding_data_vs_acc_subset,confidence_thresholding_data_vs_acc_subset_per_class,binary_threshold_vs_metric,roc_curves,roc_curves_from_test_statistics,data_vs_acc_subset,data_vs_acc_subset_per_class,calibration_1_vs_all,calibration_multiclass,confusion_matrix,compare_classifiers_multiclass_multimetric,frequency_vs_f1,learning_curves}, --visualization {compare_performance,compare_classifiers_performance_from_prob,compare_classifiers_performance_from_pred,compare_classifiers_performance_changing_k,compare_classifiers_performance_subset,compare_classifiers_predictions,compare_classifiers_predictions_distribution,confidence_thresholding,confidence_thresholding_2thresholds_3d,confidence_thresholding_data_vs_acc,confidence_thresholding_2thresholds_2d,confidence_thresholding_data_vs_acc_subset,confidence_thresholding_data_vs_acc_subset_per_class,binary_threshold_vs_metric,roc_curves,roc_curves_from_test_statistics,data_vs_acc_subset,data_vs_acc_subset_per_class,calibration_1_vs_all,calibration_multiclass,confusion_matrix,compare_classifiers_multiclass_multimetric,frequency_vs_f1,learning_curves} type of visualization -f FIELD, --field FIELD field containing ground truth -tf THRESHOLD_FIELDS [THRESHOLD_FIELDS ...], --threshold_fields THRESHOLD_FIELDS [THRESHOLD_FIELDS ...] fields for 2d threshold -pred PREDICTIONS [PREDICTIONS ...], --predictions PREDICTIONS [PREDICTIONS ...] predictions files -prob PROBABILITIES [PROBABILITIES ...], --probabilities PROBABILITIES [PROBABILITIES ...] probabilities files -tes TRAINING_STATS [TRAINING_STATS ...], --training_statistics TRAINING_STATS [TRAINING_STATS ...] training stats files -trs TEST_STATS [TEST_STATS ...], --test_statistics TEST_STATS [TEST_STATS ...] test stats files -alg ALGORITHMS [ALGORITHMS ...], --algorithms ALGORITHMS [ALGORITHMS ...] names of the algorithms (for better graphs) -tn TOP_N_CLASSES [TOP_N_CLASSES ...], --top_n_classes TOP_N_CLASSES [TOP_N_CLASSES ...] number of classes to plot -k TOP_K, --top_k TOP_K number of elements in the ranklist to consider -ll LABELS_LIMIT, --labels_limit LABELS_LIMIT maximum numbers of labels. If labels in dataset are higher than this number, "rare" label -ss {ground_truth,predictions}, --subset {ground_truth,predictions} type of subset filtering -n, --normalize normalize rows in confusion matrix -m METRICS [METRICS ...], --metrics METRICS [METRICS ...] metrics to dispay in threshold_vs_metric -pl POSITIVE_LABEL, --positive_label POSITIVE_LABEL label of the positive class for the roc curve -l {critical,error,warning,info,debug,notset}, --logging_level {critical,error,warning,info,debug,notset} the level of logging to use ``` As the `--visualization` parameters suggests, there is a vast number of visualizations readily available. Each of them requires a different subset of this command's arguments, so they will be described one by one in the [Visualizations](#visualizations) section. collect_weights --------------- This command lets you load a pre-trained model and collect the tensors with a specific name in order to save them in a NPY format. This may be useful in order to visualize the learned weights (for instance collecting embedding matrices) and for some post-hoc analyses. You can call it with: ``` ludwig collect_weights [options] ``` or with ``` python -m ludwig.collect weights [options] ``` from within Ludwig's main directory. These are the available arguments: ``` usage: ludwig collect_weights [options] This script loads a pretrained model and uses it collect weights. optional arguments: -h, --help show this help message and exit -m MODEL_PATH, --model_path MODEL_PATH model to load -t TENSORS [TENSORS ...], --tensors TENSORS [TENSORS ...] tensors to collect -od OUTPUT_DIRECTORY, --output_directory OUTPUT_DIRECTORY directory that contains the results -dbg, --debug enables debugging mode -l {critical,error,warning,info,debug,notset}, --logging_level {critical,error,warning,info,debug,notset} the level of logging to use ``` The three most important arguments are `--model_path` where you have to specify the path of the model to load, `--tensors` that lets you specify a list of tensor names in the TensorFlow graph that contain the weights you want to collect, and finally `--output_directory` that lets you specify where the NPY files (one for each tensor name specified) will be saved. In order to figure out the names fo the tensors containing the weights you want to collect, the best way is to inspect the graph of the model with TensorBoard. ``` tensorboard --logdir /path/to/model/log ``` collect_activations ------------------- This command lets you load a pre-trained model and input data and collects the values of activations contained in tensors with a specific name in order to save them in a NPY format. This may be useful in order to visualize the activations (for instance collecting last layer's activations as embeddings representations of the input datapoint) and for some post-hoc analyses. You can call it with: ``` ludwig collect_activations [options] ``` or with ``` python -m ludwig.collect activations [options] ``` from within Ludwig's main directory. These are the available arguments: ``` usage: ludwig collect_activations [options] This script loads a pretrained model and uses it collect tensors for each datapoint in the dataset. optional arguments: -h, --help show this help message and exit --data_csv DATA_CSV input data CSV file --data_hdf5 DATA_HDF5 input data HDF5 file -s {training,validation,test,full}, --split {training,validation,test,full} the split to test the model on -m MODEL_PATH, --model_path MODEL_PATH model to load -t TENSORS [TENSORS ..], --tensors TENSORS [TENSORS ..] tensors to collect -od OUTPUT_DIRECTORY, --output_directory OUTPUT_DIRECTORY directory that contains the results -bs BATCH_SIZE, --batch_size BATCH_SIZE size of batches -g GPUS, --gpus GPUS list of gpu to use -gf GPU_FRACTION, --gpu_fraction GPU_FRACTION fraction of gpu memory to initialize the process with -dbg, --debug enables debugging mode -l {critical,error,warning,info,debug,notset}, --logging_level {critical,error,warning,info,debug,notset} the level of logging to use ``` The data related and runtime related arguments (GPUs, batch size, etc.) are the same used in [predict](#predict), you can refer to that section for an explanation. The collect specific arguments `--model_path`, `--tensors` and `--output_directory` are the same used in [collect_weights](#collect_weights), you can refer to that section for an explanation. In order to figure out the names fo the tensors containing the activations you want to collect, the best way is to inspect the graph of the model with TensorBoard. ``` tensorboard --logdir /path/to/model/log ``` Data Preprocessing ================== Ludwig data preprocessing maps raw data coming in CSV format into an HDF5 file containing tensors and a JSON file containing mappings from strings to tensors when needed. This mapping is performed when a CSV is provided as input and both HDF5 and JSON files are saved in the same directory as the input CSV, unless the argument `--skip_save_processed_input` is used (both in `train` and `experiment` commands). The reason to save those files is both to provide a cache and avoid performing the preprocessing again (as, depending on the type of features involved, it could be time consuming) and to provide the needed mappings to be able to map unseen data into tensors. The preprocessing process is personalizable to fit the specifics of your data format, but the basic assumption is always that your CSV files contains one row for each datapoint and one column for each feature (either input or output), and that you are able to determine the type of that column among the ones supported by Ludwig. The reason for that is that each data type is mapped into tensors in a different way and expects the content to be formatted in a specific way. Different datatypes may have different formatters that format the values of a cell. For instance the value of a cell of a sequence feature column by default is managed by a `space` formatter, that splits the content of the value into a list of strings using space. | before formatter | after formatter | |------------------------|--------------------------| | "token3 token4 token2" | [token3, token4, token2] | | "token3 token1" | [token3, token1] | Then a list `idx2str` and two dictionaries `str2idx` and `str2freq` are created containing all the tokens in all the lists obtained by splitting all the rows of the column and an integer id is assigned to each of them (in order of frequency). ```json { "column_name": { "idx2str": [ "", "", "token3", "token2", "token4", "token1" ], "str2idx": { "": 0, "": 1, "token3": 2, "token2": 3, "token4": 4, "token1": 5 }, "str2freq": { "": 0, "": 0, "token3": 2, "token2": 1, "token4": 1, "token1": 1 } } } ``` Finally a numpy matrix is created with sizes `n x l` where `n` is the number of rows in the column and `l` is the minimum of the longest tokenized list and a `max_lenght` parameter that can be set. All sequences shorter than `l` are padded on the right (but this behavior may also be modified through a parameter). | after formatter | numpy matrix | |--------------------------|--------------| | [token3, token4, token2] | 2 4 3 | | [token3, token1] | 2 5 0 | The final result matrix is saved in the HDF5 the name of the original column in the CSV as key, while the mapping from token to integer ID (and its inverse mapping) is saved in the JSON file. Each datatype is preprocessed in a different way, using different parameters and different formatters. Details on how to set those parameters for each feature type and for each specific feature will be described in the [Model Definition - Preprocessing](#preprocessing) section. `Binary` features are directly transformed into a binary valued vector of length `n` (where `n` is the size of the dataset) and added to HDF5 with a key that reflects the name of column in the CSV. No additional information about them is available in the JSON metadata file. `Numerical` features are directly transformed into a float valued vector of length `n` (where `n` is the size of the dataset) and added to HDF5 with a key that reflects the name of column in the CSV. No additional information about them is available in the JSON metadata file. `Category` features are transformed into an integer valued vector of size `n` (where `n` is the size of the dataset) and added to HDF5 with a key that reflects the name of column in the CSV. The way categories are mapped into integers consists in first collecting a dictionary of all the different category strings present in the column of the CSV, then rank them by frequency and then assign them an increasing integer ID from the most frequent to the most rare (with 0 being assigned to a `` token). The column name is added to the JSON file, with an associated dictionary containing 1. the mapping from integer to string (`idx2str`) 2. the mapping from string to id (`str2idx`) 3. the mapping from string to frequency (`str2freq`) 4. the size of the set of all tokens (`vocab_size`) 4. additional preprocessing information (by default how to fill missing values and what token to use to fill missing values) `Set` features are transformed into a binary (int8 actually) valued matrix of size `n x l` (where `n` is the size of the dataset and `l` is the minimum of the size of the biggest set and a `max_size` parameter) and added to HDF5 with a key that reflects the name of column in the CSV. The way sets are mapped into integers consists in first using a formatter to map from strings to sequences of set items (by default this is done by splitting on spaces). Then a a dictionary of all the different set item strings present in the column of the CSV is collected, then they are ranked by frequency and an increasing integer ID is assigned to them from the most frequent to the most rare (with 0 being assigned to `` used for padding and 1 assigned to `` item). The column name is added to the JSON file, with an associated dictionary containing 1. the mapping from integer to string (`idx2str`) 2. the mapping from string to id (`str2idx`) 3. the mapping from string to frequency (`str2freq`) 4. the maximum size of all sets (`max_set_size`) 5. additional preprocessing information (by default how to fill missing values and what token to use to fill missing values) `Bag` features are treated in the same way of set features, with the only difference being that the matrix had float values (frequencies). `Sequence` features are transformed into an integer valued matrix of size `n x l` (where `n` is the size of the dataset and `l` is the minimum of the length of the longest sequence and a `sequence_length_limit` parameter) and added to HDF5 with a key that reflects the name of column in the CSV. The way sets are mapped into integers consists in first using a formatter to map from strings to sequences of tokens (by default this is done by splitting on spaces). Then a a dictionary of all the different token strings present in the column of the CSV is collected, then they are ranked by frequency and an increasing integer ID is assigned to them from the most frequent to the most rare (with 0 being assigned to `` used for padding and 1 assigned to `` item). The column name is added to the JSON file, with an associated dictionary containing 1. the mapping from integer to string (`idx2str`) 2. the mapping from string to id (`str2idx`) 3. the mapping from string to frequency (`str2freq`) 4. the maximum length of all sequences (`sequence_length_limit`) 5. additional preprocessing information (by default how to fill missing values and what token to use to fill missing values) `Text` features are treated in the same way of sequence features, with a couple differences. Two different formatting/splitting happen, one that splits at every character and one that uses a spaCy based tokenizer (and removes stopwords) are used, and two different key are added to the HDF5 file, one containing the matrix of characters and one containing the matrix of words. The same thing happens in the JSON file, where there are dictionaries for mapping characters to integers (and the inverse) and words to integers (and their inverse). In the model definition you are able to specify which level of representation to use, if the character level or the word level. `Timeseries` features are treated in the same way of sequence features, with the only difference being that the matrix in the HDF5 file does not have integer values, but float values. Moreover, there is no need for any mapping in the JSON file. `Image` features are transformed into a int8 valued tensor of size `n x h x w x c` (where `n` is the size of the dataset and `h x w` is a specific resizing of the image that can be set, and `c` is the number of color channels) and added to HDF5 with a key that reflects the name of column in the CSV. The column name is added to the JSON file, with an associated dictionary containing preprocessing information about the sizes of the resizing. CSV Format ---------- Ludwig uses Pandas under the hood to read the CSV files. Pandas tries to automatically identify the separator (generally `','`) from the data. We are using `'\'` as the default escape character. For example, if `','` is the column separator and one of your data columns has a `','` in it, Pandas would fail to load the data properly. To handle such cases, we expect your data columns to be escaped with backslashes (replace `','` in the data with `'\\,'`) Data Postprocessing =================== The JSON file obtained from preprocessing is used also for postprocessing: Ludwig models return output predictions and, depending on their datatype they are mapped back into the original space. Numerical and timeseries are returned as they are, while category, set, sequence, and text features output integers, those integers are mapped back into the original tokens / names using the `idx2str` in the JSON file. When you run `experiment` or `predict` you will find both a CSV file for each output containing the mapped predictions, a probability CSV file containing the probability of that prediction, a probabilities CSV file containing the probabilities for all alternatives (for instance, the probabilities of all the categories in case of a categorical feature). You will also find the unmapped NPY files. If you don't need them you can use the `--skip_save_unprocessed_output` argument. Model Definition ================ The model definition is the core of Ludwig. It is a dictionary that contains all the information needed to build and train a Ludwig model. It mixes ease of use, by means of reasonable defaults, with flexibility, by means of detailed control over the parameters of your model. It is provided to both `experiment` and `train` commands either as a string (`--model_definition`) or as a file (`--model_definition_file`). The string or the content of the file will be parsed by PyYAML into a dictionary in memory, so any style of YAML accepted by the parser is considered to be valid, so both multiline and oneline formats are accepted. For instance a list of dictionaries can be written both as ```yaml mylist: [{name: item1, score: 2}, {name: item2, score: 1}, {name: item3, score: 4}] ``` or as: ```yaml mylist: - name: item1 score: 2 - name: item2 score: 1 - name: item3 score: 4 ``` The structure of the model definition file is a dictionary with five keys: ```yaml input_features: [] combiner: {} output_features: [] training: {} preprocessing: {} ``` Only `input_features` and `output_features` are required, the other three fields have default values, but you are free to modify them. Input features -------------- The `input_features` list contains a list of dictionaries, each of them containing two required fields `name` and `type`. `name` is the name of the feature and is the same name of the column of the CSV input file, `type` is one of the supported datatypes. Input features may have different ways to be encoded and the parameter to decide it is `encoder`. All the other parameters you specify in an input feature will be passed as parameters to the function that build the encoder, and each encoder can have different parameters. For instance a `sequence` feature can be encoded by a `stacked_cnn` or by and `rnn`, but only the `stacked_cnn` will accept the parameter `num_filters` while only the `rnn` will accept the parameter `bidirectional`. A list of all the encoders available for all the datatypes alongside with the description of all parameters will be provided in the datatype-specific sections. Some datatypes have only one type of encoder, so you are not required to specify it. The role of the encoders is to map inputs into tensors, usually vectors in the case of datatype without a temporal / sequential aspect, matrices in case there is a temporal / sequential aspect or higher rank tensors in case there is a spatial or a spatio-temporal aspect to the input data. Different configurations of the same encoder may return a tensor with different rank, for instance a sequential encoder may return a vector of size `h` that is either the final vector of a sequence or the result of pooling over the sequence length, or it can return a matrix of size `l x h` where `l` is the length of the sequence and `h` is the hidden dimension if you specify the pooling reduce operation (`reduce_output`) to be `null`. For the sake of simplicity you can imagine the output to be a vector in most of the cases, but there is a `reduce_output` parameter one can specify to change the default behavior. An additional feature that ludwig provides is the option to have tied weights between different encoders. For instance if my model takes two sentences as input and return the probability of their entailment, I may want to encode both sentences with the same encoder. The way to do it is by specifying the `tied-weights` parameter of the second feature you define to be the name of the first feature you defined. ```yaml input_features: - name: sentence1 type: text - name: sentence2 type: text tied_weights: sentence1 ``` If you specify a name of an input feature that has not been defined yet, it will result in an error. Also, in order to be able to have tied weights, all encoder parameters have to be identical between the two input features. Combiner -------- Combiners are part of the model that take all the outputs of the different input features and combine them in a single representation that is passed to the outputs. You can specify which one to use in the `combiner` section of the model definition. Different combiners implement different combination logic, but the default one `concat` just concatenates all outputs of input feature encoders and optionally passes the concatenation through fully connected layers, with the output of the last layer being forwarded to the outputs decoders. ``` +-----------+ |Input | |Feature 1 +-+ +-----------+ | +---------+ +-----------+ | +------+ |Fully | |... +--->Concat+--->Connected+-> +-----------+ | +------+ |Layers | +-----------+ | +---------+ |Input +-+ |Feature N | +-----------+ ``` For the sake of simplicity you can imagine the both inputs and outputs are vectors in most of the cases, but there are `reduce_input` and `reduce_output` parameters to specify to change the default behavior. Output Features --------------- The `output_features` list has the same structure of the `input_features` list: it is a list of dictionaries containing a `name` and a `type`. They represent outputs / targets that you want your model to predict. In most machine learning tasks you want to predict only one target variable, but in Ludwig you are allowed to specify as many outputs as you want and they are going to be optimized in a multi-task fashion, using a weighted sum of their losses as a combined loss to optimize. Instead of having `encoders`, output features have `decoders`, but most of them have only one decoder so you don't have to specify it. Decoders take the output of the combiner as input, process it further, for instance passing it through fully connected layers, and finally predict values and compute a loss and some measures (depending on the datatype different losses and measures apply). Decoders have additional parameters, in particular `loss` that allows you to specify a different loss to optimize for this specific decoder, for instance numerical features support both `mean_squared_error` and `mean_absolute_error` as losses. Details about the available decoders and losses alongside with the description of all parameters will be provided in the datatype-specific sections. For the sake of simplicity you can imagine the input coming from the combiner to be a vector in most of the cases, but there is a `reduce_input` parameter one can specify to change the default behavior. Output Features Dependencies ---------------------------- An additional feature that Ludwig provides is the concept of dependency between `output_features`. You can specify a list of output features as dependencies when you write the dictionary of a specific feature. At model building time Ludwig checks that no cyclic dependency exists. If you do so Ludwig will concatenate all the final representations before the prediction of those output features to the original input of the decoder. The reason is that if different output features have a causal dependency, knowing which prediction has been made for one can help making the prediction of the other. For instance if two output features are one coarse grained category and one fine-grained category that are in a hierarchical structure with each other, knowing the prediction made for coarse grained restricts the possible categories to predict for the fine-grained. In this case the following model definition structure can be used: ```yaml output_features: - name: coarse_class type: category num_fc_layers: 2 fc_size: 64 - name: fine_class type: category dependencies: - coarse_class num_fc_layers: 1 fc_size: 64 ``` Assuming the input coming from the combiner has hidden dimension `h` 128, there are two fully connected layers that return a vector with hidden size 64 at the end of the `coarse_class` decoder (that vector will be used for the final layer before projecting in the output `coarse_class` space) In the decoder of `fine_class`, the 64 dimensional vector of `coarse_class` will be concatenated to the combiner output vector, making a vector of hidden size 192 that will be passed through a fully connected layer and the 64 dimensional output will be used for the final layer before projecting in the output class space of the `fine_class`. Training -------- The `training` section of the model definition lets you specify some parameters of the training process, like for instance the number of epochs or the learning rate. These are the available training parameters: - `batch_size` (default `128`): size of the batch used for training the model. - `eval_batch_size` (default `0`): size of the batch used for evaluating the model. If it is `0`, the same value of `batch_size` is used. This is usefult to speedup evaluation with a much bigger batch size than training, if enough memory is available, or to decrease the batch size when `sampled_softmax_cross_entropy` is used as loss for sequential and categorical features with big vocabulary sizes (evaluation needs to be performed on the full vocabulary, so a much smaller batch size may be needed to fit the activation tensors in memory). - `epochs` (default `100`): number of epochs the training process will run for. - `early_stop` (default `5`): if there's a validation set, number of epochs of patience without an improvement on the validation measure before the training is stopped. - `optimizer` (default `{type: adam, beta1: 0.9, beta2: 0.999, epsilon: 1e-08}`): which optimizer to use with the relative parameters. The available optimizers are: `sgd` (or `stochastic_gradient_descent`, `gd`, `gradient_descent`, they are all the same), `adam`, `adadelta`, `adagrad`, `adagradda`, `momentum`, `ftrl`, `proximalgd`, `proximaladagrad`, `rmsprop`. To know their parameters check [TensorFlow's optimizer documentation](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers). - `learning_rate` (default `0.001`): the learning rate to use. - `decay` (default `false`): if to use exponential decay of the learning rate or not. - `decay_rate` (default `0.96`): the rate of the exponential learning rate decay. - `decay_steps` (default `10000`): the number of steps of the exponential learning rate decay. - `staircase` (default `false`): decays the learning rate at discrete intervals. - `regularization_lambda` (default `0`): the lambda parameter used for adding a l2 regularization loss to the overall loss. - `dropout_rate` (default `0.0`): the probability to drop neurons in dropout. The `dropout_rate` is used throughout the whole model, but to decide which parts of the model will use it, use the `dropout` boolean parameter available in each encoder, combiner and decoder. - `reduce_learning_rate_on_plateau` (default `0`): if there's a validation set, how many times to reduce the learning rate when a plateau of validation measure is reached. - `reduce_learning_rate_on_plateau_patience` (default `5`): if there's a validation set, number of epochs of patience without an improvement on the validation measure before reducing the learning rate. - `reduce_learning_rate_on_plateau_rate` (default `0.5`): if there's a validation set, the reduction rate of the learning rate. - `increase_batch_size_on_plateau` (default `0`): if there's a validation set, how many times to increase the batch size when a plateau of validation measure is reached. - `increase_batch_size_on_plateau_patience` (default `5`): if there's a validation set, number of epochs of patience without an improvement on the validation measure before increasing the learning rate. - `increase_batch_size_on_plateau_rate` (default `2`): if there's a validation set, the increase rate of the batch size. - `increase_batch_size_on_plateau_max` (default `512`): if there's a validation set, the maximum value of batch size. - `validation_field` (default `combined`): when there is more than one output feature, which one to use for computing if there was an improvement on validation. The measure to use to determine if there was an improvement can be set with the `validation_measure` parameter. Different datatypes have different available measures, refer to the datatype-specific section for more details. `combined` indicates the use the combination of all features. For instance the combination of `combined` and `loss` as measure uses a decrease in the combined loss of all output features to check for improvement on validation, while `combined` and `accuracy` considers on how many datapoints the predictions for all output features were correct (but consider that for some features, for instance `numeric` there is no accuracy measure, so you should use `accuracy` only if all your output features have an accuracy measure). - `validation_measure:` (default `accuracy`): the measure to use to determine if there was an improvement. The measure is considered for the output feature specified in `validation_field`. Different datatypes have different available measures, refer to the datatype-specific section for more details. - `bucketing_field` (default `null`): when not `null`, when creating batches, instead of shuffling randomly, the length along the last dimension of the matrix of the specified input feature is used for bucketing datapoints and then randomly shuffled datapoints from the same bin are sampled. Padding is trimmed to the longest datapoint in the batch. The specified feature should be either a `sequence` or `text` feature and the encoder encoding it has to be `rnn`. When used, bucketing improves speed of `rnn` encoding up to 1.5x, depending on the length distribution of the inputs. Preprocessing ------------- The `preprocessing` section of the model definition makes it possible to specify datatype specific parameters to perform data preprocessing. The preprocessing dictionary contains one key of each datatype, but you have to specify only the ones that apply to your case, the other ones will be kept as defaults. Moreover, the preprocessing dictionary contains parameters related to how to split the data that are not feature specific. - `force_split` (default `false`): if `true` the `split` column in the CSV data file is ignored and the dataset is randomly split. If `false` the `split` column is used if available. - `split_probabilities` (default `[0.7, 0.1, 0.2]`): the proportion of the CSV data to end up in training, validation and test. The three values have to sum up to one. - `stratify` (default `null`): if `null` the split is random, otherwise you can specify the name of a `category` feature and the split will be stratified on that feature. Example preprocessing dictionary (showing default values): ```yaml preprocessing: force_split: false split_probabilities: [0.7, 0.1, 0.2] stratify: null category: {...} sequence: {...} text: {...} ... ``` The details about the preprocessing parameters that each datatype accepts will be provided in the datatype-specific sections. It is important to point out that different features within the same datatype may require different preprocessing. For instance a document classification model may have two text input features, one for the title of the document and one for the body. As the length of the title is much shorter than the length of the body, the parameter `word_length_limit` should be set to 10 for the title and 2000 for the body, but both of them share the same parameter `most_common_words` with value 10000. The way to do this is adding a `preprocessing` key inside the title `input_feature` dictionary and one in the `body` input feature dictionary containing the desired parameter and value. The model definition will look like: ```yaml preprocessing: text: most_common_word: 10000 input_features: - name: title type: text preprocessing: word_length_limit: 20 - name: body type: text preprocessing: word_length_limit: 2000 ``` Binary Features --------------- ### Binary Features Preprocessing Binary features are directly transformed into a binary valued vector of length `n` (where `n` is the size of the dataset) and added to HDF5 with a key that reflects the name of column in the CSV. No additional information about them is available in the JSON metadata file. The parameters available for preprocessing are - `missing_value_strategy` (default `fill_with_const`): what strategy to follow when there's a missing value in a binary column. The value should be one of `fill_with_const` (replaces the missing value with a specific value specified with the `fill_value` parameter), `fill_with_mode` (replaces the missing values with the most frequent value in the column), `fill_with_mean` (replaces the missing values with the mean of the values in the column), `backfill` (replaces the missing values with the next valid value). - `fill_value` (default `0`): the value to replace the missing values with in case the `missing_value_strategy` is `fill-value`. ### Binary Input Features and Encoders Binary features have no encoder, the raw binary values coming from the input placeholders are just returned as outputs. By consequence there are no encoding parameters. Inputs are of size `b` while outputs are of size `b x 1` where `b` is the batch size. Example binary feature entry in the output features list: ```yaml name: binary_csv_column_name type: binary ``` ### Binary Output Features and Decoders Binary features can be used when a binary classification needs to be performed or when the output is a single probability. There is only one decoder available for binary features and it is a (potentially empty) stack of fully connected layers, followed by a projection into a single number followed by a sigmoid function. These are the available parameters of a binary output feature - `reduce_inputs` (default `sum`): defines how to reduce an input that is not a vector, but a matrix or a higher order tensor, on the first dimension 9second if you count the batch dimension). Available values are: `sum`, `mean` or `avg`, `max`, `concat` (concatenates along the first dimension), `last` (returns the last vector of the first dimension). - `dependencies` (default `[]`): the output features this one is dependent on. For a detailed explanation refer to [Output Features Dependencies](#output-features-dependencies). - `reduce_dependencies` (default `sum`): defines how to reduce the output of a dependent feature that is not a vector, but a matrix or a higher order tensor, on the first dimension 9second if you count the batch dimension). Available values are: `sum`, `mean` or `avg`, `max`, `concat` (concatenates along the first dimension), `last` (returns the last vector of the first dimension). - `loss` (default `{type: cross_entropy, confidence_penalty: 0, robust_lambda: 0}`): is a dictionary containing a loss `type` and its hyperparameters. The only available loss `type` is `cross_entropy` (cross entropy), and the two optional parameters are `confidence_penalty` (an additional term that penalizes too confident predictions by adding a `a * (max_entropy - entropy) / max_entropy` term to the loss, where a is the value of this parameter) and `robust_lambda` (replaces the loss with `(1 - robust_lambda) * loss + robust_lambda / 2` which is useful in case of noisy labels). These are the available parameters of a binary output feature decoder - `fc_layers` (default `null`): it is a list of dictionaries containing the parameters of all the fully connected layers. The length of the list determines the number of stacked fully connected layers and the content of each dictionary determines the parameters for a specific layer. The available parameters for each layer are: `fc_size`, `norm`, `activation`, `dropout`, `initializer` and `regularize`. If any of those values is missing from the dictionary, the default one specified as a parameter of the decoder will be used instead. - `num_fc_layers` (default 0): this is the number of stacked fully connected layers that the input to the feature passes through. Their output is projected in the feature's output space. - `fc_size` (default `256`): if a `fc_size` is not already specified in `fc_layers` this is the default `fc_size` that will be used for each layer. It indicates the size of the output of a fully connected layer. - `activation` (default `relu`): if an `activation` is not already specified in `fc_layers` this is the default `activation` that will be used for each layer. It indicates the activation function applied to the output. - `norm` (default `null`): if a `norm` is not already specified in `fc_layers` this is the default `norm` that will be used for each layer. It indicates the norm of the output and it can be `null`, `batch` or `layer`. - `dropout` (default `false`): determines if there should be a dropout layer after each layer. - `initializer` (default `null`): the initializer to use. If `null`, the default initialized of each variable is used (`glorot_uniform` in most cases). Options are: `constant`, `identity`, `zeros`, `ones`, `orthogonal`, `normal`, `uniform`, `truncated_normal`, `variance_scaling`, `glorot_normal`, `glorot_uniform`, `xavier_normal`, `xavier_uniform`, `he_normal`, `he_uniform`, `lecun_normal`, `lecun_uniform`. Alternatively it is possible to specify a dictionary with a key `type` that identifies the type of initializer and other keys for its parameters, e.g. `{type: normal, mean: 0, stddev: 0}`. To know the parameters of each initializer, please refer to [TensorFlow's documentation](https://www.tensorflow.org/api_docs/python/tf/keras/initializers). - `regularize` (default `true`): if `true` the wights of the layers are added to the set of weights that get regularized by a regularization loss (if the `regularization_lambda` in `training` is greater than 0). - `threshold` (default `0.5`): The threshold above (greater or equal) which the predicted output of the sigmoid will be mapped to 1. Example binary feature entry (with default parameters) in the output features list: ```yaml name: binary_csv_column_name type: binary reduce_inputs: sum dependencies: [] reduce_dependencies: sum loss: type: cross_entropy confidence_penalty: 0 robust_lambda: 0 fc_layers: null num_fc_layers: 0 fc_size: 256 activation: relu norm: null dropout: false initializer: null regularize: true threshold: 0.5 ``` ### Binary Features Measures The only measures that are calculated every epoch and are available for binary features are the `accuracy` and the `loss` itself. You can set either of them as `validation_measure` in the `training` section of the model definition if you set the `validation_field` to be the name of a binary feature. Numerical Features ------------------ ### Numerical Features Preprocessing Numerical features are directly transformed into a float valued vector of length `n` (where `n` is the size of the dataset) and added to HDF5 with a key that reflects the name of column in the CSV. No additional information about them is available in the JSON metadata file. Parameters available for preprocessing are - `missing_value_strategy` (default `fill_with_const`): what strategy to follow when there's a missing value in a binary column. The value should be one of `fill_with_const` (replaces the missing value with a specific value specified with the `fill_value` parameter), `fill_with_mode` (replaces the missing values with the most frequent value in the column), `fill_with_mean` (replaces the missing values with the mean of the values in the column), `backfill` (replaces the missing values with the next valid value). - `fill_value` (default `0`): the value to replace the missing values with in case the `missing_value_strategy` is `fill-value`. ### Numerical Input Features and Encoders Numerical features have one encoder, the raw float values coming from the input placeholders are passed through a single neuron for scaling purposes, (optionally) passed through a normalization layer (either `null`, `batch_norm`, or `layer_norm`) and returned as outputs. Inputs are of size `b` while outputs are fo size `b x 1` where b is the batch size. The available encoder parameters are: - `norm'` (default `null`): norm to apply after the single neuron. It can be `null`, `batch` or `layer`. - `tied_weights` (default `null`): name of the input feature to tie the weights the encoder with. It needs to be the name of a feature of the same type and with the same encoder parameters. Example binary feature entry in the output features list: ```yaml name: numerical_csv_column_name type: numerical norm: null tied_weights: null ``` ### Numerical Output Features and Decoders Numerical features can be used when a regression needs to be performed. There is only one decoder available for numerical features and it is a (potentially empty) stack of fully connected layers, followed by a projection into a single number. These are the available parameters of a numerical output feature - `reduce_inputs` (default `sum`): defines how to reduce an input that is not a vector, but a matrix or a higher order tensor, on the first dimension 9second if you count the batch dimension). Available values are: `sum`, `mean` or `avg`, `max`, `concat` (concatenates along the first dimension), `last` (returns the last vector of the first dimension). - `dependencies` (default `[]`): the output features this one is dependent on. For a detailed explanation refer to [Output Features Dependencies](#output-features-dependencies). - `reduce_dependencies` (default `sum`): defines how to reduce the output of a dependent feature that is not a vector, but a matrix or a higher order tensor, on the first dimension 9second if you count the batch dimension). Available values are: `sum`, `mean` or `avg`, `max`, `concat` (concatenates along the first dimension), `last` (returns the last vector of the first dimension). - `loss` (default `{type: mean_squared_error}`): is a dictionary containing a loss `type`. The available losses `type` are `mean_squared_error` and `mean_absolute_error`. These are the available parameters of a numerical output feature decoder - `fc_layers` (default `null`): it is a list of dictionaries containing the parameters of all the fully connected layers. The length of the list determines the number of stacked fully connected layers and the content of each dictionary determines the parameters for a specific layer. The available parameters for each layer are: `fc_size`, `norm`, `activation`, `dropout`, `initializer` and `regularize`. If any of those values is missing from the dictionary, the default one specified as a parameter of the decoder will be used instead. - `num_fc_layers` (default 0): this is the number of stacked fully connected layers that the input to the feature passes through. Their output is projected in the feature's output space. - `fc_size` (default `256`): if a `fc_size` is not already specified in `fc_layers` this is the default `fc_size` that will be used for each layer. It indicates the size of the output of a fully connected layer. - `activation` (default `relu`): if an `activation` is not already specified in `fc_layers` this is the default `activation` that will be used for each layer. It indicates the activation function applied to the output. - `norm` (default `null`): if a `norm` is not already specified in `fc_layers` this is the default `norm` that will be used for each layer. It indicates the norm of the output and it can be `null`, `batch` or `layer`. - `dropout` (default `false`): determines if there should be a dropout layer after each layer. - `initializer` (default `null`): the initializer to use. If `null`, the default initialized of each variable is used (`glorot_uniform` in most cases). Options are: `constant`, `identity`, `zeros`, `ones`, `orthogonal`, `normal`, `uniform`, `truncated_normal`, `variance_scaling`, `glorot_normal`, `glorot_uniform`, `xavier_normal`, `xavier_uniform`, `he_normal`, `he_uniform`, `lecun_normal`, `lecun_uniform`. Alternatively it is possible to specify a dictionary with a key `type` that identifies the type of initializer and other keys for its parameters, e.g. `{type: normal, mean: 0, stddev: 0}`. To know the parameters of each initializer, please refer to [TensorFlow's documentation](https://www.tensorflow.org/api_docs/python/tf/keras/initializers). - `regularize` (default `true`): if `true` the weights of the layers are added to the set of weights that get regularized by a regularization loss (if the `regularization_lambda` in `training` is greater than 0). - `clip` (default `null`): If not `null` it specifies a minimum and maximum value the predictions will be clipped to. The value can be either a list or a tuple of length 2, with the first value representing the minimum and the second the maximum. For instance `(-5,5)` will make it so that all predictions will be clipped in the `[-5,5]` interval. Example numerical feature entry (with default parameters) in the output features list: ```yaml name: numerical_csv_column_name type: numerical reduce_inputs: sum dependencies: [] reduce_dependencies: sum loss: type: mean_squared_error fc_layers: null num_fc_layers: 0 fc_size: 256 activation: relu norm: null dropout: false initializer: null regularize: true ``` ### Numerical Features Measures The measures that are calculated every epoch and are available for numerical features are `mean_squared_error`, `mean_absolute_error`, `r2` and the `loss` itself. You can set either of them as `validation_measure` in the `training` section of the model definition if you set the `validation_field` to be the name of a binary feature. Category Features ----------------- ### Category Features Preprocessing Category features are transformed into an integer valued vector of size `n` (where `n` is the size of the dataset) and added to HDF5 with a key that reflects the name of column in the CSV. The way categories are mapped into integers consists in first collecting a dictionary of all the different category strings present in the column of the CSV, then rank them by frequency and then assign them an increasing integer ID from the most frequent to the most rare (with 0 being assigned to a `` token). The column name is added to the JSON file, with an associated dictionary containing 1. the mapping from integer to string (`idx2str`) 2. the mapping from string to id (`str2idx`) 3. the mapping from string to frequency (`str2freq`) 4. the size of the set of all tokens (`vocab_size`) 4. additional preprocessing information (by default how to fill missing values and what token to use to fill missing values) The parameters available for preprocessing are - `missing_value_strategy` (default `fill_with_const`): what strategy to follow when there's a missing value in a binary column. The value should be one of `fill_with_const` (replaces the missing value with a specific value specified with the `fill_value` parameter), `fill_with_mode` (replaces the missing values with the most frequent value in the column), `fill_with_mean` (replaces the missing values with the mean of the values in the column), `backfill` (replaces the missing values with the next valid value). - `fill_value` (default `""`): the value to replace the missing values with in case the `missing_value_strategy` is `fill-value`. - `lowercase` (default `false`): if the string has to be lowercased before being handled by the formatter. - `most_common` (default `10000`): the maximum number of most common tokens to be considered. if the data contains more than this amount, the most infrequent tokens will be treated as unknown. ### Category Input Features and Encoders Category features have one encoder, the raw integer values coming from the input placeholders are mapped to either dense or sparse embeddings (one-hot encodings) and returned as outputs. Inputs are of size `b` while outputs are fo size `b x h` where `b` is the batch size and `h` is the dimensionality of the embeddings. The available encoder parameters are - `representation'` (default `dense`): the possible values are `dense` and `sparse`. `dense` means the embeddings are initialized randomly, `sparse` means they are initialized to be one-hot encodings. - `embedding_size` (default `256`): it is the maximum embedding size, the actual size will be `min(vocabulary_size, embedding_size)` for `dense` representations and exactly `vocabulary_size` for the `sparse` encoding, where `vocabulary_size` is the number of different strings appearing in the training set in the column the feature is named after (plus 1 for ``). - `embeddings_on_cpu` (default `false`): by default embeddings matrices are stored on GPU memory if a GPU is used, as it allows for faster access, but in some cases the embedding matrix may be really big and this parameter forces the placement of the embedding matrix in regular memory and the CPU is used to resolve them, slightly slowing down the process as a result of data transfer between CPU and GPU memory. - `pretrained_embeddings` (default `null`): by default `dense` embeddings are initialized randomly, but this parameter allow to specify a path to a file containing embeddings in the [GloVe format](https://nlp.stanford.edu/projects/glove/). When the file containing the embeddings is loaded, only the embeddings with labels present in the vocabulary are kept, the others are discarded. If the vocabulary contains strings that have no match in the embeddings file, their embeddings are initialized with the average of all other embedding plus some random noise to make them different from each other. This parameter has effect only if `representation` is `dense`. - `embeddings_trainable` (default `true`): If `true` embeddings are trained during the training process, if `false` embeddings are fixed. It may be useful when loading pretrained embeddings for avoiding finetuning them. This parameter has effect only for `representation` is `dense` as `sparse` one-hot encodings are not trainable. - `dropout` (default `false`): determines if there should be a dropout layer after embedding. - `initializer` (default `null`): the initializer to use. If `null`, the default initialized of each variable is used (`glorot_uniform` in most cases). Options are: `constant`, `identity`, `zeros`, `ones`, `orthogonal`, `normal`, `uniform`, `truncated_normal`, `variance_scaling`, `glorot_normal`, `glorot_uniform`, `xavier_normal`, `xavier_uniform`, `he_normal`, `he_uniform`, `lecun_normal`, `lecun_uniform`. Alternatively it is possible to specify a dictionary with a key `type` that identifies the type of initializer and other keys for its parameters, e.g. `{type: normal, mean: 0, stddev: 0}`. To know the parameters of each initializer, please refer to [TensorFlow's documentation](https://www.tensorflow.org/api_docs/python/tf/keras/initializers). - `regularize` (default `true`): if `true` the embedding weights are added to the set of weights that get regularized by a regularization loss (if the `regularization_lambda` in `training` is greater than 0). - `tied_weights` (default `null`): name of the input feature to tie the weights the encoder with. It needs to be the name of a feature of the same type and with the same encoder parameters. Example category feature entry in the input features list: ```yaml name: category_csv_column_name type: category representation: dense embedding_size: 256 embeddings_on_cpu: false pretrained_embeddings: null embeddings_trainable: true dropout: false initializer: null regularize: true tied_weights: null ``` ### Category Output Features and Decoders Category features can be used when a multi-class classification needs to be performed. There is only one decoder available for category features and it is a (potentially empty) stack of fully connected layers, followed by a projection into a vector of size of the number of available classes, followed by a softmax. ``` +--------------+ +---------+ +-----------+ |Combiner | |Fully | |Projection | +-------+ |Output +--->Connected+--->into Output+--->Softmax| |Representation| |Layers | |Space | +-------+ +--------------+ +---------+ +-----------+ ``` These are the available parameters of a category output feature - `reduce_inputs` (default `sum`): defines how to reduce an input that is not a vector, but a matrix or a higher order tensor, on the first dimension 9second if you count the batch dimension). Available values are: `sum`, `mean` or `avg`, `max`, `concat` (concatenates along the first dimension), `last` (returns the last vector of the first dimension). - `dependencies` (default `[]`): the output features this one is dependent on. For a detailed explanation refer to [Output Features Dependencies](#output-features-dependencies). - `reduce_dependencies` (default `sum`): defines how to reduce the output of a dependent feature that is not a vector, but a matrix or a higher order tensor, on the first dimension 9second if you count the batch dimension). Available values are: `sum`, `mean` or `avg`, `max`, `concat` (concatenates along the first dimension), `last` (returns the last vector of the first dimension). - `loss` (default `{type: softmax_cross_entropy, class_similarities_temperature: 0, class_weights: 1, confidence_penalty: 0, distortion: 1, labels_smoothing: 0, negative_samples: 0, robust_lambda: 0, sampler: null, unique: false}`): is a dictionary containing a loss `type`. The available losses `type` are `softmax_cross_entropy` and `sampled_softmax_cross_entropy`. These are the `loss` parameters - `confidence_penalty` (default `0`): penalizes overconfident predictions (low entropy) by adding an additional term that penalizes too confident predictions by adding a `a * (max_entropy - entropy) / max_entropy` term to the loss, where a is the value of this parameter. Useful in case of noisy labels. - `robust_lambda` (default `0`): replaces the loss with `(1 - robust_lambda) * loss + robust_lambda / c` where `c` is the number of classes, which is useful in case of noisy labels. - `class_weights` (default `1`): the value can be a vector of weights, one of each class, that is multiplied to the loss of the datapoints that have that class as ground truth. It is an alternative to oversampling in case of unbalanced class distribution. The ordering of the vector follows the category to integer ID mapping in the JSON metadata file (the `` class needs to be included too). - `class_similarities` (default `null`): if not `null` it is a `c x c` matrix in the form of a list of lists that contains the mutual similarity of classes. It is used if `class_similarities_temperature` is greater than 0. The ordering of the vector follows the category to integer ID mapping in the JSON metadata file (the `` class needs to be included too). - `class_similarities_temperature` (default `0`): is the temperature parameter of the softmax that is performed on each row of `class_similarities`. The output of that softmax is used to determine the supervision vector to provide instead of the one hot vector that would be provided otherwise for each datapoint. The intuition behind it is that errors between similar classes are more tollerable than errors between really different classes. - `labels_smoothing` (default `0`): If label_smoothing is nonzero, smooth the labels towards `1/num_classes`: `new_onehot_labels = onehot_labels * (1 - label_smoothing) + label_smoothing / num_classes`. - `negative_samples` (default `0`): if `type` is `sampled_softmax_cross_entropy`, this parameter indicates how many negative samples to use. - `sampler` (default `null`): options are `fixed_unigram`, `uniform`, `log_uniform`, `learned_unigram`. For a detailed description of the samplers refer to [TensorFlow's documentation](https://www.tensorflow.org/api_guides/python/nn#Candidate_Sampling). - `distortion` (default `1`): when `loss` is `sampled_softmax_cross_entropy` and the sampler is either `unigram` or `learned_unigram` this is used to skew the unigram probability distribution. Each weight is first raised to the distortion's power before adding to the internal unigram distribution. As a result, distortion = 1.0 gives regular unigram sampling (as defined by the vocab file), and distortion = 0.0 gives a uniform distribution. - `unique` (default `false`): Determines whether all sampled classes in a batch are unique. These are the available parameters of a category output feature decoder - `fc_layers` (default `null`): it is a list of dictionaries containing the parameters of all the fully connected layers. The length of the list determines the number of stacked fully connected layers and the content of each dictionary determines the parameters for a specific layer. The available parameters for each layer are: `fc_size`, `norm`, `activation`, `dropout`, `initializer` and `regularize`. If any of those values is missing from the dictionary, the default one specified as a parameter of the decoder will be used instead. - `num_fc_layers` (default 0): this is the number of stacked fully connected layers that the input to the feature passes through. Their output is projected in the feature's output space. - `fc_size` (default `256`): if a `fc_size` is not already specified in `fc_layers` this is the default `fc_size` that will be used for each layer. It indicates the size of the output of a fully connected layer. - `activation` (default `relu`): if an `activation` is not already specified in `fc_layers` this is the default `activation` that will be used for each layer. It indicates the activation function applied to the output. - `norm` (default `null`): if a `norm` is not already specified in `fc_layers` this is the default `norm` that will be used for each layer. It indicates the norm of the output and it can be `null`, `batch` or `layer`. - `dropout` (default `false`): determines if there should be a dropout layer after each layer. - `initializer` (default `null`): the initializer to use. If `null`, the default initialized of each variable is used (`glorot_uniform` in most cases). Options are: `constant`, `identity`, `zeros`, `ones`, `orthogonal`, `normal`, `uniform`, `truncated_normal`, `variance_scaling`, `glorot_normal`, `glorot_uniform`, `xavier_normal`, `xavier_uniform`, `he_normal`, `he_uniform`, `lecun_normal`, `lecun_uniform`. Alternatively it is possible to specify a dictionary with a key `type` that identifies the type of initializer and other keys for its parameters, e.g. `{type: normal, mean: 0, stddev: 0}`. To know the parameters of each initializer, please refer to [TensorFlow's documentation](https://www.tensorflow.org/api_docs/python/tf/keras/initializers). - `regularize` (default `true`): if `true` the weights of the layers are added to the set of weights that get regularized by a regularization loss (if the `regularization_lambda` in `training` is greater than 0). - `top_k` (default `3`): determines the parameter `k`, the number of categories to consider when computing the `top_k` measure. It computes accuracy but considering as a match if the true category appears in the first `k` predicted categories ranked by decoder's confidence. Example category feature entry (with default parameters) in the output features list: ```yaml name: category_csv_column_name type: category reduce_inputs: sum dependencies: [] reduce_dependencies: sum loss: type: softmax_cross_entropy confidence_penalty: 0 robust_lambda: 0 class_weights: 1 class_similarities: null class_similarities_temperature: 0 labels_smoothing: 0 negative_samples: 0 sampler: null distortion: 1 unique: false fc_layers: null num_fc_layers: 0 fc_size: 256 activation: relu norm: null dropout: false initializer: null regularize: true top_k: 3 ``` ### Category Features Measures The measures that are calculated every epoch and are available for category features are `accuracy`, `top_k` (computes accuracy considering as a match if the true category appears in the first `k` predicted categories ranked by decoder's confidence) and the `loss` itself. You can set either of them as `validation_measure` in the `training` section of the model definition if you set the `validation_field` to be the name of a binary feature. Set Features ------------ ### Set Features Preprocessing Set features are transformed into a binary (int8 actually) valued matrix of size `n x l` (where `n` is the size of the dataset and `l` is the minimum of the size of the biggest set and a `max_size` parameter) and added to HDF5 with a key that reflects the name of column in the CSV. The way sets are mapped into integers consists in first using a formatter to map from strings to sequences of set items (by default this is done by splitting on spaces). Then a a dictionary of all the different set item strings present in the column of the CSV is collected, then they are ranked by frequency and an increasing integer ID is assigned to them from the most frequent to the most rare (with 0 being assigned to `` used for padding and 1 assigned to `` item). The column name is added to the JSON file, with an associated dictionary containing 1. the mapping from integer to string (`idx2str`) 2. the mapping from string to id (`str2idx`) 3. the mapping from string to frequency (`str2freq`) 4. the maximum size of all sets (`max_set_size`) 5. additional preprocessing information (by default how to fill missing values and what token to use to fill missing values) The parameters available for preprocessing arehe parameters available for preprocessing are - `missing_value_strategy` (default `fill_with_const`): what strategy to follow when there's a missing value in a binary column. The value should be one of `fill_with_const` (replaces the missing value with a specific value specified with the `fill_value` parameter), `fill_with_mode` (replaces the missing values with the most frequent value in the column), `fill_with_mean` (replaces the missing values with the mean of the values in the column), `backfill` (replaces the missing values with the next valid value). - `fill_value` (default `0`): the value to replace the missing values with in case the `missing_value_strategy` is `fill-value`. - `format` (default `space`): defines how to map from the raw string content of the CSV column to a set of elements. The default value `space` splits the string on spaces. Other options are: `underscore` (splits on underscore), `comma`(splits on comma), `json` (decodes the string into a set or a list through a JSON parser). - `lowercase` (default `false`): if the string has to be lowercased before being handled by the formatter. - `most_common` (default `10000`): the maximum number of most common tokens to be considered. if the data contains more than this amount, the most infrequent tokens will be treated as unknown. ### Set Input Features and Encoders Set features have one encoder, the raw binary values coming from the input placeholders are first transformed in sparse integer lists, then they are mapped to either dense or sparse embeddings (one-hot encodings), finally they are aggregated and returned as outputs. Inputs are of size `b` while outputs are fo size `b x h` where `b` is the batch size and `h` is the dimensionally of the embeddings. ``` +-+ |0| +-----+ |0| +-+ |emb 2| +-----------+ |1| |2| +-----+ |Aggregation| |0+--->4+---->emb 4+--->Reduce +-> |1| |5| +-----+ |Operation | |1| +-+ |emb 5| +-----------+ |0| +-----+ +-+ ``` The available encoder parameters are - `representation'` (default `dense`): the possible values are `dense` and `sparse`. `dense` means the embeddings are initialized randomly, `sparse` means they are initialized to be one-hot encodings. - `embedding_size` (default `50`): it is the maximum embedding size, the actual size will be `min(vocabulary_size, embedding_size)` for `dense` representations and exactly `vocabulary_size` for the `sparse` encoding, where `vocabulary_size` is the number of different strings appearing in the training set in the column the feature is named after (plus 1 for ``). - `embeddings_on_cpu` (default `false`): by default embeddings matrices are stored on GPU memory if a GPU is used, as it allows for faster access, but in some cases the embedding matrix may be really big and this parameter forces the placement of the embedding matrix in regular memory and the CPU is used to resolve them, slightly slowing down the process as a result of data transfer between CPU and GPU memory. - `pretrained_embeddings` (default `null`): by default `dense` embeddings are initialized randomly, but this parameter allow to specify a path to a file containing embeddings in the [GloVe format](https://nlp.stanford.edu/projects/glove/). When the file containing the embeddings is loaded, only the embeddings with labels present in the vocabulary are kept, the others are discarded. If the vocabulary contains strings that have no match in the embeddings file, their embeddings are initialized with the average of all other embedding plus some random noise to make them different from each other. This parameter has effect only if `representation` is `dense`. - `embeddings_trainable` (default `true`): If `true` embeddings are trained during the training process, if `false` embeddings are fixed. It may be useful when loading pretrained embeddings for avoiding finetuning them. This parameter has effect only for `representation` is `dense` as `sparse` one-hot encodings are not trainable. - `dropout` (default `false`): determines if there should be a dropout layer before returning the encoder output. - `initializer` (default `null`): the initializer to use. If `null`, the default initialized of each variable is used (`glorot_uniform` in most cases). Options are: `constant`, `identity`, `zeros`, `ones`, `orthogonal`, `normal`, `uniform`, `truncated_normal`, `variance_scaling`, `glorot_normal`, `glorot_uniform`, `xavier_normal`, `xavier_uniform`, `he_normal`, `he_uniform`, `lecun_normal`, `lecun_uniform`. Alternatively it is possible to specify a dictionary with a key `type` that identifies the type of initializer and other keys for its parameters, e.g. `{type: normal, mean: 0, stddev: 0}`. To know the parameters of each initializer, please refer to [TensorFlow's documentation](https://www.tensorflow.org/api_docs/python/tf/keras/initializers). - `regularize` (default `true`): if `true` the embedding weights are added to the set of weights that get regularized by a regularization loss (if the `regularization_lambda` in `training` is greater than 0). - `reduce_output` (default `sum`): describes the strategy to use to aggregate the embeddings of the items of the set. Possible values are `sum`, `mean` and `sqrt` (the weighted sum divided by the square root of the sum of the squares of the weights). - `tied_weights` (default `null`): name of the input feature to tie the weights the encoder with. It needs to be the name of a feature of the same type and with the same encoder parameters. Example set feature entry in the output features list: ```yaml name: set_csv_column_name type: set representation: dense embedding_size: 50 embeddings_on_cpu: false pretrained_embeddings: null embeddings_trainable: true dropout: false initializer: null regularize: true reduce_output: sum tied_weights: null ``` ### Set Output Features and Decoders Set features can be used when multi-label classification needs to be performed. There is only one decoder available for set features and it is a (potentially empty) stack of fully connected layers, followed by a projection into a vector of size of the number of available classes, followed by a sigmoid. ``` +--------------+ +---------+ +-----------+ |Combiner | |Fully | |Projection | +-------+ |Output +--->Connected+--->into Output+--->Sigmoid| |Representation| |Layers | |Space | +-------+ +--------------+ +---------+ +-----------+ ``` These are the available parameters of the set output feature - `reduce_inputs` (default `sum`): defines how to reduce an input that is not a vector, but a matrix or a higher order tensor, on the first dimension 9second if you count the batch dimension). Available values are: `sum`, `mean` or `avg`, `max`, `concat` (concatenates along the first dimension), `last` (returns the last vector of the first dimension). - `dependencies` (default `[]`): the output features this one is dependent on. For a detailed explanation refer to [Output Features Dependencies](#output-features-dependencies). - `reduce_dependencies` (default `sum`): defines how to reduce the output of a dependent feature that is not a vector, but a matrix or a higher order tensor, on the first dimension 9second if you count the batch dimension). Available values are: `sum`, `mean` or `avg`, `max`, `concat` (concatenates along the first dimension), `last` (returns the last vector of the first dimension). - `loss` (default `{type: sigmoid_cross_entropy}`): is a dictionary containing a loss `type`. The available loss `type` is `sigmoid_cross_entropy`. These are the available parameters of a set output feature decoder - `fc_layers` (default `null`): it is a list of dictionaries containing the parameters of all the fully connected layers. The length of the list determines the number of stacked fully connected layers and the content of each dictionary determines the parameters for a specific layer. The available parameters for each layer are: `fc_size`, `norm`, `activation`, `dropout`, `initializer` and `regularize`. If any of those values is missing from the dictionary, the default one specified as a parameter of the decoder will be used instead. - `num_fc_layers` (default 0): this is the number of stacked fully connected layers that the input to the feature passes through. Their output is projected in the feature's output space. - `fc_size` (default `256`): if a `fc_size` is not already specified in `fc_layers` this is the default `fc_size` that will be used for each layer. It indicates the size of the output of a fully connected layer. - `activation` (default `relu`): if an `activation` is not already specified in `fc_layers` this is the default `activation` that will be used for each layer. It indicates the activation function applied to the output. - `norm` (default `null`): if a `norm` is not already specified in `fc_layers` this is the default `norm` that will be used for each layer. It indicates the norm of the output and it can be `null`, `batch` or `layer`. - `dropout` (default `false`): determines if there should be a dropout layer after each layer. - `initializer` (default `null`): the initializer to use. If `null`, the default initialized of each variable is used (`glorot_uniform` in most cases). Options are: `constant`, `identity`, `zeros`, `ones`, `orthogonal`, `normal`, `uniform`, `truncated_normal`, `variance_scaling`, `glorot_normal`, `glorot_uniform`, `xavier_normal`, `xavier_uniform`, `he_normal`, `he_uniform`, `lecun_normal`, `lecun_uniform`. Alternatively it is possible to specify a dictionary with a key `type` that identifies the type of initializer and other keys for its parameters, e.g. `{type: normal, mean: 0, stddev: 0}`. To know the parameters of each initializer, please refer to [TensorFlow's documentation](https://www.tensorflow.org/api_docs/python/tf/keras/initializers). - `regularize` (default `true`): if `true` the wights of the layers are added to the set of weights that get regularized by a regularization loss (if the `regularization_lambda` in `training` is greater than 0). - `threshold` (default `0.5`): The threshold above (greater or equal) which the predicted output of the sigmoid will be mapped to 1. Example set feature entry (with default parameters) in the output features list: ```yaml name: set_csv_column_name type: set reduce_inputs: sum dependencies: [] reduce_dependencies: sum loss: type: sigmoid_cross_entropy fc_layers: null num_fc_layers: 0 fc_size: 256 activation: relu norm: null dropout: false initializer: null regularize: true threshold: 0.5 ``` ### Set Features Measures The measures that are calculated every epoch and are available for category features are `jaccard_index` and the `loss` itself. You can set either of them as `validation_measure` in the `training` section of the model definition if you set the `validation_field` to be the name of a binary feature. Bag Features ------------ ### Bag Features Preprocessing Bag features are treated in the same way of set features, with the only difference being that the matrix had float values (frequencies). ### Bag Input Features and Encoders Bag features have one encoder, the raw float values coming from the input placeholders are first transformed in sparse integer lists, then they are mapped to either dense or sparse embeddings (one-hot encodings), they are aggregated as a weighted sum, where the weights are the original float values, and finally returned as outputs. Inputs are of size `b` while outputs are fo size `b x h` where `b` is the batch size and `h` is the dimensionality of the embeddings. The parameters are the same used for set input features with the exception of `reduce_output` that does not apply in this case because the weighted sum already acts as a reducer. ### Bag Output Features and Decoders There is no bag decoder available yet. ### Bag Features Measures As there is no decoder there is also no measure available yet for bag feature. Sequence Features ----------------- ### Sequence Features Preprocessing Sequence features are transformed into an integer valued matrix of size `n x l` (where `n` is the size of the dataset and `l` is the minimum of the length of the longest sequence and a `sequence_length_limit` parameter) and added to HDF5 with a key that reflects the name of column in the CSV. The way sequences are mapped into integers consists in first using a formatter to map from strings to sequences of tokens (by default this is done by splitting on spaces). Then a a dictionary of all the different token strings present in the column of the CSV is collected, then they are ranked by frequency and an increasing integer ID is assigned to them from the most frequent to the most rare (with 0 being assigned to `` used for padding and 1 assigned to `` item). The column name is added to the JSON file, with an associated dictionary containing 1. the mapping from integer to string (`idx2str`) 2. the mapping from string to id (`str2idx`) 3. the mapping from string to frequency (`str2freq`) 4. the maximum length of all sequences (`sequence_length_limit`) 5. additional preprocessing information (by default how to fill missing values and what token to use to fill missing values) The parameters available for preprocessing are - `missing_value_strategy` (default `fill_with_const`): what strategy to follow when there's a missing value in a binary column. The value should be one of `fill_with_const` (replaces the missing value with a specific value specified with the `fill_value` parameter), `fill_with_mode` (replaces the missing values with the most frequent value in the column), `fill_with_mean` (replaces the missing values with the mean of the values in the column), `backfill` (replaces the missing values with the next valid value). - `fill_value` (default `""`): the value to replace the missing values with in case the `missing_value_strategy` is `fill_value`. - `padding` (default `right`): the direction of the padding. `right` and `left` are available options. - `padding_symbol` (default ``): the string used as a padding symbol. Is is mapped to the integer ID 0 in the vocabulary. - `unknown_symbol` (default ``): the string used as a unknown symbol. Is is mapped to the integer ID 1 in the vocabulary. - `lowercase` (default `false`): if the string has to be lowercase before being handled by the formatter. - `format` (default `space`): defines how to map from the raw string content of the CSV column to a sequence of elements. The default value `space` splits the string on spaces. Other options are: `underscore` (splits on underscore), `comma`(splits on comma), `json` (decodes the string into a set or a list through a JSON parser). - `most_common` (default `20000`): the maximum number of most common tokens to be considered. if the data contains more than this amount, the most infrequent tokens will be treated as unknown. - `sequence_length_limit` (default `256`): the maximum length of the sequence. Sequences that are longer than this value will be truncated, while sequences that are shorter will be padded. ### Sequence Input Features and Encoders Sequence features have several encoders and each of them has its own parameters. Inputs are of size `b` while outputs are fo size `b x h` where `b` is the batch size and `h` is the dimensionally of the output of the encoder. In case a representation for each element of the sequence is needed (for example for tagging them, or for using an attention mechanism), one can specify the parameter `reduce_output` to be `null` or `null` and the output will be a `b x s x h` tensor where `s` is the length of the sequence. Some encoders, because of their inner workings, may require additional parameters to be specified in order to obtain one representation for each element of the sequence. For instance the `parallel_cnn` encoder, by default pools and flattens the sequence dimension and then passes the flattened vector through fully connected layers, so in order to obtain the full tesnor one has to specify `reduce_output: null`. Sequence input feature parameters are - `encoder` (default ``parallel_cnn``): the name of the encoder to use to encode the sequence. The available ones are `embed`, `parallel_cnn`, `stacked_cnn`, `stacked_parallel_cnn`, `rnn` and `cnnrnn`. - `tied_weights` (default `null`): name of the input feature to tie the weights the encoder with. It needs to be the name of a feature of the same type and with the same encoder parameters. #### Embed Encoder The embed decoder simply maps each integer in the sequence to an embedding, creating a `b x s x h` tensor where `b` is the batch size, `s` is the length of the sequence and `h` is the embedding size. The tensor is reduced along the `s` dimension to obtain a single vector of size `h` for each element of the batch. If you want to output the full `b x s x h` tensor, you can specify `reduce_output: null`. ``` +------+ |Emb 12| +------+ +--+ |Emb 7 | |12| +------+ |7 | |Emb 43| +-----------+ |43| +------+ |Aggregation| |65+--->Emb 65+--->Reduce +-> |23| +------+ |Operation | |4 | |Emb 23| +-----------+ |1 | +------+ +--+ |Emb 4 | +------+ |Emb 1 | +------+ ``` These are the parameters available for the embed encoder - `representation'` (default `dense`): the possible values are `dense` and `sparse`. `dense` means the embeddings are initialized randomly, `sparse` means they are initialized to be one-hot encodings. - `embedding_size` (default `50`): it is the maximum embedding size, the actual size will be `min(vocabulary_size, embedding_size)` for `dense` representations and exactly `vocabulary_size` for the `sparse` encoding, where `vocabulary_size` is the number of different strings appearing in the training set in the column the feature is named after (plus 1 for ``). - `embeddings_on_cpu` (default `false`): by default embeddings matrices are stored on GPU memory if a GPU is used, as it allows for faster access, but in some cases the embedding matrix may be really big and this parameter forces the placement of the embedding matrix in regular memory and the CPU is used to resolve them, slightly slowing down the process as a result of data transfer between CPU and GPU memory. - `pretrained_embeddings` (default `null`): by default `dense` embeddings are initialized randomly, but this parameter allow to specify a path to a file containing embeddings in the [GloVe format](https://nlp.stanford.edu/projects/glove/). When the file containing the embeddings is loaded, only the embeddings with labels present in the vocabulary are kept, the others are discarded. If the vocabulary contains strings that have no match in the embeddings file, their embeddings are initialized with the average of all other embedding plus some random noise to make them different from each other. This parameter has effect only if `representation` is `dense`. - `embeddings_trainable` (default `true`): If `true` embeddings are trained during the training process, if `false` embeddings are fixed. It may be useful when loading pretrained embeddings for avoiding finetuning them. This parameter has effect only for `representation` is `dense` as `sparse` one-hot encodings are not trainable. - `dropout` (default `false`): determines if there should be a dropout layer before returning the encoder output. - `initializer` (default `null`): the initializer to use. If `null`, the default initialized of each variable is used (`glorot_uniform` in most cases). Options are: `constant`, `identity`, `zeros`, `ones`, `orthogonal`, `normal`, `uniform`, `truncated_normal`, `variance_scaling`, `glorot_normal`, `glorot_uniform`, `xavier_normal`, `xavier_uniform`, `he_normal`, `he_uniform`, `lecun_normal`, `lecun_uniform`. Alternatively it is possible to specify a dictionary with a key `type` that identifies the type of initializer and other keys for its parameters, e.g. `{type: normal, mean: 0, stddev: 0}`. To know the parameters of each initializer, please refer to [TensorFlow's documentation](https://www.tensorflow.org/api_docs/python/tf/keras/initializers). - `regularize` (default `true`): if `true` the embedding weights are added to the set of weights that get regularized by a regularization loss (if the `regularization_lambda` in `training` is greater than 0). - `reduce_output` (default `sum`): defines how to reduce the output tensor along the `s` sequence length dimension if the rank of the tensor is greater than 2. Available values are: `sum`, `mean` or `avg`, `max`, `concat` (concatenates along the first dimension), `last` (returns the last vector of the first dimension) and `null` or `null` (which does not reduce and returns the full tensor). Example sequence feature entry in the output features list using an embed encoder: ```yaml name: sequence_csv_column_name type: sequence encoder: parallel_cnn tied_weights: null representation: dense embedding_size: 256 embeddings_on_cpu: false pretrained_embeddings: null embeddings_trainable: true dropout: false initializer: null regularize: true reduce_output: sum ``` #### Parallel CNN Encoder The parallel cnn encoder is inspired by [Yoon Kim's Convolutional Neural Network for Sentence Classification](https://arxiv.org/abs/1408.5882). It works by first mapping the input integer sequence `b x s` (where `b` is the batch size and `s` is the length of the sequence) into a sequence of embeddings, then it passes the embedding through a number of parallel 1d convolutional layers with different filter size (by default 4 layers with filter size 2, 3, 4 and 5), followed by max pooling and concatenation. This single vector concatenating the outputs of the parallel convolutional layers is then passed through a stack of fully connected layers and returned as a `b x h` tensor where `h` is the output size of the last fully connected layer. If you want to output the full `b x s x h` tensor, you can specify `reduce_output: null`. ``` +-------+ +----+ +-->1D Conv+--->Pool+-+ +------+ | |Width 2| +----+ | |Emb 12| | +-------+ | +------+ | | +--+ |Emb 7 | | +-------+ +----+ | |12| +------+ +-->1D Conv+--->Pool+-+ |7 | |Emb 43| | |Width 3| +----+ | +---------+ |43| +------+ | +-------+ | +------+ |Fully | |65+--->Emb 65+-+ +->Concat+-->Connected+-> |23| +------+ | +-------+ +----+ | +------+ |Layers | |4 | |Emb 23| +-->1D Conv+--->Pool+-+ +---------+ |1 | +------+ | |Width 4| +----+ | +--+ |Emb 4 | | +-------+ | +------+ | | |Emb 1 | | +-------+ +----+ | +------+ +-->1D Conv+--->Pool+-+ |Width 5| +----+ +-------+ ``` These are the available for an parallel cnn encoder: - `representation'` (default `dense`): the possible values are `dense` and `sparse`. `dense` means the embeddings are initialized randomly, `sparse` means they are initialized to be one-hot encodings. - `embedding_size` (default `256`): it is the maximum embedding size, the actual size will be `min(vocabulary_size, embedding_size)` for `dense` representations and exactly `vocabulary_size` for the `sparse` encoding, where `vocabulary_size` is the number of different strings appearing in the training set in the column the feature is named after (plus 1 for ``). - `embeddings_on_cpu` (default `false`): by default embeddings matrices are stored on GPU memory if a GPU is used, as it allows for faster access, but in some cases the embedding matrix may be really big and this parameter forces the placement of the embedding matrix in regular memory and the CPU is used to resolve them, slightly slowing down the process as a result of data transfer between CPU and GPU memory. - `pretrained_embeddings` (default `null`): by default `dense` embeddings are initialized randomly, but this parameter allow to specify a path to a file containing embeddings in the [GloVe format](https://nlp.stanford.edu/projects/glove/). When the file containing the embeddings is loaded, only the embeddings with labels present in the vocabulary are kept, the others are discarded. If the vocabulary contains strings that have no match in the embeddings file, their embeddings are initialized with the average of all other embedding plus some random noise to make them different from each other. This parameter has effect only if `representation` is `dense`. - `embeddings_trainable` (default `true`): If `true` embeddings are trained during the training process, if `false` embeddings are fixed. It may be useful when loading pretrained embeddings for avoiding finetuning them. This parameter has effect only for `representation` is `dense` as `sparse` one-hot encodings are not trainable. - `conv_layers` (default `null`): it is a list of dictionaries containing the parameters of all the convolutional layers. The length of the list determines the number of parallel convolutional layers and the content of each dictionary determines the parameters for a specific layer. The available parameters for each layer are: `filter_size`, `num_filters`, `pool`, `norm`, `activation` and `regularize`. If any of those values is missing from the dictionary, the default one specified as a parameter of the encoder will be used instead. If both `conv_layers` and `num_conv_layers` are `null`, a default list will be assigned to `conv_layers` with the value `[{filter_size: 2}, {filter_size: 3}, {filter_size: 4}, {filter_size: 5}]`. - `num_conv_layers` (default `null`): if `conv_layers` is `null`, this is the number of parallel convolutional layers. - `filter_size` (default `3`): if a `filter_size` is not already specified in `conv_layers` this is the default `filter_size` that will be used for each layer. It indicates how wide is the 1d convolutional filter. - `num_filters` (default `256`): if a `num_filters` is not already specified in `conv_layers` this is the default `num_filters` that will be used for each layer. It indicates the number of filters, and by consequence the output channels of the 1d convolution. - `pool_size` (default `null`): if a `pool_size` is not already specified in `conv_layers` this is the default `pool_size` that will be used for each layer. It indicates the size of the max pooling that will be performed along the `s` sequence dimension after the convolution operation. - `fc_layers` (default `null`): it is a list of dictionaries containing the parameters of all the fully connected layers. The length of the list determines the number of stacked fully connected layers and the content of each dictionary determines the parameters for a specific layer. The available parameters for each layer are: `fc_size`, `norm`, `activation`, `initializer` and `regularize`. If any of those values is missing from the dictionary, the default one specified as a parameter of the encoder will be used instead. If both `fc_layers` and `num_fc_layers` are `null`, a default list will be assigned to `fc_layers` with the value `[{fc_size: 512}, {fc_size: 256}]`. (only applies if `reduce_output` is not `null`). - `num_fc_layers` (default `null`): if `fc_layers` is `null`, this is the number of stacked fully connected layers (only applies if `reduce_output` is not `null`). - `fc_size` (default `256`): if a `fc_size` is not already specified in `fc_layers` this is the default `fc_size` that will be used for each layer. It indicates the size of the output of a fully connected layer. - `activation` (default `relu`): if an `activation` is not already specified in `conv_layers` or `fc_layers` this is the default `activation` that will be used for each layer. It indicates the activation function applied to the output. - `norm` (default `null`): if a `norm` is not already specified in `conv_layers` or `fc_layers` this is the default `norm` that will be used for each layer. It indicates the norm of the output. - `dropout` (default `false`): determines if there should be a dropout layer after each layer. - `initializer` (default `null`): the initializer to use. If `null` it uses `glorot_uniform`. Options are: `constant`, `identity`, `zeros`, `ones`, `orthogonal`, `normal`, `uniform`, `truncated_normal`, `variance_scaling`, `glorot_normal`, `glorot_uniform`, `xavier_normal`, `xavier_uniform`, `he_normal`, `he_uniform`, `lecun_normal`, `lecun_uniform`. Alternatively it is possible to specify a dictionary with a key `type` that identifies the type of initializer and other keys for its parameters, e.g. `{type: normal, mean: 0, stddev: 0}`. To know the parameters of each initializer, please refer to [TensorFlow's documentation](https://www.tensorflow.org/api_docs/python/tf/keras/initializers). - `regularize` (default `true`): if a `regularize` is not already specified in `conv_layers` or `fc_layers` this is the default `regularize` that will be used for each layer. It indicates if the layer weights should be considered when computing a regularization loss. - `reduce_output` (default `sum`): defines how to reduce the output tensor along the `s` sequence length dimension if the rank of the tensor is greater than 2. Available values are: `sum`, `mean` or `avg`, `max`, `concat` (concatenates along the sequence dimension), `last` (returns the last vector of the sequence dimension) and `null` or `null` (which does not reduce and returns the full tensor). Example sequence feature entry in the output features list using a parallel cnn encoder: ```yaml name: sequence_csv_column_name type: sequence encoder: parallel_cnn tied_weights: null representation: dense embedding_size: 256 embeddings_on_cpu: false pretrained_embeddings: null embeddings_trainable: true conv_layers: null num_conv_layers: null filter_size: 3 num_filters: 256 pool_size: null fc_layers: null num_fc_layers: null fc_size: 256 activation: relu norm: null dropout: false regularize: true reduce_output: sum ``` #### Stacked CNN Encoder The stacked cnn encoder is inspired by [Xiang Zhang at all's Character-level Convolutional Networks for Text Classification](https://arxiv.org/abs/1509.01626). It works by first mapping the input integer sequence `b x s` (where `b` is the batch size and `s` is the length of the sequence) into a sequence of embeddings, then it passes the embedding through a stack of 1d convolutional layers with different filter size (by default 6 layers with filter size 7, 7, 3, 3, 3 and 3), followed by an optional final pool and by a flatten operation. This single flatten vector is then passed through a stack of fully connected layers and returned as a `b x h` tensor where `h` is the output size of the last fully connected layer. If you want to output the full `b x s x h` tensor, you can specify the `pool_size` of all your `conv_layers` to be `null` and `reduce_output: null`, while if `pool_size` has a value different from `null` and `reduce_output: null` the returned tensor will be of shape `b x s' x h`, where `s'` is width of the output of the last convolutional layer. ``` +------+ |Emb 12| +------+ +--+ |Emb 7 | |12| +------+ |7 | |Emb 43| +----------------+ +---------+ |43| +------+ |1D Conv | |Fully | |65+--->Emb 65+--->Layers +-->Connected+-> |23| +------+ |Different Widths| |Layers | |4 | |Emb 23| +----------------+ +---------+ |1 | +------+ +--+ |Emb 4 | +------+ |Emb 1 | +------+ ``` These are the parameters available for the stack cnn encoder: - `representation'` (default `dense`): the possible values are `dense` and `sparse`. `dense` means the embeddings are initialized randomly, `sparse` means they are initialized to be one-hot encodings. - `embedding_size` (default `256`): it is the maximum embedding size, the actual size will be `min(vocabulary_size, embedding_size)` for `dense` representations and exactly `vocabulary_size` for the `sparse` encoding, where `vocabulary_size` is the number of different strings appearing in the training set in the column the feature is named after (plus 1 for ``). - `embeddings_on_cpu` (default `false`): by default embeddings matrices are stored on GPU memory if a GPU is used, as it allows for faster access, but in some cases the embedding matrix may be really big and this parameter forces the placement of the embedding matrix in regular memory and the CPU is used to resolve them, slightly slowing down the process as a result of data transfer between CPU and GPU memory. - `pretrained_embeddings` (default `null`): by default `dense` embeddings are initialized randomly, but this parameter allow to specify a path to a file containing embeddings in the [GloVe format](https://nlp.stanford.edu/projects/glove/). When the file containing the embeddings is loaded, only the embeddings with labels present in the vocabulary are kept, the others are discarded. If the vocabulary contains strings that have no match in the embeddings file, their embeddings are initialized with the average of all other embedding plus some random noise to make them different from each other. This parameter has effect only if `representation` is `dense`. - `embeddings_trainable` (default `true`): If `true` embeddings are trained during the training process, if `false` embeddings are fixed. It may be useful when loading pretrained embeddings for avoiding finetuning them. This parameter has effect only for `representation` is `dense` as `sparse` one-hot encodings are not trainable. - `conv_layers` (default `null`): it is a list of dictionaries containing the parameters of all the convolutional layers. The length of the list determines the number of stacked convolutional layers and the content of each dictionary determines the parameters for a specific layer. The available parameters for each layer are: `filter_size`, `num_filters`, `pool_size`, `norm`, `activation` and `regularize`. If any of those values is missing from the dictionary, the default one specified as a parameter of the encoder will be used instead. If both `conv_layers` and `num_conv_layers` are `null`, a default list will be assigned to `conv_layers` with the value `[{filter_size: 7, pool_size: 3, regularize: false}, {filter_size: 7, pool_size: 3, regularize: false}, {filter_size: 3, pool_size: null, regularize: false}, {filter_size: 3, pool_size: null, regularize: false}, {filter_size: 3, pool_size: null, regularize: true}, {filter_size: 3, pool_size: 3, regularize: true}]`. - `num_conv_layers` (default `null`): if `conv_layers` is `null`, this is the number of stacked convolutional layers. - `filter_size` (default `3`): if a `filter_size` is not already specified in `conv_layers` this is the default `filter_size` that will be used for each layer. It indicates how wide is the 1d convolutional filter. - `num_filters` (default `256`): if a `num_filters` is not already specified in `conv_layers` this is the default `num_filters` that will be used for each layer. It indicates the number of filters, and by consequence the output channels of the 1d convolution. - `pool_size` (default `null`): if a `pool_size` is not already specified in `conv_layers` this is the default `pool_size` that will be used for each layer. It indicates the size of the max pooling that will be performed along the `s` sequence dimension after the convolution operation. - `reduce_output` (default `max`): defines how to reduce the output tensor of the convolutional layers along the `s` sequence length dimension if the rank of the tensor is greater than 2. Available values are: `sum`, `mean` or `avg`, `max`, `concat` (concatenates along the first dimension), `last` (returns the last vector of the first dimension) and `null` or `null` (which does not reduce and returns the full tensor). - `fc_layers` (default `null`): it is a list of dictionaries containing the parameters of all the fully connected layers. The length of the list determines the number of stacked fully connected layers and the content of each dictionary determines the parameters for a specific layer. The available parameters for each layer are: `fc_size`, `norm`, `activation` and `regularize`. If any of those values is missing from the dictionary, the default one specified as a parameter of the encoder will be used instead. If both `fc_layers` and `num_fc_layers` are `null`, a default list will be assigned to `fc_layers` with the value `[{fc_size: 512}, {fc_size: 256}]`. (only applies if `reduce_output` is not `null`). - `num_fc_layers` (default `null`): if `fc_layers` is `null`, this is the number of stacked fully connected layers (only applies if `reduce_output` is not `null`). - `fc_size` (default `256`): if a `fc_size` is not already specified in `fc_layers` this is the default `fc_size` that will be used for each layer. It indicates the size of the output of a fully connected layer. - `activation` (default `relu`): if an `activation` is not already specified in `conv_layers` or `fc_layers` this is the default `activation` that will be used for each layer. It indicates the activation function applied to the output. - `norm` (default `null`): if a `norm` is not already specified in `conv_layers` or `fc_layers` this is the default `norm` that will be used for each layer. It indicates the norm of the output. - `dropout` (default `false`): determines if there should be a dropout layer after each layer. - `initializer` (default `null`): the initializer to use. If `null` it uses `glorot_uniform`. Options are: `constant`, `identity`, `zeros`, `ones`, `orthogonal`, `normal`, `uniform`, `truncated_normal`, `variance_scaling`, `glorot_normal`, `glorot_uniform`, `xavier_normal`, `xavier_uniform`, `he_normal`, `he_uniform`, `lecun_normal`, `lecun_uniform`. Alternatively it is possible to specify a dictionary with a key `type` that identifies the type of initializer and other keys for its parameters, e.g. `{type: normal, mean: 0, stddev: 0}`. To know the parameters of each initializer, please refer to [TensorFlow's documentation](https://www.tensorflow.org/api_docs/python/tf/keras/initializers). - `regularize` (default `true`): if a `regularize` is not already specified in `conv_layers` or `fc_layers` this is the default `regularize` that will be used for each layer. It indicates if the layer weights should be considered when computing a regularization loss. - `reduce_output` (default `sum`): defines how to reduce the output tensor along the `s` sequence length dimension if the rank of the tensor is greater than 2. Available values are: `sum`, `mean` or `avg`, `max`, `concat` (concatenates along the first dimension), `last` (returns the last vector of the first dimension) and `null` or `null` (which does not reduce and returns the full tensor). Example sequence feature entry in the output features list using a parallel cnn encoder: ```yaml name: sequence_csv_column_name type: sequence encoder: stacked_cnn tied_weights: null representation: dense embedding_size: 256 embeddings_on_cpu: false pretrained_embeddings: null embeddings_trainable: true conv_layers: null num_conv_layers: null filter_size: 3 num_filters: 256 pool_size: null fc_layers: null num_fc_layers: null fc_size: 256 activation: relu norm: null dropout: false initializer: null regularize: true reduce_output: max ``` #### Stacked Parallel CNN Encoder The stacked parallel cnn encoder is a combination of the Parallel CNN and the Stacked CNN encoders where each layer of the stack is a composed of parallel convolutional layers. It works by first mapping the input integer sequence `b x s` (where `b` is the batch size and `s` is the length of the sequence) into a sequence of embeddings, then it passes the embedding through a stack of several parallel 1d convolutional layers with different filter size, followed by an optional final pool and by a flatten operation. This single flatten vector is then passed through a stack of fully connected layers and returned as a `b x h` tensor where `h` is the output size of the last fully connected layer. If you want to output the full `b x s x h` tensor, you can specify `reduce_output: null`. ``` +-------+ +-------+ +-->1D Conv+-+ +-->1D Conv+-+ +------+ | |Width 2| | | |Width 2| | |Emb 12| | +-------+ | | +-------+ | +------+ | | | | +--+ |Emb 7 | | +-------+ | | +-------+ | |12| +------+ +-->1D Conv+-+ +-->1D Conv+-+ |7 | |Emb 43| | |Width 3| | | |Width 3| | +---------+ |43| +------+ | +-------+ | +------+ +---+ | +-------+ | +------+ +----+ |Fully | |65+--->Emb 65+-+ +->Concat+-->...+-+ +->Concat+-->Pool+-->Connected+-> |23| +------+ | +-------+ | +------+ +---+ | +-------+ | +------+ +----+ |Layers | |4 | |Emb 23| +-->1D Conv+-+ +-->1D Conv+-+ +---------+ |1 | +------+ | |Width 4| | | |Width 4| | +--+ |Emb 4 | | +-------+ | | +-------+ | +------+ | | | | |Emb 1 | | +-------+ | | +-------+ | +------+ +-->1D Conv+-+ +-->1D Conv+-+ |Width 5| |Width 5| +-------+ +-------+ ``` These are the available parameters for the stack parallel cnn encoder: - `representation'` (default `dense`): the possible values are `dense` and `sparse`. `dense` means the embeddings are initialized randomly, `sparse` means they are initialized to be one-hot encodings. - `embedding_size` (default `256`): it is the maximum embedding size, the actual size will be `min(vocabulary_size, embedding_size)` for `dense` representations and exactly `vocabulary_size` for the `sparse` encoding, where `vocabulary_size` is the number of different strings appearing in the training set in the column the feature is named after (plus 1 for ``). - `embeddings_on_cpu` (default `false`): by default embeddings matrices are stored on GPU memory if a GPU is used, as it allows for faster access, but in some cases the embedding matrix may be really big and this parameter forces the placement of the embedding matrix in regular memory and the CPU is used to resolve them, slightly slowing down the process as a result of data transfer between CPU and GPU memory. - `pretrained_embeddings` (default `null`): by default `dense` embeddings are initialized randomly, but this parameter allow to specify a path to a file containing embeddings in the [GloVe format](https://nlp.stanford.edu/projects/glove/). When the file containing the embeddings is loaded, only the embeddings with labels present in the vocabulary are kept, the others are discarded. If the vocabulary contains strings that have no match in the embeddings file, their embeddings are initialized with the average of all other embedding plus some random noise to make them different from each other. This parameter has effect only if `representation` is `dense`. - `embeddings_trainable` (default `true`): If `true` embeddings are trained during the training process, if `false` embeddings are fixed. It may be useful when loading pretrained embeddings for avoiding finetuning them. This parameter has effect only for `representation` is `dense` as `sparse` one-hot encodings are not trainable. - `stacked_layers` (default `null`): it is a of lists of list of dictionaries containing the parameters of the stack of parallel convolutional layers. The length of the list determines the number of stacked parallel convolutional layers, length of the sub-lists determines the number of parallel conv layers and the content of each dictionary determines the parameters for a specific layer. The available parameters for each layer are: `filter_size`, `num_filters`, `pool_size`, `norm`, `activation` and `regularize`. If any of those values is missing from the dictionary, the default one specified as a parameter of the encoder will be used instead. If both `stacked_layers` and `num_stacked_layers` are `null`, a default list will be assigned to `stacked_layers` with the value `[[{filter_size: 2}, {filter_size: 3}, {filter_size: 4}, {filter_size: 5}], [{filter_size: 2}, {filter_size: 3}, {filter_size: 4}, {filter_size: 5}], [{filter_size: 2}, {filter_size: 3}, {filter_size: 4}, {filter_size: 5}]]`. - `num_stacked_layers` (default `null`): if `stacked_layers` is `null`, this is the number of elements in the stack of parallel convolutional layers. - `filter_size` (default `3`): if a `filter_size` is not already specified in `conv_layers` this is the default `filter_size` that will be used for each layer. It indicates how wide is the 1d convolutional filter. - `num_filters` (default `256`): if a `num_filters` is not already specified in `conv_layers` this is the default `num_filters` that will be used for each layer. It indicates the number of filters, and by consequence the output channels of the 1d convolution. - `pool_size` (default `null`): if a `pool_size` is not already specified in `conv_layers` this is the default `pool_size` that will be used for each layer. It indicates the size of the max pooling that will be performed along the `s` sequence dimension after the convolution operation. - `fc_layers` (default `null`): it is a list of dictionaries containing the parameters of all the fully connected layers. The length of the list determines the number of stacked fully connected layers and the content of each dictionary determines the parameters for a specific layer. The available parameters for each layer are: `fc_size`, `norm`, `activation` and `regularize`. If any of those values is missing from the dictionary, the default one specified as a parameter of the encoder will be used instead. If both `fc_layers` and `num_fc_layers` are `null`, a default list will be assigned to `fc_layers` with the value `[{fc_size: 512}, {fc_size: 256}]`. (only applies if `reduce_output` is not `null`). - `num_fc_layers` (default `null`): if `fc_layers` is `null`, this is the number of stacked fully connected layers (only applies if `reduce_output` is not `null`). - `fc_size` (default `256`): if a `fc_size` is not already specified in `fc_layers` this is the default `fc_size` that will be used for each layer. It indicates the size of the output of a fully connected layer. - `norm` (default `null`): if a `norm` is not already specified in `conv_layers` or `fc_layers` this is the default `norm` that will be used for each layer. It indicates the norm of the output. - `activation` (default `relu`): if an `activation` is not already specified in `conv_layers` or `fc_layers` this is the default `activation` that will be used for each layer. It indicates the activation function applied to the output. - `regularize` (default `true`): if a `regularize` is not already specified in `conv_layers` or `fc_layers` this is the default `regularize` that will be used for each layer. It indicates if the layer weights should be considered when computing a regularization loss. - `reduce_output` (default `sum`): defines how to reduce the output tensor along the `s` sequence length dimension if the rank of the tensor is greater than 2. Available values are: `sum`, `mean` or `avg`, `max`, `concat` (concatenates along the first dimension), `last` (returns the last vector of the first dimension) and `null` or `null` (which does not reduce and returns the full tensor). Example sequence feature entry in the output features list using a parallel cnn encoder: ```yaml name: sequence_csv_column_name type: sequence encoder: stacked_parallel_cnn tied_weights: null representation: dense embedding_size: 256 embeddings_on_cpu: false pretrained_embeddings: null embeddings_trainable: true stacked_layers: null num_stacked_layers: null filter_size: 3 num_filters: 256 pool_size: null fc_layers: null num_fc_layers: null fc_size: 256 norm: null activation: relu regularize: true reduce_output: max ``` #### RNN Encoder The rnn encoder works by first mapping the input integer sequence `b x s` (where `b` is the batch size and `s` is the length of the sequence) into a sequence of embeddings, then it passes the embedding through a stack of recurrent layers (by default 1 layer), followed by a reduce operation that by default only returns the last output, but can perform other reduce functions. If you want to output the full `b x s x h` where `h` is the size of the output of the last rnn layer, you can specify `reduce_output: null`. ``` +------+ |Emb 12| +------+ +--+ |Emb 7 | |12| +------+ |7 | |Emb 43| +---------+ |43| +------+ +----------+ |Fully | |65+--->Emb 65+--->RNN Layers+-->Connected+-> |23| +------+ +----------+ |Layers | |4 | |Emb 23| +---------+ |1 | +------+ +--+ |Emb 4 | +------+ |Emb 1 | +------+ ``` These are the available parameters for the rnn encoder: - `representation'` (default `dense`): the possible values are `dense` and `sparse`. `dense` means the embeddings are initialized randomly, `sparse` means they are initialized to be one-hot encodings. - `embedding_size` (default `256`): it is the maximum embedding size, the actual size will be `min(vocabulary_size, embedding_size)` for `dense` representations and exactly `vocabulary_size` for the `sparse` encoding, where `vocabulary_size` is the number of different strings appearing in the training set in the column the feature is named after (plus 1 for ``). - `embeddings_on_cpu` (default `false`): by default embeddings matrices are stored on GPU memory if a GPU is used, as it allows for faster access, but in some cases the embedding matrix may be really big and this parameter forces the placement of the embedding matrix in regular memory and the CPU is used to resolve them, slightly slowing down the process as a result of data transfer between CPU and GPU memory. - `pretrained_embeddings` (default `null`): by default `dense` embeddings are initialized randomly, but this parameter allow to specify a path to a file containing embeddings in the [GloVe format](https://nlp.stanford.edu/projects/glove/). When the file containing the embeddings is loaded, only the embeddings with labels present in the vocabulary are kept, the others are discarded. If the vocabulary contains strings that have no match in the embeddings file, their embeddings are initialized with the average of all other embedding plus some random noise to make them different from each other. This parameter has effect only if `representation` is `dense`. - `embeddings_trainable` (default `true`): If `true` embeddings are trained during the training process, if `false` embeddings are fixed. It may be useful when loading pretrained embeddings for avoiding finetuning them. This parameter has effect only for `representation` is `dense` as `sparse` one-hot encodings are not trainable. - `num_layers` (default `1`): the number of stacked recurrent layers. - `cell_type` (default `rnn`): the type of recurrent cell to use. Available values are: `rnn`, `lstm`, `lstm_block`, `lstm`, `ln`, `lstm_cudnn`, `gru`, `gru_block`, `gru_cudnn`. For reference about the differences between the cells please refer to [TensorFlow's documentation](https://www.tensorflow.org/api_docs/python/tf/nn/rnn_cell). We suggest to use the `block` variants on CPU and the `cudnn` variants on GPU because of their increased speed. - `state_size` (default `256`): the size of the state of the rnn. - `bidirectional` (default `false`): if `true` two recurrent networks will perform encoding in the forward and backward direction and their outputs will be concatenated. - `dropout` (default `false`): determines if there should be a dropout layer before returning the encoder output. - `initializer` (default `null`): the initializer to use. If `null`, the default initialized of each variable is used (`glorot_uniform` in most cases). Options are: `constant`, `identity`, `zeros`, `ones`, `orthogonal`, `normal`, `uniform`, `truncated_normal`, `variance_scaling`, `glorot_normal`, `glorot_uniform`, `xavier_normal`, `xavier_uniform`, `he_normal`, `he_uniform`, `lecun_normal`, `lecun_uniform`. Alternatively it is possible to specify a dictionary with a key `type` that identifies the type of initializer and other keys for its parameters, e.g. `{type: normal, mean: 0, stddev: 0}`. To know the parameters of each initializer, please refer to [TensorFlow's documentation](https://www.tensorflow.org/api_docs/python/tf/keras/initializers). - `regularize` (default `true`): if `true` the embedding weights are added to the set of weights that get regularized by a regularization loss (if the `regularization_lambda` in `training` is greater than 0). - `reduce_output` (default `last`): defines how to reduce the output tensor along the `s` sequence length dimension if the rank of the tensor is greater than 2. Available values are: `sum`, `mean` or `avg`, `max`, `concat` (concatenates along the first dimension), `last` (returns the last vector of the first dimension) and `null` or `null` (which does not reduce and returns the full tensor). Example sequence feature entry in the output features list using a parallel cnn encoder: ```yaml name: sequence_csv_column_name type: sequence encoder: rnn tied_weights: null representation: dense embedding_size: 256 embeddings_on_cpu: false pretrained_embeddings: null embeddings_trainable: true num_layers: 1 cell_type: rnn state_size: 256 bidirectional: false dropout: false initializer: null regularize: true reduce_output: sum ``` #### CNN RNN Encoder The cnn rnn encoder works by first mapping the input integer sequence `b x s` (where `b` is the batch size and `s` is the length of the sequence) into a sequence of embeddings, then it passes the embedding through a stack of convolutional layers (by default 2), that is followed by a stack of recurrent layers (by default 1), followed by a reduce operation that by default only returns the last output, but can perform other reduce functions. If you want to output the full `b x s x h` where `h` is the size of the output of the last rnn layer, you can specify `reduce_output: null`. ``` +------+ |Emb 12| +------+ +--+ |Emb 7 | |12| +------+ |7 | |Emb 43| +---------+ |43| +------+ +----------+ +----------+ |Fully | |65+--->Emb 65+--->CNN Layers+--->RNN Layers+-->Connected+-> |23| +------+ +----------+ +----------+ |Layers | |4 | |Emb 23| +---------+ |1 | +------+ +--+ |Emb 4 | +------+ |Emb 1 | +------+ ``` These are the available parameters of the cnn rnn encoder: - `representation'` (default `dense`): the possible values are `dense` and `sparse`. `dense` means the embeddings are initialized randomly, `sparse` means they are initialized to be one-hot encodings. - `embedding_size` (default `256`): it is the maximum embedding size, the actual size will be `min(vocabulary_size, embedding_size)` for `dense` representations and exactly `vocabulary_size` for the `sparse` encoding, where `vocabulary_size` is the number of different strings appearing in the training set in the column the feature is named after (plus 1 for ``). - `embeddings_on_cpu` (default `false`): by default embeddings matrices are stored on GPU memory if a GPU is used, as it allows for faster access, but in some cases the embedding matrix may be really big and this parameter forces the placement of the embedding matrix in regular memory and the CPU is used to resolve them, slightly slowing down the process as a result of data transfer between CPU and GPU memory. - `pretrained_embeddings` (default `null`): by default `dense` embeddings are initialized randomly, but this parameter allow to specify a path to a file containing embeddings in the [GloVe format](https://nlp.stanford.edu/projects/glove/). When the file containing the embeddings is loaded, only the embeddings with labels present in the vocabulary are kept, the others are discarded. If the vocabulary contains strings that have no match in the embeddings file, their embeddings are initialized with the average of all other embedding plus some random noise to make them different from each other. This parameter has effect only if `representation` is `dense`. - `embeddings_trainable` (default `true`): If `true` embeddings are trained during the training process, if `false` embeddings are fixed. It may be useful when loading pretrained embeddings for avoiding finetuning them. This parameter has effect only for `representation` is `dense` as `sparse` one-hot encodings are not trainable. - `conv_layers` (default `null`): it is a list of dictionaries containing the parameters of all the convolutional layers. The length of the list determines the number of stacked convolutional layers and the content of each dictionary determines the parameters for a specific layer. The available parameters for each layer are: `filter_size`, `num_filters`, `pool_size`, `norm`, `activation` and `regularize`. If any of those values is missing from the dictionary, the default one specified as a parameter of the encoder will be used instead. If both `conv_layers` and `num_conv_layers` are `null`, a default list will be assigned to `conv_layers` with the value `[{filter_size: 7, pool_size: 3, regularize: false}, {filter_size: 7, pool_size: 3, regularize: false}, {filter_size: 3, pool_size: null, regularize: false}, {filter_size: 3, pool_size: null, regularize: false}, {filter_size: 3, pool_size: null, regularize: true}, {filter_size: 3, pool_size: 3, regularize: true}]`. - `num_conv_layers` (default `null`): if `conv_layers` is `null`, this is the number of parallel convolutional layers. - `filter_size` (default `3`): if a `filter_size` is not already specified in `conv_layers` this is the default `filter_size` that will be used for each layer. It indicates how wide is the 1d convolutional filter. - `num_filters` (default `256`): if a `num_filters` is not already specified in `conv_layers` this is the default `num_filters` that will be used for each layer. It indicates the number of filters, and by consequence the output channels of the 1d convolution. - `pool_size` (default `null`): if a `pool_size` is not already specified in `conv_layers` this is the default `pool_size` that will be used for each layer. It indicates the size of the max pooling that will be performed along the `s` sequence dimension after the convolution operation. - `num_rec_layers` (default `1`): the number of stacked recurrent layers. - `cell_type` (default `rnn`): the type of recurrent cell to use. Available values are: `rnn`, `lstm`, `lstm_block`, `lstm`, `ln`, `lstm_cudnn`, `gru`, `gru_block`, `gru_cudnn`. For reference about the differences between the cells please refer to [TensorFlow's documentstion](https://www.tensorflow.org/api_docs/python/tf/nn/rnn_cell). We suggest to use the `block` variants on CPU and the `cudnn` variants on GPU because of their increased speed. - `state_size` (default `256`): the size of the state of the rnn. - `bidirectional` (default `false`): if `true` two recurrent networks will perform encoding in the forward and backward direction and their outputs will be concatenated. - `dropout` (default `false`): determines if there should be a dropout layer between `conv_layers` and before returning the encoder output. - `initializer` (default `null`): the initializer to use. If `null`, the default initialized of each variable is used (`glorot_uniform` in most cases). Options are: `constant`, `identity`, `zeros`, `ones`, `orthogonal`, `normal`, `uniform`, `truncated_normal`, `variance_scaling`, `glorot_normal`, `glorot_uniform`, `xavier_normal`, `xavier_uniform`, `he_normal`, `he_uniform`, `lecun_normal`, `lecun_uniform`. Alternatively it is possible to specify a dictionary with a key `type` that identifies the type of initializer and other keys for its parameters, e.g. `{type: normal, mean: 0, stddev: 0}`. To know the parameters of each initializer, please refer to [TensorFlow's documentation](https://www.tensorflow.org/api_docs/python/tf/keras/initializers). - `regularize` (default `true`): if `true` the embedding weights are added to the set of weights that get regularized by a regularization loss (if the `regularization_lambda` in `training` is greater than 0). - `reduce_output` (default `last`): defines how to reduce the output tensor along the `s` sequence length dimension if the rank of the tensor is greater than 2. Available values are: `sum`, `mean` or `avg`, `max`, `concat` (concatenates along the first dimension), `last` (returns the last vector of the first dimension) and `null` or `null` (which does not reduce and returns the full tensor). Example sequence feature entry in the output features list using a parallel cnn encoder: ```yaml name: sequence_csv_column_name type: sequence encoder: rnn tied_weights: null representation: dense embedding_size: 256 embeddings_on_cpu: false pretrained_embeddings: null embeddings_trainable: true conv_layers: null num_conv_layers: null filter_size: 3 num_filters: 256 pool_size: null norm: null activation: relu num_rec_layers: 1 cell_type: rnn state_size: 256 bidirectional: false dropout: false initializer: null regularize: true reduce_output: last ``` ### Sequence Output Features and Decoders Sequential features can be used when sequence tagging (classifying each element of an input sequence) or sequence generation needs to be performed. There are two decoders available for those to tasks names `tagger` and `generator`. These are the available parameters of a sequence output feature - `reduce_inputs` (default `sum`): defines how to reduce an input that is not a vector, but a matrix or a higher order tensor, on the first dimension 9second if you count the batch dimension). Available values are: `sum`, `mean` or `avg`, `max`, `concat` (concatenates along the first dimension), `last` (returns the last vector of the first dimension). - `dependencies` (default `[]`): the output features this one is dependent on. For a detailed explanation refer to [Output Features Dependencies](#output-features-dependencies). - `reduce_dependencies` (default `sum`): defines how to reduce the output of a dependent feature that is not a vector, but a matrix or a higher order tensor, on the first dimension 9second if you count the batch dimension). Available values are: `sum`, `mean` or `avg`, `max`, `concat` (concatenates along the first dimension), `last` (returns the last vector of the first dimension). - `loss` (default `{type: softmax_cross_entropy, class_similarities_temperature: 0, class_weights: 1, confidence_penalty: 0, distortion: 1, labels_smoothing: 0, negative_samples: 0, robust_lambda: 0, sampler: null, unique: false}`): is a dictionary containing a loss `type`. The available losses `type` are `softmax_cross_entropy` and `sampled_softmax_cross_entropy`. For details on both losses, please refer to the [category feature output feature section](#category-output-features-and-encoders). #### Tagger Decoder In the case of `tagger` the decoder is a (potentially empty) stack of fully connected layers, followed by a projection into a tensor of size `b x s x c`, where `b` is the batch size, `s` is the length of the sequence and `c` is the number of classes, followed by a softmax_cross_entropy. This decoder requires its input to be shaped as `b x s x h`, where `h` is an hidden dimension, which is the output of a sequence, text or timeseries input feature without reduced outputs or the output of a sequence-based combiner. If a `b x h` input is provided instead, an error will be raised during model building. ``` Combiner Output +---+ +----------+ +-------+ |emb| +---------+ |Projection| |Softmax| +---+ |Fully | +----------+ +-------+ |...+--->Connected+--->... +--->... | +---+ |Layers | +----------+ +-------+ |emb| +---------+ |Projection| |Softmax| +---+ +----------+ +-------+ ``` These are the available parameters of a tagger decoder: - `fc_layers` (default `null`): it is a list of dictionaries containing the parameters of all the fully connected layers. The length of the list determines the number of stacked fully connected layers and the content of each dictionary determines the parameters for a specific layer. The available parameters for each layer are: `fc_size`, `norm`, `activation`, `dropout`, `initializer` and `regularize`. If any of those values is missing from the dictionary, the default one specified as a parameter of the decoder will be used instead. - `num_fc_layers` (default 0): this is the number of stacked fully connected layers that the input to the feature passes through. Their output is projected in the feature's output space. - `fc_size` (default `256`): if a `fc_size` is not already specified in `fc_layers` this is the default `fc_size` that will be used for each layer. It indicates the size of the output of a fully connected layer. - `activation` (default `relu`): if an `activation` is not already specified in `fc_layers` this is the default `activation` that will be used for each layer. It indicates the activation function applied to the output. - `norm` (default `null`): if a `norm` is not already specified in `fc_layers` this is the default `norm` that will be used for each layer. It indicates the norm of the output and it can be `null`, `batch` or `layer`. - `dropout` (default `false`): determines if there should be a dropout layer after each layer. - `initializer` (default `null`): the initializer to use. If `null`, the default initialized of each variable is used (`glorot_uniform` in most cases). Options are: `constant`, `identity`, `zeros`, `ones`, `orthogonal`, `normal`, `uniform`, `truncated_normal`, `variance_scaling`, `glorot_normal`, `glorot_uniform`, `xavier_normal`, `xavier_uniform`, `he_normal`, `he_uniform`, `lecun_normal`, `lecun_uniform`. Alternatively it is possible to specify a dictionary with a key `type` that identifies the type of initializer and other keys for its parameters, e.g. `{type: normal, mean: 0, stddev: 0}`. To know the parameters of each initializer, please refer to [TensorFlow's documentation](https://www.tensorflow.org/api_docs/python/tf/keras/initializers). - `regularize` (default `true`): if `true` the weights of the layers are added to the set of weights that get regularized by a regularization loss (if the `regularization_lambda` in `training` is greater than 0). Example sequence feature entry using a tagger decoder (with default parameters) in the output features list: ```yaml name: sequence_csv_column_name type: sequence reduce_inputs: sum dependencies: [] reduce_dependencies: sum loss: type: softmax_cross_entropy confidence_penalty: 0 robust_lambda: 0 class_weights: 1 class_similarities: null class_similarities_temperature: 0 labels_smoothing: 0 negative_samples: 0 sampler: null distortion: 1 unique: false fc_layers: null num_fc_layers: 0 fc_size: 256 activation: relu norm: null dropout: false initializer: null regularize: true ``` #### Generator Decoder In the case of `generator` the decoder is a (potentially empty) stack of fully connected layers, followed by an rnn that generates outputs feeding on its own previous predictions and generates a tensor of size `b x s' x c`, where `b` is the batch size, `s'` is the length of the generated sequence and `c` is the number of classes, followed by a softmax_cross_entropy. By default a generator expects a `b x h` shaped input tensor, where `h` is a hidden dimension. The `h` vectors are (after an optional stack of fully connected layers) fed into the rnn generator. One exception is when the generator uses attention, as in that case the expected size of the input tensor is `b x s x h`, which is the output of a sequence, text or timeseries input feature without reduced outputs or the output of a sequence-based combiner. If a `b x h` input is provided to a generator decoder using an rnn with attention instead, an error will be raised during model building. ``` Output Output 1 +-+ ... +--+ END ^ | ^ | ^ +--------+ +---------+ | | | | | |Combiner| |Fully | +---+--+ | +---+---+ | +---+--+ |Output +--->Connected+---+RNN +--->RNN... +--->RNN | | | |Layers | +---^--+ | +---^---+ | +---^--+ +--------+ +---------+ | | | | | GO +-----+ +-----+ ``` These are the available parameters of a tagger decoder: - `fc_layers` (default `null`): it is a list of dictionaries containing the parameters of all the fully connected layers. The length of the list determines the number of stacked fully connected layers and the content of each dictionary determines the parameters for a specific layer. The available parameters for each layer are: `fc_size`, `norm`, `activation`, `dropout`, `initializer` and `regularize`. If any of those values is missing from the dictionary, the default one specified as a parameter of the decoder will be used instead. - `num_fc_layers` (default 0): this is the number of stacked fully connected layers that the input to the feature passes through. Their output is projected in the feature's output space. - `fc_size` (default `256`): if a `fc_size` is not already specified in `fc_layers` this is the default `fc_size` that will be used for each layer. It indicates the size of the output of a fully connected layer. - `activation` (default `relu`): if an `activation` is not already specified in `fc_layers` this is the default `activation` that will be used for each layer. It indicates the activation function applied to the output. - `norm` (default `null`): if a `norm` is not already specified in `fc_layers` this is the default `norm` that will be used for each layer. It indicates the norm of the output and it can be `null`, `batch` or `layer`. - `dropout` (default `false`): determines if there should be a dropout layer after each layer. - `initializer` (default `null`): the initializer to use. If `null`, the default initialized of each variable is used (`glorot_uniform` in most cases). Options are: `constant`, `identity`, `zeros`, `ones`, `orthogonal`, `normal`, `uniform`, `truncated_normal`, `variance_scaling`, `glorot_normal`, `glorot_uniform`, `xavier_normal`, `xavier_uniform`, `he_normal`, `he_uniform`, `lecun_normal`, `lecun_uniform`. Alternatively it is possible to specify a dictionary with a key `type` that identifies the type of initializer and other keys for its parameters, e.g. `{type: normal, mean: 0, stddev: 0}`. To know the parameters of each initializer, please refer to [TensorFlow's documentation](https://www.tensorflow.org/api_docs/python/tf/keras/initializers). - `regularize` (default `true`): if `true` the weights of the layers are added to the set of weights that get regularized by a regularization loss (if the `regularization_lambda` in `training` is greater than 0). - `cell_type` (default `rnn`): the type of recurrent cell to use. Available values are: `rnn`, `lstm`, `lstm_block`, `lstm`, `ln`, `lstm_cudnn`, `gru`, `gru_block`, `gru_cudnn`. For reference about the differences between the cells please refer to [TensorFlow's documentstion](https://www.tensorflow.org/api_docs/python/tf/nn/rnn_cell). We suggest to use the `block` variants on CPU and the `cudnn` variants on GPU because of their increased speed. - `state_size` (default `256`): the size of the state of the rnn. - `tied_embeddings` (default `null`): if `null` the embeddings of the targets are initialized randomly, while if the values is the name of an input feature, the embeddings of that input feature will be used as embeddings of the target. The `vocabulary_size` of that input feature has to be the same of the output feature one and it has to have an embedding matrix (binary and numerical features will not have one, fo instance). In this case the `embedding_size` will be the same as the `state_size`. This is useful for implementing autoencoders where the encoding and decoding part of the model share parameters. - `embedding_size` (default 256): if `tied_target_embeddings` is `false`, the input embeddings and the weights of the softmax_cross_entropy weights before the softmax_cross_entropy are not tied together and can have different sizes, this parameter describes the size of the embeddings of the inputs of the generator. - `beam_width` (default `1`): sampling from the rnn generator is performed using beam search. By default, with a beam of one, only a greedy sequence using always the most probably next token is generated, but the beam size can be increased. This usually leads to better performance at the expense of more computation and slower generation. - `attention_mechanism` (default `null`): the recurrent generator may use an attention mechanism. The available ones are `bahdanau` and `luong` (for more information refer to [TensorFlow's documentation](https://www.tensorflow.org/api_guides/python/contrib.seq2seq#Attention)). When `attention` is not `null` the expected size of the input tensor is `b x s x h`, which is the output of a sequence, text or timeseries input feature without reduced outputs or the output of a sequence-based combiner. If a `b x h` input is provided to a generator decoder using an rnn with attention instead, an error will be raised during model building. Example sequence feature entry using a tagger decoder (with default parameters) in the output features list: ```yaml name: sequence_csv_column_name type: sequence reduce_inputs: sum dependencies: [] reduce_dependencies: sum loss: type: softmax_cross_entropy confidence_penalty: 0 robust_lambda: 0 class_weights: 1 class_similarities: null class_similarities_temperature: 0 labels_smoothing: 0 negative_samples: 0 sampler: null distortion: 1 unique: false fc_layers: null num_fc_layers: 0 fc_size: 256 activation: relu norm: null dropout: false initializer: null regularize: true cell_type: rnn state_size: 256 tied_target_embeddings: true embedding_size: 256 beam_width: 1 attention_mechanism: null ``` ### Sequence Features Measures The measures that are calculated every epoch and are available for category features are `accuracy` (counts the number of datapoints where all the elements of the predicted sequence are correct over the number of all datapoints), `token_accuracy` (computes the number of elements in all the sequences that are correctly predicted over the number of all the elements in all the sequences), `last_accuracy` (accuracy considering only the last element of the sequence, it is useful for being sure special end-of-sequence tokens are generated or tagged), `edit_distance` (the levenshtein distance between the predicted and ground truth sequence), `perplexity` (the perplexity of the ground truth sequence according to the model) and the `loss` itself. You can set either of them as `validation_measure` in the `training` section of the model definition if you set the `validation_field` to be the name of a binary feature. Text Features ------------- ### Text Features Preprocessing Text features are treated in the same way of sequence features, with a couple differences. Two different formattings/splittings happen, one that splits at every character and one that uses a spaCy based tokenizer (and removes stopwords) are used, and two different key are added to the HDF5 file, one containing the matrix of characters and one containing the matrix of words. The same thing happens in the JSON file, where there are dictionaries for mapping characters to integers (and the inverse) and words to integers (and their inverse). In the model definition you are able to specify which level of representation to use, if the character level or the word level. The parameters available for preprocessing are: - `missing_value_strategy` (default `fill_with_const`): what strategy to follow when there's a missing value in a binary column. The value should be one of `fill_with_const` (replaces the missing value with a specific value specified with the `fill_value` parameter), `fill_with_mode` (replaces the missing values with the most frequent value in the column), `fill_with_mean` (replaces the missing values with the mean of the values in the column), `backfill` (replaces the missing values with the next valid value). - `fill_value` (default `""`): the value to replace the missing values with in case the `missing_value_strategy` is `fill-value`. - `padding` (default `right`): the direction of the padding. `right` and `left` are available options. - `padding_symbol` (default ``): the string used as a padding symbol. Is is mapped to the integer ID 0 in the vocabulary. - `unknown_symbol` (default ``): the string used as a unknown symbol. Is is mapped to the integer ID 1 in the vocabulary. - `lowercase` (default `false`): if the string has to be lowercased before being handled by the formatter. - `word_sequence_length_limit` (default `256`): the maximum length of the text in words. Texts that are longer than this value will be truncated, while texts that are shorter will be padded. - `word_format` (default `space_punct`): defines how to map from the raw string content of the CSV column to a sequence of words. The default value `space_punct` splits the string using a regular expression that separates also punctuation. Other options are: `space` (splits on space), `underscore` (splits on underscore), `comma`(splits on comma), `json` (decodes the string into a set or a list through a JSON parser), and a set of format functions that rely on [spaCy](https://spacy.io). - `word_most_common` (default `20000`): the maximum number of most common words to be considered. If the data contains more than this amount, the most infrequent words will be treated as unknown. - `char_sequence_length_limit` (default `1024`): the maximum length of the text in characters. Texts that are longer than this value will be truncated, while sequences that are shorter will be padded. - `char_format` (default `characters`): defines how to map from the raw string content of the CSV column to a sequence of characters. The default value and only available option is `characters` and the behavior is to split the string at each character. - `char_most_common` (default `70`): the maximum number of most common characters to be considered. if the data contains more than this amount, the most infrequent characters will be treated as unknown. #### spaCy based word format options The spaCy based `word_format` options are functions that use the powerful tokenization and NLP preprocessing models provided the library. Several languages are available: English (code `en`), Italian (code `it`), Spanish (code `es`), German (code `de`), French (code `fr`), Portuguese (code `pt`), Dutch (code `nl`), Greek (code `el`) and Multi (code `xx`, useful in case you have a dataset of different languages). For each language different functions are available: - `tokenize`: uses spaCy tokenizer, - `tokenize_filter`: uses spaCy tokenizer and filters out punctuation, numbers, stopwords and words shorter than 3 characters, - `tokenize_remove_stopwords`: uses spaCy tokenizer and filters out stopwords, - `lemmatize`: uses spaCy lemmatizer, - `lemmatize_filter`: uses spaCy lemmatizer and filters out punctuation, numbers, stopwords and words shorter than 3 characters, - `lemmatize_remove_stopwords`: uses spaCy lemmatize and filters out stopwords. In order to use these options, you have to download the the spaCy model: ``` python -m spacy download ``` and provide `_` as `word_format` like: `english_tokenizer`, `italian_lemmatize_filter`, `multi_tokenize_filter` and so on. More details on the models can be found in the [spaCy documentation](https://spacy.io/models). ### Text Input Features and Encoders The encoders are the same used for the [Sequence Features](#sequence-input-features-and-encoders). The only difference is that you can specify an additional `level` parameter with possible values `word` or `char` to force to use the text words or characters as inputs (by default the encoder will use `word`). ### Text Output Features and Decoders The decoders are the same used for the [Sequence Features](#sequence-output-features-and-decoders). The only difference is that you can specify an additional `level` parameter with possible values `word` or `char` to force to use the text words or characters as inputs (by default the encoder will use `word`). ### Text Features Measures The measures are the same used for the [Sequence Features](#sequence-features-measures). Time Series Features -------------------- ### Time Series Features Preprocessing Timeseries features are treated in the same way of sequence features, with the only difference being that the matrix in the HDF5 file does not have integer values, but float values. Moreover, there is no need for any mapping in the JSON file. ### Time Series Input Features and Encoders The encoders are the same used for the [Sequence Features](#sequence-input-features-and-encoders). The only difference is that time series features don't have an embedding layer at the beginning, so the `b x s` placeholders (where `b` is the batch size and `s` is the sequence length) are directly mapped to a `b x s x 1` tensor and then passed to the different sequential encoders. ### Time Series Output Features and Decoders There are no time series decoders at the moment (WIP), so time series cannot be used as output features. ### Time Series Features Measures As no time series decoders are available at the moment, there are also no time series measures. Image Features -------------- ### Image Features Preprocessing Ludwig supports grayscale and color images, image type is automatically inferred. During preprocessing raw image files are transformed into numpy ndarrays and saved in the hdf5 format. Images should have the same size. If they have different sizes they can be converted to the same size which should be set in the feature preprocessing parameters. - `in_memory` (default: `true`): defines whether image dataset will reside in memory during the training process or will be dynamically fetched from disk (useful for large datasets). In the latter case a training batch of input images will be fetched from disk each training iteration. - `resize_method` (default: `crop_or_pad`): available options: `crop_or_pad` - crops larger images to the desired size or pads smalled images using edge padding; `interpolate` - uses interpolation. - `height` (default: null): image height in pixels, must be set if resizing is required - `width` (default: null): image width in pixels, must be set if resizing is required ### Image Input Features and Encoders Input image features are transformed into a float valued tensors of size `N x H x W x C` (where `N` is the size of the dataset and `H x W` is a specific resizing of the image that can be set, and `C` is the number of channels) and added to HDF5 with a key that reflects the name of column in the CSV. The column name is added to the JSON file, with an associated dictionary containing preprocessing information about the sizes of the resizing. Currently there are two encoders supported for images: Convolutional Stack Encoder and ResNet encoder which can be set by setting `encoder` parameter to `stacked_cnn` or `resnet` in the input feature dictionary in the model definition (`stacked_cnn` is the default one). #### Convolutional Stack Encoder Convolutional Stack Encoder takes the following optional parameters: - `conv_layers` (default `null`): it is a list of dictionaries containing the parameters of all the convolutional layers. The length of the list determines the number of stacked convolutional layers and the content of each dictionary determines the parameters for a specific layer. The available parameters for each layer are: `filter_size`, `num_filters`, `pool_size`, `norm`, `activation` and `regularize`. If any of those values is missing from the dictionary, the default one specified as a parameter of the encoder will be used instead. If both `conv_layers` and `num_conv_layers` are `null`, a default list will be assigned to `conv_layers` with the value `[{filter_size: 7, pool_size: 3, regularize: false}, {filter_size: 7, pool_size: 3, regularize: false}, {filter_size: 3, pool_size: null, regularize: false}, {filter_size: 3, pool_size: null, regularize: false}, {filter_size: 3, pool_size: null, regularize: true}, {filter_size: 3, pool_size: 3, regularize: true}]`. - `num_conv_layers` (default `null`): if `conv_layers` is `null`, this is the number of stacked convolutional layers. - `filter_size` (default `3`): if a `filter_size` is not already specified in `conv_layers` this is the default `filter_size` that will be used for each layer. It indicates how wide is the 1d convolutional filter. - `num_filters` (default `256`): if a `num_filters` is not already specified in `conv_layers` this is the default `num_filters` that will be used for each layer. It indicates the number of filters, and by consequence the output channels of the 2d convolution. - `pool_stride` (default `1`): if a `pool_stride` is not already specified in `conv_layers` this is the default `pool_stride` that will be used for each layer. - `fc_layers` (default `null`): it is a list of dictionaries containing the parameters of all the fully connected layers. The length of the list determines the number of stacked fully connected layers and the content of each dictionary determines the parameters for a specific layer. The available parameters for each layer are: `fc_size`, `norm`, `activation` and `regularize`. If any of those values is missing from the dictionary, the default one specified as a parameter of the encoder will be used instead. If both `fc_layers` and `num_fc_layers` are `null`, a default list will be assigned to `fc_layers` with the value `[{fc_size: 512}, {fc_size: 256}]`. (only applies if `reduce_output` is not `null`). - `num_fc_layers` (default `1`): This is the number of stacked fully connected layers. - `fc_size` (default `256`): if a `fc_size` is not already specified in `fc_layers` this is the default `fc_size` that will be used for each layer. It indicates the size of the output of a fully connected layer. - `norm` (default `null`): if a `norm` is not already specified in `fc_layers` or `conv_layers` this is the default `norm` that will be used for each layer. It indicates the norm of the output and it can be `null`, `batch` or `layer`. - `activation` (default `relu`): if an `activation` is not already specified in `fc_layers` or `conv_layers` this is the default `activation` that will be used for each layer. It indicates the activation function applied to the output. - `dropout` (default `false`): determines if there should be a dropout layer after each layer. - `initializer` (default `null`): the initializer to use. If `null`, the default initialized of each variable is used (`glorot_uniform` in most cases). Options are: `constant`, `identity`, `zeros`, `ones`, `orthogonal`, `normal`, `uniform`, `truncated_normal`, `variance_scaling`, `glorot_normal`, `glorot_uniform`, `xavier_normal`, `xavier_uniform`, `he_normal`, `he_uniform`, `lecun_normal`, `lecun_uniform`. Alternatively it is possible to specify a dictionary with a key `type` that identifies the type of initializer and other keys for its parameters, e.g. `{type: normal, mean: 0, stddev: 0}`. To know the parameters of each initializer, please refer to [TensorFlow's documentation](https://www.tensorflow.org/api_docs/python/tf/keras/initializers). - `regularize` (default `true`): if `true` the weights of the layers are added to the set of weights that get regularized by a regularization loss (if the `regularization_lambda` in `training` is greater than 0). #### ResNet Encoder ResNet Encoder takes the following optional parameters: - `resnet_size` (default `50`): A single integer for the size of the ResNet model. - `num_filters` (default `16`): It indicates the number of filters, and by consequence the output channels of the 2d convolution. - `kernel_size` (default `3`): The kernel size to use for convolution. - `conv_stride` (default `1`): Stride size for the initial convolutional layer. - `first_pool_size` (default `null`): Pool size to be used for the first pooling layer. If none, the first pooling layer is skipped. - `fc_layers` (default `null`): it is a list of dictionaries containing the parameters of all the fully connected layers. The length of the list determines the number of stacked fully connected layers and the content of each dictionary determines the parameters for a specific layer. The available parameters for each layer are: `fc_size`, `norm`, `activation` and `regularize`. If any of those values is missing from the dictionary, the default one specified as a parameter of the encoder will be used instead. If both `fc_layers` and `num_fc_layers` are `null`, a default list will be assigned to `fc_layers` with the value `[{fc_size: 512}, {fc_size: 256}]`. (only applies if `reduce_output` is not `null`). - `num_fc_layers` (default `1`): This is the number of stacked fully connected layers. - `fc_size` (default `256`): if a `fc_size` is not already specified in `fc_layers` this is the default `fc_size` that will be used for each layer. It indicates the size of the output of a fully connected layer. - `norm` (default `null`): if a `norm` is not already specified in `fc_layers` or `conv_layers` this is the default `norm` that will be used for each layer. It indicates the norm of the output and it can be `null`, `batch` or `layer`. - `activation` (default `relu`): if an `activation` is not already specified in `fc_layers` or `conv_layers` this is the default `activation` that will be used for each layer. It indicates the activation function applied to the output. - `dropout` (default `false`): determines if there should be a dropout layer after each layer. - `initializer` (default `null`): the initializer to use. If `null`, the default initialized of each variable is used (`glorot_uniform` in most cases). Options are: `constant`, `identity`, `zeros`, `ones`, `orthogonal`, `normal`, `uniform`, `truncated_normal`, `variance_scaling`, `glorot_normal`, `glorot_uniform`, `xavier_normal`, `xavier_uniform`, `he_normal`, `he_uniform`, `lecun_normal`, `lecun_uniform`. Alternatively it is possible to specify a dictionary with a key `type` that identifies the type of initializer and other keys for its parameters, e.g. `{type: normal, mean: 0, stddev: 0}`. To know the parameters of each initializer, please refer to [TensorFlow's documentation](https://www.tensorflow.org/api_docs/python/tf/keras/initializers). - `regularize` (default `true`): if `true` the weights of the layers are added to the set of weights that get regularized by a regularization loss (if the `regularization_lambda` in `training` is greater than 0). ### Image Output Features and Decoders There are no image decoders at the moment (WIP), so image cannot be used as output features. ### Image Features Measures As no image decoders are available at the moment, there are also no image measures. Combiners --------- Combiners are the part of the model that take the outputs of the encoders of all input features and combine them before providing the combined representation to the different output decoders. If you don't specify a combiner, the `concat` combiner will be used. ### Concat Combiner The concat combiner assumes all outputs from encoders are tensors of size `b x h` where `b` is the batch size and `h` is the hidden dimension, which can be different for each input. It concatenates along the `h` dimension, and then (optionally) passes the concatenated tensor through a stack of fully connected layers. It returns the final `b x h'` tensor where `h'` is the size of the last fully connected layer or the sum of the sizes of the `h` of all inputs in the case there are no fully connected layers. If there's only one input feature and no fully connected layers are specified, the output of the input feature is just passed through as output. ``` +-----------+ |Input | |Feature 1 +-+ +-----------+ | +---------+ +-----------+ | +------+ |Fully | |... +--->Concat+--->Connected+-> +-----------+ | +------+ |Layers | +-----------+ | +---------+ |Input +-+ |Feature N | +-----------+ ``` These are the available parameters of a concat combiner - `fc_layers` (default `null`): it is a list of dictionaries containing the parameters of all the fully connected layers. The length of the list determines the number of stacked fully connected layers and the content of each dictionary determines the parameters for a specific layer. The available parameters for each layer are: `fc_size`, `norm`, `activation`, `dropout`, `initializer` and `regularize`. If any of those values is missing from the dictionary, the default one specified as a parameter of the decoder will be used instead. - `num_fc_layers` (default 0): this is the number of stacked fully connected layers that the input to the feature passes through. Their output is projected in the feature's output space. - `fc_size` (default `256`): if a `fc_size` is not already specified in `fc_layers` this is the default `fc_size` that will be used for each layer. It indicates the size of the output of a fully connected layer. - `activation` (default `relu`): if an `activation` is not already specified in `fc_layers` this is the default `activation` that will be used for each layer. It indicates the activation function applied to the output. - `norm` (default `null`): if a `norm` is not already specified in `fc_layers` this is the default `norm` that will be used for each layer. It indicates the norm of the output and it can be `null`, `batch` or `layer`. - `dropout` (default `false`): determines if there should be a dropout layer after each layer. - `initializer` (default `null`): the initializer to use. If `null`, the default initialized of each variable is used (`glorot_uniform` in most cases). Options are: `constant`, `identity`, `zeros`, `ones`, `orthogonal`, `normal`, `uniform`, `truncated_normal`, `variance_scaling`, `glorot_normal`, `glorot_uniform`, `xavier_normal`, `xavier_uniform`, `he_normal`, `he_uniform`, `lecun_normal`, `lecun_uniform`. Alternatively it is possible to specify a dictionary with a key `type` that identifies the type of initializer and other keys for its parameters, e.g. `{type: normal, mean: 0, stddev: 0}`. To know the parameters of each initializer, please refer to [TensorFlow's documentation](https://www.tensorflow.org/api_docs/python/tf/keras/initializers). - `regularize` (default `true`): if `true` the weights of the layers are added to the set of weights that get regularized by a regularization loss (if the `regularization_lambda` in `training` is greater than 0). Example concat combiner in the model definition: ```yaml type: concat fc_layers: null num_fc_layers: 0 fc_size: 256 activation: relu norm: null dropout: false initializer: null regularize: true ``` ### Sequence Concat Combiner The sequence concat combiner assumes at least one output from encoders is a tensors of size `b x s x h` where `b` is the batch size, `s` is the length of the sequence and `h` is the hidden dimension. The sequence / text / sequential input can be specified with the `main_sequence_feature` parameter that should have the name of the sequential feature as value. If no `main_sequence_feature` is specified, the combiner will look through all the features in the order they are defined in the model definition and will look for a feature with a rank 3 tensor output (sequence, text or time series). If it cannot find one it will raise an exception, otherwise the output of that feature will be used for concatenating the other features along the sequence `s` dimension. If there are other input features with a rank 3 output tensor, the combiner will concatenate them alongside the `s` dimension, which means that all of them must have identical `s` dimension, otherwise an error will be thrown. Specifically, as the placeholders of the sequential features are of dimension `[None, None]` in order to make the `BucketedBatcher` trim longer sequences to their actual length, the check if the sequences are of the same length cannot be performed at model building time, and a dimension mismatch error will be returned during training when a datapoint with two sequential features of different lengths are provided. Other features that have a `b x h` rank 2 tensor output will be replicated `s` times and concatenated to the `s` dimension. The final output is a `b x s x h'` tensor where `h'` is the size of the concatenation of the `h` dimensions of all input features. ``` +-----------+ |Input | |Feature 1 +-+ +-----------+ | +---------+ +-----------+ | +------+ |Fully | |... +--->Concat+--->Connected+-> +-----------+ | +------+ |Layers | +-----------+ | +---------+ |Input +-+ |Feature N | +-----------+ ``` These are the available parameters of a sequence concat combiner - `main_sequence_feature` (default `null`): name fo the sequence / text/ time series feature to concatenate the outputs of the other features to. If no `main_sequence_feature` is specified, the combiner will look through all the features in the order they are defined in the model definition and will look for a feature with a rank 3 tensor output (sequence, text or time series). If it cannot find one it will raise an exception, otherwise the output of that feature will be used for concatenating the other features along the sequence `s` dimension. If there are other input features with a rank 3 output tensor, the combiner will concatenate them alongside the `s` dimension, which means that all of them must have identical `s` dimension, otherwise an error will be thrown. Example sequence concat combiner in the model definition: ```yaml type: sequence_concat main_sequence_feature: null ``` ### Sequence Combiner The sequence combiner stacks a sequence concat combiner with a sequence encoder one on top of each other. All the considerations about inputs tensor ranks describer for the [sequence concat combiner](#sequence-concat-combiner) apply also in this case, but the main difference is that this combiner uses the `b x s x h'` output of the sequence concat combiner, where `b` is the batch size, `s` is the sequence length and `h'` is the sum of the hidden dimensions of all input features, as input fo any of the sequence encoders described in the [sequence features encoders section](#sequence-inpit-features-and-encoders). Refer to that section for more detailed information about the sequence encoders and their parameters. Also all the considerations on the shape of the outputs done for the sequence encoders apply in this case too. ``` Sequence Feature Output +---------+ |emb seq 1| +---------+ |... +--+ +---------+ | +-----------------+ |emb seq n| | |emb seq 1|emb oth| +--------+ +---------+ | +-----------------+ |Sequence| +-->... |... +-->+Encoder +-> Other | +-----------------+ | | Feature | |emb seq n|emb oth| +--------+ Output | +-----------------+ | +-------+ | |emb oth+----+ +-------+ ``` Example sequence concat combiner in the model definition: ```yaml type: sequence main_sequence_feature: null encoder: parallel_cnn ... encoder parameters ... ``` Distributed Training ==================== You can distribute the training and prediction of your models using [Horovod](https://github.com/uber/horovod), which allows to train on a single machine with multiple GPUs as well as on multiple machines with multiple GPUs. In order to use distributed training you have to install Horovod as detailed in [Horovod's installation instructions](https://github.com/uber/horovod#install) (which include installing [OpenMPI](https://www.open-mpi.org) or other [MPI](https://en.wikipedia.org/wiki/Message_Passing_Interface) implementations) and then install the two packages: ``` pip install horovod mpi4py ``` Horovod works by, in practice, increasing the batch size and distributing a part of each batch to a different node and collecting the gradients from all the nodes in a smart and scalable way. It also adjusts the learning rate to counter balance the increase in the batch size. The advantage is that training speed scales almost linearly with the number of nodes. `experiment`, `train` and `predict` commands accept a `--horovod` argument that instructs the model building, training and prediction phases to be conducted using Horovod in a distributed way. An MPI command specifying which machines and / or GPUs to use, together with a few more parameters, must be provided before the call to Ludwig's command. For instance, in order to train a Ludwig model on a local machine with four GPUs one you can run: ``` mpirun -np 4 \ -H localhost:4 \ -bind-to none -map-by slot \ -x NCCL_DEBUG=INFO -x LD_LIBRARY_PATH -x PATH \ -mca pml ob1 -mca btl ^openib \ ludwig train --horovod ...other Ludwig parameters... ``` While for training on four remote machines with four GPUs each you can run: ``` mpirun -np 16 \ -H server1:4,server2:4,server3:4,server4:4 \ -bind-to none -map-by slot \ -x NCCL_DEBUG=INFO -x LD_LIBRARY_PATH -x PATH \ -mca pml ob1 -mca btl ^openib \ ludwig train --horovod ...other Ludwig parameters... ``` The same applies to `experiment` and `predict`. More details on the installation of MPI and how to run Horovod can be found in [Horovod's documentation](https://github.com/uber/horovod). Programmatic API ================ Ludwig functionalities can also be accessed through a programmatic API. The API consists of one `LudwigModel` class that can be initialized with a model definition dictionary and then can be trained with data coming in the form of a dataframe or a CSV file. Pretrained models can be loaded and can be used to obtain predictions on new data, again either in dataframe or CSV format. A detailed documentation of all the functions available in `LudwigModel` is provided in the [API documentation](api.md). Training a Model ---------------- To train a model one has first to initialize it using the initializer `LudwigModel()` and a model definition dictionary, and then calling the `train()` function using either a dataframe or a CSV file. ```python from ludwig import LudwigModel model_definition = {...} model = LudwigModel(model_definition) train_stats = model.train(data_csv=csv_file_path) # or train_stats = model.train(data_df=dataframe) ``` `model_definition` is a dictionary that has the same key-value structure of a model definition YAML file, as it's technically equivalent as parsing the YAML file into a Python dictionary. `train_statistics` will be a dictionary containing statistics about the training. The contents are exactly the same of the `training_statistics.json` file produced by the `experiment` and `train` commands. Loading a Pre-trained Model --------------------------- In order to load a pre-trained Ludwig model you have to call the static function `load()` of the `LudwigModel` class providing the path containing the model. ```python from ludwig import LudwigModel model = LudwigModel.load(model_path) ``` Predicting ---------- Either a newly trained model or a pre-trained loaded model can be used for predicting on new data using the `predict()` function of the model object. The CSV / dataframe has to contain columns with the same names of all the input features of the model. ```python predictions = model.predict(dataset_csv=csv_file_path) #or predictions = model.predict(dataset_df=dataframe) ``` `predictions` will be a dataframe containing the prediction and confidence score / probability of all output features. If you want to compute also measures on the quality of the predictions you can run: ```python predictions, test_stats = model.test(dataset_csv=csv_file_path) #or predictions, test_stats = model.test(dataset_df=dataframe) ``` In this case the CSV / dataframe should also contain columns with the same names of all the output features, as their content is going to be used as ground truth to compare the predictions against and compute the measures and `test_statistics` will be a dictionary containing several measures of quality depending on the type of each output feature (e.g. `category` features will have an accuracy measure and a confusion matrix, among other measures, associated to them, while `numerical` features will have measures like mean squared loss and R2 among others). Visualizations ============== Several visualization can be obtained from the result files from both `train`, `predict` and `experiment` by using the `visualize` command. The command has several parameters, but not all the visualizations use all of them. Let's first present the parameters of the general script, and then, for each available visualization, we will discuss about the specific parameters needed and what visualization they produce. ``` usage: ludwig visualize [options] This script analyzes results and shows some nice plots. optional arguments: -h, --help show this help message and exit -d DATA_CSV, --data_csv DATA_CSV raw data file -g GROUND_TRUTH, --ground_truth GROUND_TRUTH ground truth file -gm GROUND_TRUTH_METADATA, --ground_truth_metadata GROUND_TRUTH_METADATA input metadata JSON file -v {learning_curves,compare_performance,compare_classifiers_performance_from_prob,compare_classifiers_performance_from_pred,compare_classifiers_performance_subset,compare_classifiers_performance_changing_k,compare_classifiers_multiclass_multimetric,compare_classifiers_predictions,compare_classifiers_predictions_distribution,confidence_thresholding,confidence_thresholding_data_vs_acc,confidence_thresholding_data_vs_acc_subset,confidence_thresholding_data_vs_acc_subset_per_class,confidence_thresholding_2thresholds_2d,confidence_thresholding_2thresholds_3d,binary_threshold_vs_metric,roc_curves,roc_curves_from_prediction_statistics,calibration_1_vs_all,calibration_multiclass,confusion_matrix,frequency_vs_f1}, --visualization {learning_curves,compare_performance,compare_classifiers_performance_from_prob,compare_classifiers_performance_from_pred,compare_classifiers_performance_subset,compare_classifiers_performance_changing_k,compare_classifiers_multiclass_multimetric,compare_classifiers_predictions,compare_classifiers_predictions_distribution,confidence_thresholding,confidence_thresholding_data_vs_acc,confidence_thresholding_data_vs_acc_subset,confidence_thresholding_data_vs_acc_subset_per_class,confidence_thresholding_2thresholds_2d,confidence_thresholding_2thresholds_3d,binary_threshold_vs_metric,roc_curves,roc_curves_from_prediction_statistics,calibration_1_vs_all,calibration_multiclass,confusion_matrix,frequency_vs_f1} type of visualization -f FIELD, --field FIELD field containing ground truth -tf THRESHOLD_FIELDS [THRESHOLD_FIELDS ...], --threshold_fields THRESHOLD_FIELDS [THRESHOLD_FIELDS ...] fields for 2d threshold -pred PREDICTIONS [PREDICTIONS ...], --predictions PREDICTIONS [PREDICTIONS ...] predictions files -prob PROBABILITIES [PROBABILITIES ...], --probabilities PROBABILITIES [PROBABILITIES ...] probabilities files -ts TRAINING_STATISTICS [TRAINING_STATISTICS ...], --training_statistics TRAINING_STATISTICS [TRAINING_STATISTICS ...] training stats files -ps PREDICTION_STATISTICS [PREDICTION_STATISTICS ...], --prediction_statistics PREDICTION_STATISTICS [PREDICTION_STATISTICS ...] test stats files -mn MODEL_NAMES [MODEL_NAMES ...], --model_names MODEL_NAMES [MODEL_NAMES ...] names of the models to use as labels -tn TOP_N_CLASSES [TOP_N_CLASSES ...], --top_n_classes TOP_N_CLASSES [TOP_N_CLASSES ...] number of classes to plot -k TOP_K, --top_k TOP_K number of elements in the ranklist to consider -ll LABELS_LIMIT, --labels_limit LABELS_LIMIT maximum numbers of labels. If labels in dataset are higher than this number, "rare" label -ss {ground_truth,predictions}, --subset {ground_truth,predictions} type of subset filtering -n, --normalize normalize rows in confusion matrix -m METRICS [METRICS ...], --metrics METRICS [METRICS ...] metrics to dispay in threshold_vs_metric -pl POSITIVE_LABEL, --positive_label POSITIVE_LABEL label of the positive class for the roc curve -l {critical,error,warning,info,debug,notset}, --logging_level {critical,error,warning,info,debug,notset} the level of logging to use ``` Some additional information on the parameters: - The list parameters are considered to be aligned, meaning `predictions`, `probabilities`, `training_statistics`, `prediction_statistics` and `model_names` are indexed altogether, for instance the name of the model producing the second predictions in the list will be the second in the model names. - `data_csv` is intended to be the data the model(s) were trained on. - `ground_truth` and `ground_truth_metadata` are respectively the `HDF5` and `JSON` file obtained during training preprocessing. If you plan to use the visualizations then be sure not to use the `skip_save_preprocessing` when training. Those files are needed because they contain the split performed at preprocessing time, so it is easy to extract the test set from them. - `field` is the output feature to use for creating the visualization. Other parameters will be detailed for each visualization as different ones use them differently. Learning Curves --------------- ### learning_curves This visualization uses the `training_statistics` and `model_names` parameters. For each model (in the aligned lists of `training_statistics` and `model_names`) and for each output feature and measure of the model, it produces a line plot showing how that measure changed over the course of the epochs of training on the training and validation sets. ![Learning Curves Loss](images/learning_curves_loss.png "Learning Curves Loss") ![Learning Curves Accuracy](images/learning_curves_accuracy.png "Learning Curves Accuracy") Confusion Matrix ---------------- ### confusion_matrix This visualization uses the `top_n_classes`, `normalize`, `ground_truth_metadata`, `prediction_statistics` and `model_names` parameters. For each model (in the aligned lists of `prediction_statistics` and `model_names`) it produces a heatmap of the confusion matrix in the predictions for each field that has a confusion matrix in `prediction_statistics`. The value of `top_n_classes` limits the heatmap to the `n` most frequent classes. ![Confusion Matrix](images/confusion_matrix.png "Confusion Matrix") The second plot produced, is a barplot showing the entropy of each class, ranked from most entropic to least entropic. ![Confusion Matrix Entropy](images/confusion_matrix_entropy.png "Confusion Matrix Entropy") Compare Performance ------------------- ### compare_performance This visualization uses the `field`, `prediction_statistics` and `model_names` parameters. For each model (in the aligned lists of `prediction_statistics` and `model_names`) it produces bars in a bar plot, one for each overall metric available in the `prediction_statistics` file for the specified `field`. ![Compare Classifiers Performance](images/compare_performance.png "Compare Classifiers Performance") ### compare_classifiers_performance_from_prob This visualization uses the `ground_truth`, `field`, `probabilities` and `model_names` parameters. `field` needs to be a category. For each model (in the aligned lists of `probabilities` and `model_names`) it produces bars in a bar plot, one for each overall metric computed on the fly from the probabilities of predictions for the specified `field`. ![Compare Classifiers Performance from Probabilities](images/compare_classifiers_performance_from_prob.png "Compare Classifiers Performance from probabilities") ### compare_classifiers_performance_from_pred This visualization uses the `ground_truth`, `ground_truth_metadata`, `field`, `predictions` and `model_names` parameters. `field` needs to be a category. For each model (in the aligned lists of `predictions` and `model_names`) it produces bars in a bar plot, one for each overall metric computed on the fly from the predictions for the specified `field`. ![Compare Classifiers Performance from Predictions](images/compare_classifiers_performance_from_pred.png "Compare Classifiers Performance from Predictions") ### compare_classifiers_performance_subset This visualization uses the `top_n_classes`, `subset`, `ground_truth`, `ground_truth_metadata`, `field`, `probabilities` and `model_names` parameters. `field` needs to be a category. For each model (in the aligned lists of `predictions` and `model_names`) it produces bars in a bar plot, one for each overall metric computed on the fly from the probabilities predictions for the specified `field`, considering only a subset of the full training set. The way the subset is obtained is using the `top_n_classes` and `subset` parameters. If the values of `subset` is `ground_truth`, then only datapoints where the ground truth class is within the top `n` most frequent ones will be considered as test set, and the percentage of datapoints that have been kept from the original set will be displayed. ![Compare Classifiers Performance Subset Ground Truth](images/compare_classifiers_performance_subset_gt.png "Compare Classifiers Performance Subset Ground Truth") If the values of `subset` is `predictions`, then only datapoints where the the model predicts a class that is within the top `n` most frequent ones will be considered as test set, and the percentage of datapoints that have been kept from the original set will be displayed for each model. ![Compare Classifiers Performance Subset Ground Predictions](images/compare_classifiers_performance_subset_pred.png "Compare Classifiers Performance Subset Ground Predictions") ### compare_classifiers_performance_changing_k This visualization uses the `top_k`, `ground_truth_metadata`, `field`, `probabilities` and `model_names` parameters. `field` needs to be a category. For each model (in the aligned lists of `probabilities` and `model_names`) it produces a line plot that shows the Hits@K measure (that counts a prediction as correct if the model produces it among the first `k`) while changing `k` from 1 to `top_k` for the specified `field`. ![Compare Classifiers Performance Changing K](images/compare_classifiers_performance_changing_k.png "Compare Classifiers Performance Changing K") ### compare_classifiers_multiclass_multimetric This visualization uses the `top_n_classes`, `ground_truth_metadata`, `field`, `prediction_statistics` and `model_names` parameters. `field` needs to be a category. For each model (in the aligned lists of `prediction_statistics` and `model_names`) it produces four plots that show the precision, recall and F1 of the model on several classes for the specified `field`. The first one show the measures on the `n` most frequent classes. ![Multiclass Multimetric top k](images/compare_classifiers_multiclass_multimetric_topk.png "Multiclass Multimetric most frequent classes") The second one shows the measures on the `n` classes where the model performs the best. ![Multiclass Multimetric best k](images/compare_classifiers_multiclass_multimetric_bestk.png "Multiclass Multimetric best classes") The third one shows the measures on the `n` classes where the model performs the worst. ![Multiclass Multimetric worst k](images/compare_classifiers_multiclass_multimetric_worstk.png "Multiclass Multimetric worst classes") The fourth one shows the measures on all the classes, sorted by their frequency. This could become unreadable in case the number of classes is really high. ![Multiclass Multimetric sorted](images/compare_classifiers_multiclass_multimetric_sorted.png "Multiclass Multimetric sorted classes") Compare Classifier Predictions ------------------------------ ### compare_classifiers_predictions This visualization uses the `ground_truth`, `ground_truth_metadata`, `field`, `predictions` and `model_names` parameters. `field` needs to be a category and there must be two and only two models (in the aligned lists of `predictions` and `model_names`). This visualization produces a pie chart comparing the predictions of the two models for the specified `field`. ![Compare Classifiers Predictions](images/compare_classifiers_predictions.png "Compare Classifiers Predictions") ### compare_classifiers_predictions_distribution This visualization uses the `ground_truth`, `ground_truth_metadata`, `field`, `predictions` and `model_names` parameters. `field` needs to be a category. This visualization produces a radar plot comparing the distributions of predictions of the models for the first 10 classes of the specified `field`. ![Compare Classifiers Predictions Distribution](images/compare_classifiers_predictions_distribution.png "Compare Classifiers Predictions Distribution") Confidence_Thresholding -------------------- ### confidence_thresholding This visualization uses the `ground_truth`, `field`, `probabilities` and `model_names` parameters. `field` needs to be a category. For each model (in the aligned lists of `probabilities` and `model_names`) it produces a pair of lines indicating the accuracy of the model and the data coverage while increasing a threshold (x axis) on the probabilities of predictions for the specified `field`. ![Confidence_Thresholding](images/confidence_thresholding.png "Confidence_Thresholding") ### confidence_thresholding_data_vs_acc This visualization uses the `ground_truth`, `field`, `probabilities` and `model_names` parameters. `field` needs to be a category. For each model (in the aligned lists of `probabilities` and `model_names`) it produces a line indicating the accuracy of the model and the data coverage while increasing a threshold on the probabilities of predictions for the specified `field`. The difference with `confidence_thresholding` is that it uses two axes instead of three, not visualizing the threshold and having coverage as x axis instead of the threshold. ![Confidence_Thresholding Data vs Accuracy](images/confidence_thresholding_data_vs_acc.png "Confidence_Thresholding Data vs Accuracy") ### confidence_thresholding_data_vs_acc_subset This visualization uses the `top_n_classes`, `subset`, `ground_truth`, `field`, `probabilities` and `model_names` parameters. `field` needs to be a category. For each model (in the aligned lists of `probabilities` and `model_names`) it produces a line indicating the accuracy of the model and the data coverage while increasing a threshold on the probabilities of predictions for the specified `field`, considering only a subset of the full training set. The way the subset is obtained is using the `top_n_classes` and `subset` parameters.. The difference with `confidence_thresholding` is that it uses two axes instead of three, not visualizing the threshold and having coverage as x axis instead of the threshold. If the values of `subset` is `ground_truth`, then only datapoints where the ground truth class is within the top `n` most frequent ones will be considered as test set, and the percentage of datapoints that have been kept from the original set will be displayed. If the values of `subset` is `predictions`, then only datapoints where the the model predicts a class that is within the top `n` most frequent ones will be considered as test set, and the percentage of datapoints that have been kept from the original set will be displayed for each model. ![Confidence_Thresholding Data vs Accuracy Subset](images/confidence_thresholding_data_vs_acc_subset.png "Confidence_Thresholding Data vs Accuracy Subset") ### confidence_thresholding_data_vs_acc_subset_per_class This visualization uses the `top_n_classes`, `subset`, `ground_truth`, `ground_truth_metadata`, `field`, `probabilities` and `model_names` parameters. `field` needs to be a category. For each model (in the aligned lists of `probabilities` and `model_names`) it produces a line indicating the accuracy of the model and the data coverage while increasing a threshold on the probabilities of predictions for the specified `field`, considering only a subset of the full training set. The way the subset is obtained is using the `top_n_classes` and `subset` parameters.. The difference with `confidence_thresholding` is that it uses two axes instead of three, not visualizing the threshold and having coverage as x axis instead of the threshold. If the values of `subset` is `ground_truth`, then only datapoints where the ground truth class is within the top `n` most frequent ones will be considered as test set, and the percentage of datapoints that have been kept from the original set will be displayed. If the values of `subset` is `predictions`, then only datapoints where the the model predicts a class that is within the top `n` most frequent ones will be considered as test set, and the percentage of datapoints that have been kept from the original set will be displayed for each model. The difference with `confidence_thresholding_data_vs_acc_subset` is that it produces one plot per class within the `top_n_classes`. ![Confidence_Thresholding Data vs Accuracy Subset per class 1](images/confidence_thresholding_data_vs_acc_subset_per_class_1.png "Confidence_Thresholding Data vs Accuracy Subset per class 1") ![Confidence_Thresholding Data vs Accuracy Subset per class 4](images/confidence_thresholding_data_vs_acc_subset_per_class_4.png "Confidence_Thresholding Data vs Accuracy Subset per class 4") ### confidence_thresholding_2thresholds_2d This visualization uses the `ground_truth`, `threshold_fields`, `probabilities` and `model_names` parameters. `threshold_fields` need to be exactly two, either category or binary. `probabilities` need to be exactly two, aligned with `threshold_fields`. `model_names` has to be exactly one. Three plots are produced. The first plot shows several semi transparent lines. They summarize the 3d surfaces displayed by `confidence_thresholding_2thresholds_3d` that have thresholds on the confidence of the predictions of the two `threshold_fields` as x and y axes and either the data coverage percentage or the accuracy as z axis. Each line represents a slice of the data coverage surface projected onto the accuracy surface. ![Confidence_Thresholding two thresholds 2D Multiline](images/confidence_thresholding_2thresholds_2d_multiline.png "Confidence_Thresholding two thresholds 2D Multiline") The second plot shows the max of all the lines displayed in the first plot. ![Confidence_Thresholding two thresholds 2D Maxline](images/confidence_thresholding_2thresholds_2d_maxline.png "Confidence_Thresholding two thresholds 2D Maxline") The third plot shows the max line and the values of the thresholds that obtained a specific data coverage vs accuracy pair of values. ![Confidence_Thresholding two thresholds 2D Accuracy and Thresholds](images/confidence_thresholding_2thresholds_2d_accthr.png "Confidence_Thresholding two thresholds 2D Accuracy and Thresholds") ### confidence_thresholding_2thresholds_3d This visualization uses the `ground_truth`, `threshold_fields`, `probabilities` and `model_names` parameters. `threshold_fields` need to be exactly two, either category or binary. `probabilities` need to be exactly two, aligned with `threshold_fields`. `model_names` has to be exactly one. The plot shows the 3d surfaces displayed by `confidence_thresholding_2thresholds_3d` that have thresholds on the confidence of the predictions of the two `threshold_fields` as x and y axes and either the data coverage percentage or the accuracy as z axis. ![Confidence_Thresholding two thresholds 3D](images/confidence_thresholding_2thresholds_3d.png "Confidence_Thresholding two thresholds 3D") Binary Threshold vs. Metric --------------------------- ### binary_threshold_vs_metric This visualization uses the `positive_label`, `metrics`, `ground_truth`, `ground_truth_metadata`, `field`, `probabilities` and `model_names` parameters. `field` can be a category or binary feature. For each metric specified in `metrics` (options are `f1`, `precision`, `recall`, `accuracy`), this visualization produces a line chart plotting a threshold on the confidence of the model against the metric for the specified `field`. If `field` is a category feature, `positive_label` indicates which is the class to be considered positive class and all the others will be considered negative. It needs to be an integer, to figure out the association between classes and integers check the `ground_truth_metadata` JSON file. ![Binary_Threshold_vs_Metric](images/binary_threshold_vs_metric.png "Binary_Threshold_vs_Metric") ROC Curves ---------- ### roc_curves This visualization uses the `positive_label`, `ground_truth`, `ground_truth_metadata`, `field`, `probabilities` and `model_names` parameters. `field` can be a category or binary feature. This visualization produces a line chart plotting the roc curves for the specified `field`. If `field` is a category feature, `positive_label` indicates which is the class to be considered positive class and all the others will be considered negative. It needs to be an integer, to figure out the association between classes and integers check the `ground_truth_metadata` JSON file. ![ROC Curves](images/roc_curves.png "ROC Curves") ### roc_curves_from_test_statistics This visualization uses the `field`, `prediction_statistics` and `model_names` parameters. `field` needs to be binary feature. This visualization produces a line chart plotting the roc curves for the specified `field`. ![ROC Curves from Prediction Statistics](images/roc_curves_from_prediction_statistics.png "ROC Curves from Prediction Statistics") Calibration Plot ---------------- ### calibration_1_vs_all This visualization uses the `top_k`, `ground_truth`, `field`, `probabilities` and `model_names` parameters. `field` needs to be a category or binary. For each class or each of the `k` most frequent classes if `top_k` is specified, it produces two plots computed on the fly from the probabilities of predictions for the specified `field`. The first plot is a calibration curve that shows the calibration of the predictions considering the current class to be the true one and all others to be a false one, drawing one line for each model (in the aligned lists of `probabilities` and `model_names`). ![Calibration 1 vs All Curve](images/calibration_1_vs_all_curve.png "Calibration 1 vs All Curve") The second plot shows the distributions of the predictions considering the current class to be the true one and all others to be a false one, drawing the distribution for each model (in the aligned lists of `probabilities` and `model_names`). ![Calibration 1 vs All Counts](images/calibration_1_vs_all_counts.png "Calibration 1 vs All Counts") ### calibration_multiclass This visualization uses the `ground_truth`, `field`, `probabilities` and `model_names` parameters. `field` needs to be a category. For each class, produces two plots computed on the fly from the probabilities of predictions for the specified `field`. The first plot is a calibration curve that shows the calibration of the predictions considering al classes, drawing one line for each model (in the aligned lists of `probabilities` and `model_names`). ![Calibration Multiclass Curve](images/calibration_multiclass_curve.png "Calibration Multiclass Curve") The second plot shows a bar plot of the brier score (that calculates how calibrated are the probabilities of the predictions of a model), drawing one bar for each model (in the aligned lists of `probabilities` and `model_names`). ![Calibration Multiclass Brier](images/calibration_multiclass_brier.png "Calibration Multiclass Brier") Class Frequency vs. F1 score ---------------------------- ### frequency_vs_f1 This visualization uses the `ground_truth_metadata`, `field`, `prediction_statistics` and `model_names` parameters. `field` needs to be a category. For each model (in the aligned lists of `prediction_statistics` and `model_names`), produces two plots statistics of predictions for the specified `field`. The first plot is a line plot with one x axis representing the different classes and two vertical axes colored in orange and blue respectively. The orange one is the frequency of the class and an orange line is plotted to show the trend. The blue one is the F1 score for that class and a blue line is plotted to show the trend. The classes on the x axis are sorted by f1 score. ![Frequency vs F1 sorted by F1](images/freq_vs_f1_sorted_f1.png "Frequency vs F1 sorted by F1") The second plot has the same structure of the first one, but the axes are flipped and the classes on the x axis are sorted by frequency. ![Frequency vs F1 sorted by Frequency](images/freq_vs_f1_sorted_freq.png "Frequency vs F1 sorted by Frequency") """ try: from ludwig import LudwigModel except ImportError as e: print("packages are not installed - {}".format(e)) PKFONQ_mlvajra/model/__init__.py""" model building using torch,tensorflow,spark,sklearn """ from mlvajra.model.DSL import LudwigModel from mlvajra.model.vajron import *PKCUNCQ00mlvajra/model/vajron.pyfrom ..evaluation import classification_metrics,regression_metrics try: from mlflow import (create_experiment, set_experiment, run,ActiveRun , log_metric, log_param, set_tag, log_artifacts, log_artifact, active_run, start_run ,end_run, get_artifact_uri ) except ImportError as e: print("packages are not installed - {}".format(e)) """ class mlflowFillMissingUDF(Preprocessing): __name__="mlflowFillMissingUDF" def __init__(self,config,sklearnTransformer): self.config=config self.logger=gogo.Gogo( 'spark_preprocesing_transformers.mlflowUDF', low_hdlr=gogo.handlers.file_hdlr('{}/{}.log'.format(self.config['LOGS']['path'], self.__name__)), low_formatter=formatter, high_level='error', high_formatter=formatter).logger self.sklearnTransformer=sklearnTransformer self.DIR=self.config[self.__name__]['DIR']#/opt/apps/coeus/coeus/model_repository self.index=self.config[self.__name__]['index_columns'].split(',') self.columns=self.config[self.__name__]['reqcolumns'].split(',') self.groupbycolumns=self.config[self.__name__]['groupbycolumns'].split(',') def preprocessing_during_train(self,df, columns=['BerPreFecMax', 'PhaseCorrectionAve', 'PmdMin','Qmin', 'SoPmdAve'], index= ['nodeid','section','module'], transformer=StandardScaler, path='/opt/apps/coeus/coeus/model_repository/'): scalar_dict={} list_df=[] grouped=df.groupby(index) #columns=['BerPreFecMax', 'PhaseCorrectionAve', 'PmdMin','Qmin', 'SoPmdAve'] #index=['nodeid','section','module'] for name,v in grouped.groups.items(): scalar=transformer() a=scalar.fit_transform(df.iloc[v][columns]) tmp1=pd.DataFrame(a,columns=columns) tmp2=pd.DataFrame(df.iloc[v][index].values,columns=index) list_df.append(pd.concat([tmp2,tmp1])) scalar_dict[name[0]+'_'+name[2]]=scalar pickle.dump(scalar_dict,open(path+'{}_scalar_dict.pkl'.format(self.__name__),'wb')) pickle.dump(columns,open(path+'{}_columns.pkl'.format(self.__name__),'wb')) pickle.dump(index,open(path+'{}_index.pkl'.format(self.__name__),'wb')) return pd.concat(list_df) def apply_train(self,df): " # call preprocessing_during_train " artifacts = { "scalar_path":"{}/{}_scalar_dict.pkl".format(self.DIRself.__name__) , "columns_path":"{}/{}_columns.pkl".format(self.DIR,self.__name__), "index_path":"{}/{}_index.pkl".format(self.DIR,self.__name__) } pdf=df.toPandas() df=self.preprocessing_during_train(df,self.columns,self.groupbycolumns,self.sklearnTransformer,self.DIR) self.mlflow_pyfunc_model_path = "{}/{}_preprocessingUDF_{}".format(self.DIR,self.__name__,datetime.now().strftime("%Y_%M_%d")) mlflow.pyfunc.save_model( dst_path=self.mlflow_pyfunc_model_path, python_model=preprocessingUDF(), artifacts=artifacts) return df def apply_test(self,df,op): " # expected order nodeid,section,module,ts,BerPreFecMax,PhaseCorrectionAve,PmdMin,Qmin,SoPmdAve " df.createTempView('temp') df=op.spark.sql("select nodeid,section,module,ts,BerPreFecMax,PhaseCorrectionAve,PmdMin,Qmin,SoPmdAve from temp") pyfunc_udf = mlflow.pyfunc.spark_udf(op.spark,self.mlflow_pyfunc_model_path, result_type=ArrayType(FloatType())) df = df.withColumn("preprocessed_output",pyfunc_udf('nodeid','section','module','ts','BerPreFecMax', 'PhaseCorrectionAve', 'PmdMin','Qmin', 'SoPmdAve')) unnested_df=df.cols.unnest("preprocessed_output") #pre_actual_old_columns=['nodeid','section','module','ts']+[f"preprocessed_output_{i}" for i in range(0,5)] nested_df=unnested_df.cols.nest([f"preprocessed_output_{i}" for i in range(0,5)],output_col='preprocessed_output',separator=',') #pre_actual_df=unnested_df.cols.select(pre_actual_old_columns) #pre_actual_new_columns=['nodeid','section','module','ts']+['BerPreFecMax','PhaseCorrectionAve', 'PmdMin','Qmin', 'SoPmdAve'] #pre_actual_df=pre_actual_df.toDF(pre_actual_new_columns) df=nested_df.select(['nodeid','section','module','ts','preprocessed_output']) #nested_df=unnested_df.cols.nest([f"preprocessed_output_{i}" for i in range(0,5)],output_col='preprocessed_output',separator=',') #expected order nodeid,section,module,ts,BerPreFecMax,PhaseCorrectionAve,PmdMin,Qmin,SoPmdAve # The prediction column will contain all the numeric columns returned by the model as floats #TODO: df.createTempView('temp') # TODO:april_test=op.spark.sql("select nodeid,section,module,ts,BerPreFecMax,PhaseCorrectionAve,PmdMin,Qmin,SoPmdAve from temp") return df, #pre_actual_df """ PKWN|<<!mlvajra/preprocessing/__init__.py""" preprocessing for spark,sklearn,tensorflow,torch """ PK5N$-mlvajra/preprocessing/sklearnPreprocessing.pyfrom collections import namedtuple import pandas as pd import glob from sklearn.base import TransformerMixin,BaseEstimator #Tabular transformers from sklearn.preprocessing import (Binarizer,FunctionTransformer,KBinsDiscretizer, KernelCenterer,LabelBinarizer,LabelEncoder, MultiLabelBinarizer,MaxAbsScaler, MinMaxScaler, Normalizer,OneHotEncoder,OrdinalEncoder, PolynomialFeatures,PowerTransformer,QuantileTransformer, RobustScaler,StandardScaler,add_dummy_feature ,binarize, label_binarize,maxabs_scale,minmax_scale,normalize, quantile_transform,robust_scale,scale,power_transform) # NLP transformers from sklearn.feature_extraction.text import (CountVectorizer, TfidfTransformer,TfidfVectorizer, FeatureHasher,HashingVectorizer) class ParagraphVectors(BaseEstimator,TransformerMixin): """ sklearn style transformer for DOC2vec as paragraphVectors """ def __init__ (self,max_epochs=100, vec_size=100, alpha=0.025, dm=1, filename='user_story', **doc2vec_args): try : from gensim.models.doc2vec import Doc2Vec, TaggedDocument from nltk.tokenize import word_tokenize except ImportError: print("gensim or nltk was not installed") self.max_epochs=max_epochs self.vec_size=vec_size self.alpha=alpha self.dm=dm self.filename=filename self.DIRECTORY_PATH=r'tmp\\' def fit_transform(self,X,y=None): self.fit(X) return self.transform(X) def get_tagged_data(self,text_series): data=text_series.tolist() tagged_data = [TaggedDocument(words=word_tokenize(_d.lower()), tags=[str(i)]) for i, _d in enumerate(data)] return tagged_data def fit(self,text_series,**doc2vec_args): """ Distribuited memory vectors """ if not glob.glob(self.DIRECTORY_PATH+self.filename+'*.model'): tagged_data=self.get_tagged_data(text_series) model = Doc2Vec(size=self.vec_size, alpha=self.alpha, min_alpha=0.025, min_count=1, dm =self.dm, **doc2vec_args) model.build_vocab(tagged_data) else: tagged_data=self.get_tagged_data(text_series) model= Doc2Vec.load("{}{}_d2v.model".format(self.DIRECTORY_PATH,self.filename)) for epoch in range(self.max_epochs): print('iteration {0}'.format(epoch)) model.train(tagged_data, total_examples=model.corpus_count, epochs=model.iter) # decrease the learning rate model.alpha -= 0.0002 # fix the learning rate, no decay model.min_alpha = model.alpha model.save("{}{}_d2v.model".format(self.DIRECTORY_PATH,self.filename)) print("Model Saved to {}{}".format(self.DIRECTORY_PATH,self.filename)) def transform(self,test_sent_list): if isinstance(test_sent_list,pd.core.series.Series): test_sent_list=test_sent_list.tolist() if not (isinstance(test_sent_list,list) or isinstance(test_sent_list,np.ndarray)): test_sent_list=[test_sent_list] model= Doc2Vec.load("{}{}_d2v.model".format(self.DIRECTORY_PATH,self.filename)) pred=[] for sent in test_sent_list: pred.append(model.infer_vector(sent)) return np.vstack([pred]) # __all__=['Binarizer','FunctionTransformer','KBinsDiscretizer', # 'KernelCenterer','LabelBinarizer','LabelEncoder', # 'MultiLabelBinarizer','MaxAbsScaler', 'MinMaxScaler', # 'Normalizer','OneHotEncoder','OrdinalEncoder', # 'PolynomialFeatures','PowerTransformer','QuantileTransformer', # 'RobustScaler','StandardScaler','add_dummy_feature' ,'binarize', # 'label_binarize','maxabs_scale','minmax_scale','normalize', # 'quantile_transform','robust_scale','scale','power_transform'] tabular_transforms=['Binarizer','FunctionTransformer','KBinsDiscretizer', 'KernelCenterer','LabelBinarizer','LabelEncoder', 'MultiLabelBinarizer','MaxAbsScaler', 'MinMaxScaler', 'Normalizer','OneHotEncoder','OrdinalEncoder', 'PolynomialFeatures','PowerTransformer','QuantileTransformer', 'RobustScaler','StandardScaler','add_dummy_feature' ,'binarize', 'label_binarize','maxabs_scale','minmax_scale','normalize', 'quantile_transform','robust_scale','scale','power_transform'] nlp_transformers=['CountVectorizer','TfidfTransformer','TfidfVectorizer', 'FeatureHasher','HashingVectorizer','ParagraphVectors'] Tabular=namedtuple('tabular',tabular_transforms) Tabular.Binarizer=Binarizer Tabular.FunctionTransformer=FunctionTransformer Tabular.KBinsDiscretizer=KBinsDiscretizer Tabular.KernelCenterer=KernelCenterer Tabular.LabelBinarizer=LabelBinarizer Tabular.LabelEncoder=LabelEncoder Tabular.MultiLabelBinarizer=MultiLabelBinarizer Tabular.MaxAbsScaler=MaxAbsScaler Tabular.MinMaxScaler=MinMaxScaler Tabular.Normalizer=Normalizer Tabular.OneHotEncoder=OneHotEncoder Tabular.OrdinalEncoder=OrdinalEncoder Tabular.PolynomialFeatures=PolynomialFeatures Tabular.PowerTransformer=PowerTransformer Tabular.QuantileTransformer=QuantileTransformer nlp=namedtuple('nlp',nlp_transformers) nlp.CountVectorizer=CountVectorizer nlp.FeatureHasher=FeatureHasher nlp.HashingVectorizer=HashingVectorizer nlp.TfidfTransformer=TfidfTransformer nlp.TfidfVectorizer=TfidfVectorizer nlp.ParagraphVectors=ParagraphVectors """ TODO:adding datetime features cyclic features lag features window features """ __all__=['Tabular','nlp'] PK`NKʮ++0mlvajra/preprocessing/tensorflowPreprocessing.pyimport tensorflow_transform as tft tft.PKN_R~ ~ +mlvajra/preprocessing/torchPreprocessing.pyimport torch import torch.nn as nn import torch.optim as optim from torch.autograd import Function from numpy.fft import rfft2, irfft2 class BadFFTFunction(Function): def forward(self, input): numpy_input = input.detach().numpy() result = abs(rfft2(numpy_input)) return input.new(result) def backward(self, grad_output): numpy_go = grad_output.numpy() result = irfft2(numpy_go) return grad_output.new(result) # since this layer does not have any parameters, we can # simply declare this as a function, rather than as an nn.Module class def incorrect_fft(input): return BadFFTFunction()(input) __all__ = ['TabularModel'] class TabularModel(nn.Module): "Basic model for tabular data." def __init__(self, emb_szs:ListSizes, n_cont:int, out_sz:int, layers:Collection[int], ps:Collection[float]=None, emb_drop:float=0., y_range:OptRange=None, use_bn:bool=True, bn_final:bool=False): super().__init__() ps = ifnone(ps, [0]*len(layers)) ps = listify(ps, layers) self.embeds = nn.ModuleList([embedding(ni, nf) for ni,nf in emb_szs]) self.emb_drop = nn.Dropout(emb_drop) self.bn_cont = nn.BatchNorm1d(n_cont) n_emb = sum(e.embedding_dim for e in self.embeds) self.n_emb,self.n_cont,self.y_range = n_emb,n_cont,y_range sizes = self.get_sizes(layers, out_sz) actns = [nn.ReLU(inplace=True) for _ in range(len(sizes)-2)] + [None] layers = [] for i,(n_in,n_out,dp,act) in enumerate(zip(sizes[:-1],sizes[1:],[0.]+ps,actns)): layers += bn_drop_lin(n_in, n_out, bn=use_bn and i!=0, p=dp, actn=act) if bn_final: layers.append(nn.BatchNorm1d(sizes[-1])) self.layers = nn.Sequential(*layers) def get_sizes(self, layers, out_sz): return [self.n_emb + self.n_cont] + layers + [out_sz] def forward(self, x_cat:Tensor, x_cont:Tensor) -> Tensor: if self.n_emb != 0: x = [e(x_cat[:,i]) for i,e in enumerate(self.embeds)] x = torch.cat(x, 1) x = self.emb_drop(x) if self.n_cont != 0: x_cont = self.bn_cont(x_cont) x = torch.cat([x, x_cont], 1) if self.n_emb != 0 else x_cont x = self.layers(x) if self.y_range is not None: x = (self.y_range[1]-self.y_range[0]) * torch.sigmoid(x) + self.y_range[0] return x PKNB!|//(mlvajra/preprocessing/tsPreprocessing.py""" shamelessly copied from Fastai repository with few changes Cleaning and feature engineering functions for structured data """ import re from functools import partial from pandas.api.types import is_numeric_dtype from datetime import date, datetime import calendar import pandas as pd from pandas import DataFrame from typing import * from enum import IntEnum import numpy as np from datetime import datetime from sklearn.impute import MissingIndicator def ifnone(a:Any,b:Any)->Any: "`a` if `a` is not None, otherwise `b`." return b if a is None else a def listify(p=None, q=None): "Make `p` listy and the same length as `q`." if p is None: p=[] elif isinstance(p, str): p = [p] elif not isinstance(p, Iterable): p = [p] #Rank 0 tensors in PyTorch are Iterable but don't have a length. else: try: a = len(p) except: p = [p] n = q if type(q)==int else len(p) if q is None else len(q) if len(p)==1: p = p * n assert len(p)==n, f'List len mismatch ({len(p)} vs {n})' return list(p) __all__ = ['add_datepart', 'cont_cat_split', 'Categorify', 'FillMissing', 'FillStrategy', 'Normalize', 'TabularProc', 'add_elapsed_times', 'make_date', 'add_cyclic_datepart'] def make_date(df:DataFrame, date_field:str): "Make sure `df[field_name]` is of the right date type." field_dtype = df[date_field].dtype if isinstance(field_dtype, pd.core.dtypes.dtypes.DatetimeTZDtype): field_dtype = np.datetime64 if not np.issubdtype(field_dtype, np.datetime64): df[date_field] = pd.to_datetime(df[date_field], infer_datetime_format=True) def cyclic_dt_feat_names(time:bool=True, add_linear:bool=False)->List[str]: "Return feature names of date/time cycles as produced by `cyclic_dt_features`." fs = ['cos','sin'] attr = [f'{r}_{f}' for r in 'weekday day_month month_year day_year'.split() for f in fs] if time: attr += [f'{r}_{f}' for r in 'hour clock min sec'.split() for f in fs] if add_linear: attr.append('year_lin') return attr def cyclic_dt_features(d:Union[date,datetime], time:bool=True, add_linear:bool=False)->List[float]: "Calculate the cos and sin of date/time cycles." tt,fs = d.timetuple(), [np.cos, np.sin] day_year,days_month = tt.tm_yday, calendar.monthrange(d.year, d.month)[1] days_year = 366 if calendar.isleap(d.year) else 365 rs = d.weekday()/7, (d.day-1)/days_month, (d.month-1)/12, (day_year-1)/days_year feats = [f(r * 2 * np.pi) for r in rs for f in fs] if time and isinstance(d, datetime) and type(d) != date: rs = tt.tm_hour/24, tt.tm_hour%12/12, tt.tm_min/60, tt.tm_sec/60 feats += [f(r * 2 * np.pi) for r in rs for f in fs] if add_linear: if type(d) == date: feats.append(d.year + rs[-1]) else: secs_in_year = (datetime(d.year+1, 1, 1) - datetime(d.year, 1, 1)).total_seconds() feats.append(d.year + ((d - datetime(d.year, 1, 1)).total_seconds() / secs_in_year)) return feats def add_cyclic_datepart(df:DataFrame, field_name:str, prefix:str=None, drop:bool=True, time:bool=False, add_linear:bool=False): "Helper function that adds trigonometric date/time features to a date in the column `field_name` of `df`." make_date(df, field_name) field = df[field_name] prefix = ifnone(prefix, re.sub('[Dd]ate$', '', field_name)) series = field.apply(partial(cyclic_dt_features, time=time, add_linear=add_linear)) columns = [prefix + c for c in cyclic_dt_feat_names(time, add_linear)] df_feats = pd.DataFrame([item for item in series], columns=columns, index=series.index) df = pd.concat([df, df_feats], axis=1) if drop: df.drop(field_name, axis=1, inplace=True) return df def add_datepart(df:DataFrame, field_name:str, prefix:str=None, drop:bool=True, time:bool=False): "Helper function that adds columns relevant to a date in the column `field_name` of `df`." make_date(df, field_name) field = df[field_name] prefix = ifnone(prefix, re.sub('[Dd]ate$', '', field_name)) attr = ['Year', 'Month', 'Week', 'Day', 'Dayofweek', 'Dayofyear', 'Is_month_end', 'Is_month_start', 'Is_quarter_end', 'Is_quarter_start', 'Is_year_end', 'Is_year_start'] if time: attr = attr + ['Hour', 'Minute', 'Second'] for n in attr: df[prefix + n] = getattr(field.dt, n.lower()) df[prefix + 'Elapsed'] = field.astype(np.int64) // 10 ** 9 if drop: df.drop(field_name, axis=1, inplace=True) return df def _get_elapsed(df:DataFrame,field_names:Collection[str], date_field:str, base_field:str, prefix:str): for f in field_names: day1 = np.timedelta64(1, 'D') last_date,last_base,res = np.datetime64(),None,[] for b,v,d in zip(df[base_field].values, df[f].values, df[date_field].values): if last_base is None or b != last_base: last_date,last_base = np.datetime64(),b if v: last_date = d res.append(((d-last_date).astype('timedelta64[D]') / day1)) df[prefix + f] = res return df def add_elapsed_times(df:DataFrame, field_names:Collection[str], date_field:str, base_field:str): field_names = listify(field_names) #Make sure date_field is a date and base_field a bool df[field_names] = df[field_names].astype('bool') make_date(df, date_field) work_df = df[field_names + [date_field, base_field]] work_df = work_df.sort_values([base_field, date_field]) work_df = _get_elapsed(work_df, field_names, date_field, base_field, 'After') work_df = work_df.sort_values([base_field, date_field], ascending=[True, False]) work_df = _get_elapsed(work_df, field_names, date_field, base_field, 'Before') for a in ['After' + f for f in field_names] + ['Before' + f for f in field_names]: work_df[a] = work_df[a].fillna(0).astype(int) for a,s in zip([True, False], ['_bw', '_fw']): work_df = work_df.set_index(date_field) tmp = (work_df[[base_field] + field_names].sort_index(ascending=a) .groupby(base_field).rolling(7, min_periods=1).sum()) tmp.drop(base_field,1,inplace=True) tmp.reset_index(inplace=True) work_df.reset_index(inplace=True) work_df = work_df.merge(tmp, 'left', [date_field, base_field], suffixes=['', s]) work_df.drop(field_names,1,inplace=True) return df.merge(work_df, 'left', [date_field, base_field]) def cont_cat_split(df, max_card=20, dep_var=None)->Tuple[List,List]: "Helper function that returns column names of cont and cat variables from given df." cont_names, cat_names = [], [] for label in df: if label == dep_var: continue if df[label].dtype == int and df[label].unique().shape[0] > max_card or df[label].dtype == float: cont_names.append(label) else: cat_names.append(label) return cont_names, cat_names class TabularProc(): "A processor for tabular dataframes." def __init__(self,cat_names,cont_names): self.cat_names:List[str]=cat_names self.cont_names:List[str]=cont_names def __call__(self, df:DataFrame, test:bool=False): "Apply the correct function to `df` depending on `test`." func = self.apply_test if test else self.apply_train func(df) def apply_train(self, df:DataFrame): "Function applied to `df` if it's the train set." raise NotImplementedError def apply_test(self, df:DataFrame): "Function applied to `df` if it's the test set." self.apply_train(df) class Categorify(TabularProc): "Transform the categorical variables to that type." def __init__(self,cat_names,cont_names): super().__init__(cat_names,cont_names) def apply_train(self, df:DataFrame): "Transform `self.cat_names` columns in categorical." self.categories = {} for n in self.cat_names: df.loc[:,n] = df.loc[:,n].astype('category').cat.as_ordered() self.categories[n] = df[n].cat.categories def apply_test(self, df:DataFrame): "Transform `self.cat_names` columns in categorical using the codes decided in `apply_train`." for n in self.cat_names: df.loc[:,n] = pd.Categorical(df[n], categories=self.categories[n], ordered=True) FillStrategy = IntEnum('FillStrategy', 'MEDIAN COMMON CONSTANT') class FillMissing(TabularProc): "Fill the missing values in continuous columns." def __init__(self,cat_names,cont_names,fill_strategy:FillStrategy=FillStrategy.MEDIAN, add_col_bool:bool=True,fill_Val:float=0): super(FillMissing, self).__init__(cat_names,cont_names) self.fill_strategy=fill_strategy self.add_col=add_col_bool self.fill_Val=fill_Val def apply_train(self, df:DataFrame): "Fill missing values in `self.cont_names` according to `self.fill_strategy`." self.na_dict = {} for name in self.cont_names: if pd.isnull(df[name]).sum(): if self.add_col: df[name+'_na'] = pd.isnull(df[name]) if name+'_na' not in self.cat_names: self.cat_names.append(name+'_na') if self.fill_strategy == FillStrategy.MEDIAN: filler = df[name].median() elif self.fill_strategy == FillStrategy.CONSTANT: filler = self.fill_val else: filler = df[name].dropna().value_counts().idxmax() df[name] = df[name].fillna(filler) self.na_dict[name] = filler def apply_test(self, df:DataFrame): "Fill missing values in `self.cont_names` like in `apply_train`." for name in self.cont_names: if name in self.na_dict: # only add columns if the columns are present while applying Training # if name+'_na' in self.cat_names: # df[name+'_na'] = pd.isnull(df[name]) if self.add_col: df[name+'_na'] = pd.isnull(df[name]) if name+'_na' not in self.cat_names: self.cat_names.append(name+'_na') df[name] = df[name].fillna(self.na_dict[name]) elif pd.isnull(df[name]).sum() != 0: raise Exception(f"""There are nan values in field {name} but there were none in the training set. Please fix those manually.""") class Normalize(TabularProc): "Normalize the continuous variables." def __init__(self,cat_names,cont_names): super().__init__(cat_names,cont_names) def apply_train(self, df:DataFrame): "Compute the means and stds of `self.cont_names` columns to normalize them." self.means,self.stds = {},{} for n in self.cont_names: assert is_numeric_dtype(df[n]), (f"""Cannot normalize '{n}' column as it isn't numerical. Are you sure it doesn't belong in the categorical set of columns?""") self.means[n],self.stds[n] = df[n].mean(),df[n].std() df[n] = (df[n]-self.means[n]) / (1e-7 + self.stds[n]) def apply_test(self, df:DataFrame): "Normalize `self.cont_names` with the same statistics as in `apply_train`." for n in self.cont_names: df[n] = (df[n]-self.means[n]) / (1e-7 + self.stds[n]) if __name__=='__main__': print("Testing") import pandas_datareader as pdr df_main=pdr.get_data_fred('GS10') df_main=df_main.reset_index() df=df_main print(df.dtypes,df.head()) make_date(df,'DATE') print(df.dtypes,df.head()) df1=add_datepart(df,field_name='DATE',drop=False) print(df1.dtypes) df2=add_cyclic_datepart(df_main.copy(),'DATE') df1['uni_index']=df1.index df3=add_elapsed_times(df1,'GS10','DATE','uni_index') print(df3.dtypes,df3.head()) df3['GS10'][100]=np.nan fm=FillMissing(['DATEIs_quarter_end'],cont_names=['GS10']) df4=fm.apply_train(df3) PKIN ~Q!mlvajra/visualization/__init__.py""" vis utils TODO: Add a visualization to add preprocessing feature Add a visualization to add evalution [Add interactive charts and dashboard utilities -superset] --Not programatically atleast for time being """ PKN"mlvajra/visualization/clfVis.pyfrom sklearn.metrics import * import matplotlib.pyplot as plt import seaborn as sns import pandas as pd import numpy as np def evalBinaryClassifier(model,x, y, labels=['Positives','Negatives']): ''' Visualize the performance of a Logistic Regression Binary Classifier. Displays a labelled Confusion Matrix, distributions of the predicted probabilities for both classes, the ROC curve, and F1 score of a fitted Binary Logistic Classifier. Author: gregcondit.com/articles/logr-charts Parameters ---------- model : fitted scikit-learn model with predict_proba & predict methods and classes_ attribute. Typically LogisticRegression or LogisticRegressionCV x : {array-like, sparse matrix}, shape (n_samples, n_features) Training vector, where n_samples is the number of samples in the data to be tested, and n_features is the number of features y : array-like, shape (n_samples,) Target vector relative to x. labels: list, optional list of text labels for the two classes, with the positive label first Displays ---------- 3 Subplots Returns ---------- F1: float ''' #model predicts probabilities of positive class p = model.predict_proba(x) if len(model.classes_)!=2: raise ValueError('A binary class problem is required') if model.classes_[1] == 1: pos_p = p[:,1] elif model.classes_[0] == 1: pos_p = p[:,0] #FIGURE plt.figure(figsize=[15,4]) #1 -- Confusion matrix cm = confusion_matrix(y,model.predict(x)) plt.subplot(131) ax = sns.heatmap(cm, annot=True, cmap='Blues', cbar=False, annot_kws={"size": 14}, fmt='g') cmlabels = ['True Negatives', 'False Positives', 'False Negatives', 'True Positives'] for i,t in enumerate(ax.texts): t.set_text(t.get_text() + "\n" + cmlabels[i]) plt.title('Confusion Matrix', size=15) plt.xlabel('Predicted Values', size=13) plt.ylabel('True Values', size=13) #2 -- Distributions of Predicted Probabilities of both classes df = pd.DataFrame({'probPos':pos_p, 'target': y}) plt.subplot(132) plt.hist(df[df.target==1].probPos, density=True, alpha=.5, color='green', label=labels[0]) plt.hist(df[df.target==0].probPos, density=True, alpha=.5, color='red', label=labels[1]) plt.axvline(.5, color='blue', linestyle='--', label='Boundary') plt.xlim([0,1]) plt.title('Distributions of Predictions', size=15) plt.xlabel('Positive Probability (predicted)', size=13) plt.ylabel('Samples (normalized scale)', size=13) plt.legend(loc="upper right") #3 -- ROC curve with annotated decision point fp_rates, tp_rates, _ = roc_curve(y,p[:,1]) roc_auc = auc(fp_rates, tp_rates) plt.subplot(133) plt.plot(fp_rates, tp_rates, color='green', lw=1, label='ROC curve (area = %0.2f)' % roc_auc) plt.plot([0, 1], [0, 1], lw=1, linestyle='--', color='grey') #plot current decision point: tn, fp, fn, tp = [i for i in cm.ravel()] plt.plot(fp/(fp+tn), tp/(tp+fn), 'bo', markersize=8, label='Decision Point') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate', size=13) plt.ylabel('True Positive Rate', size=13) plt.title('ROC Curve', size=15) plt.legend(loc="lower right") plt.subplots_adjust(wspace=.3) plt.show() #Print and Return the F1 score tn, fp, fn, tp = [i for i in cm.ravel()] precision = tp / (tp + fp) recall = tp / (tp + fn) F1 = 2*(precision * recall) / (precision + recall) printout = ( f'Precision: {round(precision,2)} | ' f'Recall: {round(recall,2)} | ' f'F1 Score: {round(F1,2)} | ' ) print(printout) return F1 if __name__=='__main__': y_pred = [0, 1, 1, 0] y_true = [0, 1, 0, 0] X=np.array([[12,13,12],[11,12,33],[12,34,5],[23,34,55]]) from sklearn.ensemble import GradientBoostingClassifier gbc=GradientBoostingClassifier() gbc.fit(X,y_true) evalBinaryClassifier(gbc,X,y_pred)PKNEo9 $ $!mlvajra-0.1.4.3.dist-info/LICENSEApache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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