def nlcl_model_train(n_clicks): c = db.get('nlcl.model_class') var = db.get('nlcl.model_variables') train = db.get('nlcl.model_train') if c is None and var is None and train is None: div = "" elif train is None or train < 0 or train > 100: div = common.error_msg('Training % should be between 0 - 100 !!') elif len(var) != 2: div = common.error_msg('Select Two Features!!') elif (not c is None) and (not var is None) and (not train is None): try: cols = [] + var cols.append(c) df = db.get('nlcl.data') df = df[cols] train_df, test_df = common.split_df(df, c, train) train_df.columns = ['X1', 'X2', 'Class'] distinct_count_df_total = get_distinct_count_df(df, c, 'Total Count') distinct_count_df_train = get_distinct_count_df(train_df, c, 'Training Count') distinct_count_df_test = get_distinct_count_df(test_df, c, 'Testing Count') distinct_count_df = distinct_count_df_total.join(distinct_count_df_train.set_index('Class'), on='Class') distinct_count_df = distinct_count_df.join(distinct_count_df_test.set_index('Class'), on='Class') model = non_separable_train(train_df) print(model) summary = {} summary['Total Training Data'] = len(train_df) summary['Total Testing Data'] = len(test_df) summary['Total Number of Features in Dataset'] = len(var) summary['Model Accuracy %'] = 'TODO' summary['Features'] = str(var) summary_df = pd.DataFrame(summary.items(), columns=['Parameters', 'Value']) db.put('nlcl.data_train', train_df) db.put('nlcl.data_test', test_df) db.put('nlcl.model_summary', summary) db.put('nlcl.model_instance', model) #confusion_df = get_confusion_matrix(test_df, c, var, instanceOfLR) except Exception as e: traceback.print_exc() return common.error_msg("Exception during training model: " + str(e)) clazz_col = c train_df.columns = cols df = train_df x_col = var[0] y_col = var[1] x1, y1 = get_rect_coordinates(model[0]) x2, y2 = get_rect_coordinates(model[1]) x3, y3 = get_rect_coordinates(model[2]) graph_data = [ go.Scatter( x=df[df[clazz_col] == clazz][x_col], y=df[df[clazz_col] == clazz][y_col], text=df[df[clazz_col] == clazz][clazz_col], mode='markers', opacity=0.8, marker={ 'size': 15, 'line': {'width': 0.5, 'color': 'white'} }, name=clazz ) for clazz in df[clazz_col].unique() ] graph_data.append(go.Scatter(x=x1, y=y1, text = 'Specific Rectangle', name = 'Specific Rectangle')) graph_data.append(go.Scatter(x=x3, y=y3, text = 'Optimal Rectangle', name = 'Optimal Rectangle')) graph_data.append(go.Scatter(x=x2, y=y2, text = 'Generic Rectangle', name = 'Generic Rectangle')) graph = dcc.Graph( id='nlcl-x-vs-y-rectangle', figure={ 'data': graph_data, 'layout': dict( title='Boundaries & Train Data Set Scatter Plot', xaxis={'title': x_col}, yaxis={'title': y_col}, margin={'l': 40, 'b': 40}, legend={'x': 0, 'y': 1}, hovermode='closest' ) } ) div = html.Div([ html.H2('Class Grouping in Data:'), dbc.Table.from_dataframe(distinct_count_df, striped=True, bordered=True, hover=True, style = common.table_style), html.H2('Model Parameters & Summary:'), dbc.Table.from_dataframe(summary_df, striped=True, bordered=True, hover=True, style = common.table_style), html.Br(), graph, #html.H2('Confusion Matrix (Precision & Recall):'), #dbc.Table.from_dataframe(confusion_df, striped=True, bordered=True, hover=True, style = common.table_style), html.H2('Prediction/Classification:'), html.P('Features to be Predicted (comma separated): ' + ','.join(var), style = {'font-size': '16px'}), dbc.Input(id="nlcl-prediction-data", placeholder=','.join(var), type="text"), html.Br(), dbc.Button("Predict", color="primary", id = 'nlcl-predict'), html.Div([], id = "nlcl-prediction"), html.Div([],id = "nlcl-predicted-scatter-plot") ]) else: div = common.error_msg('Select Proper Model Parameters!!') return div
def sgd_model_train(n_clicks): c = db.get('sgd.model_class') var = db.get('sgd.model_variables') train = db.get('sgd.model_train') #test = db.get('sgd.model_test') lr = db.get('sgd.model_lr') epoch = db.get('sgd.model_epoch') #no_of_hidden_layer = db.get('sgd.no_of_hidden_layer') no_of_neuron = db.get('sgd.no_of_neuron') no_of_neuron_h2 = db.get('sgd.no_of_neuron_h2') layer = 1 if not no_of_neuron_h2 is None: layer = 2 db.put("sgd.model_layer", layer) if c is None and var is None and train is None and lr is None and epoch is None: div = "" elif train is None or train < 0 or train > 100: div = common.error_msg('Training % should be between 0 - 100 !!') elif (not c is None) and (not var is None) and (not train is None) and ( not lr is None) and (not epoch is None): try: cols = [] + var cols.append(c) df = db.get('sgd.data') df = df[cols] ## Make DataFrame compatible for SGD API ## df, quantized_classes, reverse_quantized_classes = quantized_class( df, c) train_df, test_df = common.split_df(df, c, train) distinct_count_df_total = get_distinct_count_df( df, c, 'Total Count') distinct_count_df_train = get_distinct_count_df( train_df, c, 'Training Count') distinct_count_df_test = get_distinct_count_df( test_df, c, 'Testing Count') distinct_count_df = distinct_count_df_total.join( distinct_count_df_train.set_index('Class'), on='Class') distinct_count_df = distinct_count_df.join( distinct_count_df_test.set_index('Class'), on='Class') distinct_count_df['Class'] = distinct_count_df['Class'].map( reverse_quantized_classes) if layer == 1: ycap, loss_dict, cc_percentage, wc_percentage, model, yu = ann_training( train_df[var], train_df[c], no_of_neuron, lr, epoch) ycap, cc_percentage, wc_percentage = ann_testing( test_df[var], test_df[c], model, yu) elif layer == 2: ycap, loss_dict, cc_percentage, wc_percentage, model, yu = ann_training_h2( train_df[var], train_df[c], no_of_neuron, no_of_neuron_h2, lr, epoch) ycap, cc_percentage, wc_percentage = ann_testing_h2( train_df[var], train_df[c], model, yu) summary = {} summary['Total Training Data'] = len(train_df) summary['Total Testing Data'] = len(test_df) summary['Total Number of Features in Dataset'] = len(var) summary['Total no of Layers'] = layer + 2 summary['No of Hidden Layer'] = layer summary['No of Neuron in Hidden Layer 1'] = no_of_neuron summary['No of Neuron in Hidden Layer 2'] = no_of_neuron_h2 summary['Activation Function'] = 'Sigmoid' summary['Learning rate'] = lr summary['Epochs'] = epoch summary['Model Accuracy'] = round(cc_percentage, 2) summary['Features'] = str(var) summary_df = pd.DataFrame(summary.items(), columns=['Parameters', 'Value']) db.put('sgd.data_train', train_df) db.put('sgd.data_test', test_df) db.put('sgd.quantized_classes', quantized_classes) db.put('sgd.reverse_quantized_classes', reverse_quantized_classes) db.put('sgd.model', model) db.put('sgd.model_yu', yu) db.put('sgd.summary', summary) confusion_df = get_confusion_matrix(test_df, c, var, model, yu, reverse_quantized_classes) except Exception as e: traceback.print_exc() return common.error_msg("Exception during training model: " + str(e)) trace = go.Scatter(x=loss_dict['Epoch_no'], y=loss_dict['Loss'], line=dict(width=2, color='rgb(106, 181, 135)')) convergence_title = go.Layout(title='Convergence Plot', hovermode='closest', xaxis={'title': 'Epoch'}, yaxis={'title': 'Loss Function'}) convergence_fig = go.Figure(data=[trace], layout=convergence_title) div = html.Div([ html.H2('Class Grouping in Data:'), dbc.Table.from_dataframe(distinct_count_df, striped=True, bordered=True, hover=True, style=common.table_style), html.H2('Model Parameters & Summary:'), dbc.Table.from_dataframe(summary_df, striped=True, bordered=True, hover=True, style=common.table_style), html.Br(), dcc.Graph(id='sgd-convergence-plot', figure=convergence_fig), html.H2('Confusion Matrix (Precision & Recall):'), dbc.Table.from_dataframe(confusion_df, striped=True, bordered=True, hover=True, style=common.table_style), html.Br(), html.Br() ]) else: div = common.error_msg('Select Proper Model Parameters!!') return div
def knn_model_train(n_clicks): c = db.get('knn.model_class') var = db.get('knn.model_variables') train = db.get('knn.model_train') k = db.get('knn.distance') file = db.get("knn.file") if c is None and var is None and train is None and k is None: div = "" elif train is None or train < 0 or train > 100: div = common.error_msg('Training % should be between 0 - 100 !!') elif (not c is None) and (not var is None) and (not train is None) and (not k is None): try: cols = [] + var cols.append(c) df = db.get('knn.data') df = df[cols] train_df, test_df = common.split_df(df, c, train) distinct_count_df_total = get_distinct_count_df(df, c, 'Total Count') distinct_count_df_train = get_distinct_count_df(train_df, c, 'Training Count') distinct_count_df_test = get_distinct_count_df(test_df, c, 'Testing Count') distinct_count_df = distinct_count_df_total.join(distinct_count_df_train.set_index('Class'), on='Class') distinct_count_df = distinct_count_df.join(distinct_count_df_test.set_index('Class'), on='Class') train_dataset = train_df[cols].astype(str).values.tolist() test_dataset = test_df[cols].astype(str).values.tolist() result = knn_predict(train_dataset, test_dataset, k) cc_percentage = calculate_predict_accuracy(result) summary = {} summary['Total Training Data'] = len(train_df) summary['Total Testing Data'] = len(test_df) summary['Total Number of Features in Dataset'] = len(var) summary['Model Accuracy %'] = round(cc_percentage, 2) summary['Features'] = str(var) summary_df = pd.DataFrame(summary.items(), columns=['Parameters', 'Value']) db.put('knn.data_train', train_df) db.put('knn.data_test', test_df) db.put('knn.model_summary', summary) classes = df[c].unique() confusion_df = get_confusion_matrix(result, classes) except Exception as e: traceback.print_exc() return common.error_msg("Exception during training model: " + str(e)) div = html.Div([ html.H2('Class Grouping in Data:'), dbc.Table.from_dataframe(distinct_count_df, striped=True, bordered=True, hover=True, style = common.table_style), html.H2('Model Parameters & Summary:'), dbc.Table.from_dataframe(summary_df, striped=True, bordered=True, hover=True, style = common.table_style), html.H2('Confusion Matrix (Precision & Recall):'), dbc.Table.from_dataframe(confusion_df, striped=True, bordered=True, hover=True, style = common.table_style), html.H2('Prediction/Classification:'), html.P('Features to be Predicted (comma separated): ' + ','.join(var), style = {'font-size': '16px'}), dbc.Input(id="knn-prediction-data", placeholder=','.join(var), type="text"), html.Br(), dbc.Button("Predict", color="primary", id = 'knn-predict'), html.Div([], id = "knn-prediction"), html.Div([],id = "knn-predicted-scatter-plot") ]) else: div = common.error_msg('Select Proper Model Parameters!!') return div
def cl_model_train(n_clicks): c = db.get('cl.model_class') var = db.get('cl.model_variables') train = db.get('cl.model_train') #test = db.get('cl.model_test') lr = db.get('cl.model_lr') epoch = db.get('cl.model_epoch') if c is None and var is None and train is None and lr is None and epoch is None: div = "" elif train is None or train < 0 or train > 100: div = common.error_msg('Training % should be between 0 - 100 !!') elif (not c is None) and (not var is None) and (not train is None) and ( not lr is None) and (not epoch is None): #parameters = "Training Data = " + str(train) + " % Testing Data = " + str(100 - train) + " % Learning rate = " + str(lr) + " Epoch = " + str(epoch) try: cols = [] + var cols.append(c) df = db.get('cl.data') df = df[cols] train_df, test_df = common.split_df(df, c, train) distinct_count_df_total = get_distinct_count_df( df, c, 'Total Count') distinct_count_df_train = get_distinct_count_df( train_df, c, 'Training Count') distinct_count_df_test = get_distinct_count_df( test_df, c, 'Testing Count') distinct_count_df = distinct_count_df_total.join( distinct_count_df_train.set_index('Class'), on='Class') distinct_count_df = distinct_count_df.join( distinct_count_df_test.set_index('Class'), on='Class') instanceOfLR, summary = linearClassifier(train_df, test_df, len(var), lr, epoch) summary['Features'] = str(var) summary_df = pd.DataFrame(summary.items(), columns=['Parameters', 'Value']) db.put('cl.data_train', train_df) db.put('cl.data_test', test_df) db.put('cl.model_summary', summary) db.put('cl.model_instance', instanceOfLR) confusion_df = get_confusion_matrix(test_df, c, var, instanceOfLR) except Exception as e: traceback.print_exc() return common.error_msg("Exception during training model: " + str(e)) div = html.Div([ html.H2('Class Grouping in Data:'), dbc.Table.from_dataframe(distinct_count_df, striped=True, bordered=True, hover=True, style=common.table_style), html.H2('Model Parameters & Summary:'), dbc.Table.from_dataframe(summary_df, striped=True, bordered=True, hover=True, style=common.table_style), html.H2('Confusion Matrix (Precision & Recall):'), dbc.Table.from_dataframe(confusion_df, striped=True, bordered=True, hover=True, style=common.table_style), html.H2('Prediction/Classification:'), html.P('Features to be Predicted (comma separated): ' + ','.join(var), style={'font-size': '16px'}), dbc.Input(id="cl-prediction-data", placeholder=','.join(var), type="text"), html.Br(), dbc.Button("Predict", color="primary", id='cl-predict'), html.Div([], id="cl-prediction"), html.Div([], id="cl-predicted-scatter-plot") ]) else: div = common.error_msg('Select Proper Model Parameters!!') return div